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Andriiashen V, van Liere R, van Leeuwen T, Batenburg KJ. Quantifying the effect of X-ray scattering for data generation in real-time defect detection. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024:XST230389. [PMID: 38701129 DOI: 10.3233/xst-230389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
BACKGROUND X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust, and fast algorithms. Deep Convolutional Neural Networks (DCNNs) satisfy these requirements when a large amount of labeled data is available. To overcome the challenge of collecting these data, different methods of X-ray image generation are considered. OBJECTIVE Depending on the desired degree of similarity to real data, different physical effects should either be simulated or can be ignored. X-ray scattering is known to be computationally expensive to simulate, and this effect can greatly affect the accuracy of a generated X-ray image. We aim to quantitatively evaluate the effect of scattering on defect detection. METHODS Monte-Carlo simulation is used to generate X-ray scattering distribution. DCNNs are trained on the data with and without scattering and applied to the same test datasets. Probability of Detection (POD) curves are computed to compare their performance, characterized by the size of the smallest detectable defect. RESULTS We apply the methodology to a model problem of defect detection in cylinders. When trained on data without scattering, DCNNs reliably detect defects larger than 1.3 mm, and using data with scattering improves performance by less than 5%. If the analysis is performed on the cases with large scattering-to-primary ratio (1 < SPR < 5), the difference in performance could reach 15% (approx. 0.4 mm). CONCLUSION Excluding the scattering signal from the training data has the largest effect on the smallest detectable defects, and the difference decreases for larger defects. The scattering-to-primary ratio has a significant effect on detection performance and the required accuracy of data generation.
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Affiliation(s)
| | - Robert van Liere
- Computational Imaging, Centrum Wiskunde en Informatica, Amsterdam, The Netherlands
- Faculteit Wiskunde en Informatica, Technical University Eindhoven, Eindhoven, The Netherlands
| | - Tristan van Leeuwen
- Computational Imaging, Centrum Wiskunde en Informatica, Amsterdam, The Netherlands
- Mathematical Institute, Utrecht University, Utrecht, The Netherlands
| | - Kees Joost Batenburg
- Computational Imaging, Centrum Wiskunde en Informatica, Amsterdam, The Netherlands
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
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2
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Niu J, Miao B, Guo J, Ding Z, He Y, Chi Z, Wang F, Ma X. Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness. MATERIALS (BASEL, SWITZERLAND) 2023; 17:148. [PMID: 38204003 PMCID: PMC10780037 DOI: 10.3390/ma17010148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/22/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024]
Abstract
This research presents a comprehensive analysis of deep neural network models (DNNs) for the precise prediction of Vickers hardness (HV) in nitrided and carburized M50NiL steel samples, with hardness values spanning from 400 to 1000 HV. By conducting rigorous experimentation and obtaining corresponding nanoindentation data, we evaluated the performance of four distinct neural network architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and Transformer. Our findings reveal that MLP and LSTM models excel in predictive accuracy and efficiency, with MLP showing exceptional iteration efficiency and predictive precision. The study validates models for broad application in various steel types and confirms nanoindentation as an effective direct measure for HV hardness in thin films and gradient-variable regions. This work contributes a validated and versatile approach to the hardness assessment of thin-film materials and those with intricate microstructures, enhancing material characterization and potential application in advanced material engineering.
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Affiliation(s)
- Junbo Niu
- School of Material Science & Engineering, Harbin Institute of Technology, Harbin 150001, China; (B.M.); (J.G.); (Z.D.); (Y.H.); (Z.C.); (F.W.)
| | - Bin Miao
- School of Material Science & Engineering, Harbin Institute of Technology, Harbin 150001, China; (B.M.); (J.G.); (Z.D.); (Y.H.); (Z.C.); (F.W.)
| | - Jiaxu Guo
- School of Material Science & Engineering, Harbin Institute of Technology, Harbin 150001, China; (B.M.); (J.G.); (Z.D.); (Y.H.); (Z.C.); (F.W.)
| | - Zhifeng Ding
- School of Material Science & Engineering, Harbin Institute of Technology, Harbin 150001, China; (B.M.); (J.G.); (Z.D.); (Y.H.); (Z.C.); (F.W.)
| | - Yin He
- School of Material Science & Engineering, Harbin Institute of Technology, Harbin 150001, China; (B.M.); (J.G.); (Z.D.); (Y.H.); (Z.C.); (F.W.)
| | - Zhiyu Chi
- School of Material Science & Engineering, Harbin Institute of Technology, Harbin 150001, China; (B.M.); (J.G.); (Z.D.); (Y.H.); (Z.C.); (F.W.)
| | - Feilong Wang
- School of Material Science & Engineering, Harbin Institute of Technology, Harbin 150001, China; (B.M.); (J.G.); (Z.D.); (Y.H.); (Z.C.); (F.W.)
| | - Xinxin Ma
- School of Material Science & Engineering, Harbin Institute of Technology, Harbin 150001, China; (B.M.); (J.G.); (Z.D.); (Y.H.); (Z.C.); (F.W.)
- State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001, China
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3
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Jeong SH, Lee SH, Won HI. Development of microcatheter tube extrusion angle estimation system using convolutional neural network segmentation. Sci Rep 2023; 13:18468. [PMID: 37891249 PMCID: PMC10611707 DOI: 10.1038/s41598-023-45759-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 10/23/2023] [Indexed: 10/29/2023] Open
Abstract
This study presents a deep learning-based monitoring system for estimating extrusion angles in the manufacturing process of microcatheter tubes. Given the critical nature of these tubes, which are directly inserted into the human body, strict quality control is imperative. To mitigate potential quality variations stemming from operator actions, a system utilizing a convolutional neural network to precisely measure the extrusion angle-a parameter with profound implications for tube quality-is developed. Until now, there has been no method to estimate the extrusion angle of resin being extruded in real-time. In this study, for the first time, a method using deep learning to estimate the angle was proposed. This innovative system comprises two RGB cameras capturing both front and side perspectives. The acquired images undergo segmentation via a meticulously trained convolutional neural network. Subsequently, the extrusion angle is accurately estimated through the application of principal component analysis on the segmented image. The usefulness of the proposed system was rigorously confirmed through comprehensive validation measures, including mean intersection over union (mIoU), mean absolute angle error (MAE), and inference time, using a real-world dataset. The attained metrics, with an mIoU of 0.8848, MAE of 0.5968, and an inference time of 0.0546, unequivocally affirm the system's suitability for enhancing the catheter tube extrusion process.
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Affiliation(s)
- Seung Hyun Jeong
- School of Mechatronics Engineering, Korea University of Technology and Education, Cheonan-si, 31253, Republic of Korea
| | - Sang Heon Lee
- Manufacturing AI Research Center, Korea Institute of Industrial Technology, Cheonan-si, 31056, Republic of Korea
| | - Hong-In Won
- Manufacturing AI Research Center, Korea Institute of Industrial Technology, Cheonan-si, 31056, Republic of Korea.
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4
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Silenzi A, Castorani V, Tomassini S, Falcionelli N, Contardo P, Bonci A, Dragoni AF, Sernani P. Quality Control of Carbon Look Components via Surface Defect Classification with Deep Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:7607. [PMID: 37688059 PMCID: PMC10490784 DOI: 10.3390/s23177607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/11/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023]
Abstract
Many "Industry 4.0" applications rely on data-driven methodologies such as Machine Learning and Deep Learning to enable automatic tasks and implement smart factories. Among these applications, the automatic quality control of manufacturing materials is of utmost importance to achieve precision and standardization in production. In this regard, most of the related literature focused on combining Deep Learning with Nondestructive Testing techniques, such as Infrared Thermography, requiring dedicated settings to detect and classify defects in composite materials. Instead, the research described in this paper aims at understanding whether deep neural networks and transfer learning can be applied to plain images to classify surface defects in carbon look components made with Carbon Fiber Reinforced Polymers used in the automotive sector. To this end, we collected a database of images from a real case study, with 400 images to test binary classification (defect vs. no defect) and 1500 for the multiclass classification (components with no defect vs. recoverable vs. non-recoverable). We developed and tested ten deep neural networks as classifiers, comparing ten different pre-trained CNNs as feature extractors. Specifically, we evaluated VGG16, VGG19, ResNet50 version 2, ResNet101 version 2, ResNet152 version 2, Inception version 3, MobileNet version 2, NASNetMobile, DenseNet121, and Xception, all pre-trainined with ImageNet, combined with fully connected layers to act as classifiers. The best classifier, i.e., the network based on DenseNet121, achieved a 97% accuracy in classifying components with no defects, recoverable components, and non-recoverable components, demonstrating the viability of the proposed methodology to classify surface defects from images taken with a smartphone in varying conditions, without the need for dedicated settings. The collected images and the source code of the experiments are available in two public, open-access repositories, making the presented research fully reproducible.
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Affiliation(s)
- Andrea Silenzi
- Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; (A.S.); (S.T.); (N.F.); (P.C.); (A.B.); (A.F.D.)
| | - Vincenzo Castorani
- HP Composites S.p.A., Via del Lampo S.N., Z.Ind.le Campolungo, 63100 Ascoli Piceno, Italy;
| | - Selene Tomassini
- Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; (A.S.); (S.T.); (N.F.); (P.C.); (A.B.); (A.F.D.)
| | - Nicola Falcionelli
- Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; (A.S.); (S.T.); (N.F.); (P.C.); (A.B.); (A.F.D.)
| | - Paolo Contardo
- Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; (A.S.); (S.T.); (N.F.); (P.C.); (A.B.); (A.F.D.)
| | - Andrea Bonci
- Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; (A.S.); (S.T.); (N.F.); (P.C.); (A.B.); (A.F.D.)
| | - Aldo Franco Dragoni
- Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; (A.S.); (S.T.); (N.F.); (P.C.); (A.B.); (A.F.D.)
| | - Paolo Sernani
- Department of Law, University of Macerata, Piaggia dell’Università 2, 62100 Macerata, Italy
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5
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Li R, Chen P, Huang J, Fu K. Research on Internal Shape Anomaly Inspection Technology for Pipeline Girth Welds Based on Alternating Excitation Detection. SENSORS (BASEL, SWITZERLAND) 2023; 23:7519. [PMID: 37687973 PMCID: PMC10490758 DOI: 10.3390/s23177519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/23/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
Abnormal formation of girth weld is a major threat to the safe operation of pipelines, which may lead to serious accidents. Therefore, regular inspection and maintenance of girth weld are essential for accident prevention and energy security. This paper presents a novel method for inspecting abnormal girth weld formation in oil and gas pipelines using alternating excitation detection technology. The method is based on the analysis of the microscopic magnetic variations in the welded area under alternating magnetic fields. An internal inspection probe and electronic system for detecting abnormal girth weld formation were designed and developed. The system's capability to identify misalignment, undercutting, root concavity, and abnormal formation height of girth weld was tested by numerical simulation and experimental study. The results show that the detection system can effectively identify a minimum misalignment of 0.5 mm at a lift-off height of 15 mm. The proposed method offers several advantages, such as rapid response, low cost, non-contact operation, and high sensitivity to surface flaws in ferromagnetic pipelines.
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Affiliation(s)
- Rui Li
- Pipechina Research Institute, Langfang 065000, China; (P.C.); (K.F.)
| | - Pengchao Chen
- Pipechina Research Institute, Langfang 065000, China; (P.C.); (K.F.)
| | - Jie Huang
- College of Mechanical and Transportation, China University of Petroleum, Beijing 102249, China;
| | - Kuan Fu
- Pipechina Research Institute, Langfang 065000, China; (P.C.); (K.F.)
- College of Mechanical and Transportation, China University of Petroleum, Beijing 102249, China;
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6
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Karapalidou E, Alexandris N, Antoniou E, Vologiannidis S, Kalomiros J, Varsamis D. Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units. SENSORS (BASEL, SWITZERLAND) 2023; 23:6502. [PMID: 37514798 PMCID: PMC10384423 DOI: 10.3390/s23146502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023]
Abstract
The advent of Industry 4.0 introduced new ways for businesses to evolve by implementing maintenance policies leading to advancements in terms of productivity, efficiency, and financial performance. In line with the growing emphasis on sustainability, industries implement predictive techniques based on Artificial Intelligence for the purpose of mitigating machine and equipment failures by predicting anomalies during their production process. In this work, a new dataset that was made publicly available, collected from an industrial blower, is presented, analyzed and modeled using a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder. Specifically the right and left mounted ball bearing units were measured during several months of normal operational condition as well as during an encumbered operational state. An anomaly detection model was developed for the purpose of analyzing the operational behavior of the two bearing units. A stacked sparse Long Short-Term Memory Autoencoder was successfully trained on the data obtained from the left unit under normal operating conditions, learning the underlying patterns and statistical connections of the data. The model was evaluated by means of the Mean Squared Error using data from the unit's encumbered state, as well as using data collected from the right unit. The model performed satisfactorily throughout its evaluation on all collected datasets. Also, the model proved its capability for generalization along with adaptability on assessing the behavior of equipment similar to the one it was trained on.
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Affiliation(s)
- Elisavet Karapalidou
- Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
| | - Nikolaos Alexandris
- Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
| | - Efstathios Antoniou
- Department of Informatics and Electronics Engineering, International Hellenic University, 57400 Thessaloniki, Greece
| | - Stavros Vologiannidis
- Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
| | - John Kalomiros
- Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
| | - Dimitrios Varsamis
- Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
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7
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Xu Z, Wang A, Hou F, Zhao G. Defect detection of gear parts in virtual manufacturing. Vis Comput Ind Biomed Art 2023; 6:6. [PMID: 36988838 DOI: 10.1186/s42492-023-00133-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
AbstractGears play an important role in virtual manufacturing systems for digital twins; however, the image of gear tooth defects is difficult to acquire owing to its non-convex shape. In this study, a deep learning network is proposed to detect gear defects based on their point cloud representation. This approach mainly consists of three steps: (1) Various types of gear defects are classified into four cases (fracture, pitting, glue, and wear); A 3D gear dataset was constructed with 10000 instances following the aforementioned classification. (2) Gear-PCNet+ + introduces a novel Combinational Convolution Block, proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology; (3) Compared with other methods, experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.
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8
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An efficient lightweight convolutional neural network for industrial surface defect detection. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10438-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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9
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Sundaram S, Zeid A. Artificial Intelligence-Based Smart Quality Inspection for Manufacturing. MICROMACHINES 2023; 14:570. [PMID: 36984977 PMCID: PMC10058274 DOI: 10.3390/mi14030570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/18/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
In today's era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products.
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10
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Burdzik R. Impact and Assessment of Suspension Stiffness on Vibration Propagation into Vehicle. SENSORS (BASEL, SWITZERLAND) 2023; 23:1930. [PMID: 36850528 PMCID: PMC9965621 DOI: 10.3390/s23041930] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
The impact of transport-induced vibrations on people is a particularly important problem. Sudden or intensifying vibration phenomena of a local nature may compromise safety, especially in transport. The paper addresses the results of research on the impact of spring stiffness parameters on the propagation of vibrations in the vehicle structure using simple amplitude and frequency measures. The use of the developed method of selective multi-criteria analysis of frequency bands made it possible to compare the vibrations recorded in the vehicle with a new or worn coil spring. The results of the present study allow the development of a large data base in which all signals are classified by the exploitation parameters and location of the propagation of vibration in the vehicle. The most important findings and achievements of the presented study are the testing of actual suspension components with real damage under controlled conditions, the identification of the vibration propagation path from the wheel to the driver and passenger feet, the quantitative comparison of vibrations affecting humans in the vehicle (through the feet), and the frequency decomposition of vibration for selected bands. These findings improve the proper interpretation of the developed measures and, as a result, the difficulties in using this knowledge at the engineering level, for example, in the design and construction improvement stage. Therefore, innovation points and engineering significances are a method of selective multi-criteria analysis of frequency bands and have potential applications in diagnostics and the design of suspension systems and in terms of passengers' comfort.
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Affiliation(s)
- Rafał Burdzik
- Department of Road Transport, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
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11
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An optimized deep learning approach to detect and classify defective tiles in production line for efficient industrial quality control. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08283-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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12
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Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia. Brain Sci 2023; 13:brainsci13010147. [PMID: 36672129 PMCID: PMC9856834 DOI: 10.3390/brainsci13010147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 01/17/2023] Open
Abstract
Customer satisfaction and loyalty are essential for every business. Feedback prediction and social media classification are crucial and play a key role in accurately identifying customer satisfaction. This paper presents sentiment analysis-based customer feedback prediction based on Twitter Arabic datasets of telecommunications companies in Saudi Arabia. The human brain, which contains billions of neurons, provides feedback based on the current and past experience provided by the services and other related stakeholders. Artificial Intelligent (AI) based methods, parallel to human brain processing methods such as Deep Learning (DL) algorithms, are famous for classifying and analyzing such datasets. Comparing the Arabic Dataset to English, it is pretty challenging for typical methods to outperform in the classification or prediction tasks. Therefore, the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model was used and analyzed with various parameters such as activation functions and topologies and simulated customer satisfaction prediction takes using Arabic Twitter datasets. The prediction results were compared with two famous DL algorithms: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Results show that these methods have been successfully applied and obtained highly accurate classification results. AraBERT achieved the best prediction accuracy among the three ML methods, especially with Mobily and STC datasets.
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13
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Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images. Soft comput 2023; 27:3307-3326. [PMID: 33994846 PMCID: PMC8107782 DOI: 10.1007/s00500-021-05839-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2021] [Indexed: 11/05/2022]
Abstract
The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s.
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14
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Thomson EE, Harfouche M, Kim K, Konda PC, Seitz CW, Cooke C, Xu S, Jacobs WS, Blazing R, Chen Y, Sharma S, Dunn TW, Park J, Horstmeyer RW, Naumann EA. Gigapixel imaging with a novel multi-camera array microscope. eLife 2022; 11:e74988. [PMID: 36515989 PMCID: PMC9917455 DOI: 10.7554/elife.74988] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/23/2022] [Indexed: 12/15/2022] Open
Abstract
The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. Further, the MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms across a wide range of spatial scales.
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Affiliation(s)
- Eric E Thomson
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | | | - Kanghyun Kim
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Pavan C Konda
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Catherine W Seitz
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | - Colin Cooke
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Shiqi Xu
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Whitney S Jacobs
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | - Robin Blazing
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | - Yang Chen
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
| | | | - Timothy W Dunn
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | | | - Roarke W Horstmeyer
- Ramona Optics IncDurhamUnited States
- Biomedical Engineering, Duke UniversityDurhamUnited States
| | - Eva A Naumann
- Department of Neurobiology, Duke School of MedicineDurhamUnited States
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15
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Chi Z, Jiang Z, Kamruzzaman MM, Hafshejani BA, Safarpour M. Adaptive momentum-based optimization to train deep neural network for simulating the static stability of the composite structure. ENGINEERING WITH COMPUTERS 2022; 38:4027-4049. [DOI: 10.1007/s00366-021-01335-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 02/03/2021] [Indexed: 08/29/2023]
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16
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Zheng J, Yu W, Ding Z, Kong L, Liu S, Chen Q. Real-time batch inspection system for surface defects on circular optical filters. APPLIED OPTICS 2022; 61:9634-9645. [PMID: 36606904 DOI: 10.1364/ao.474272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/11/2022] [Indexed: 06/17/2023]
Abstract
Optical filters, one of the essential parts of many optical instruments, are used to select a specific radiation band of optical devices. There are specifications for the surface quality of the optical filter in order to ensure the instrument's regular operation. The traditional machine learning techniques for examining the optical filter surface quality mentioned in the current studies primarily rely on the manual extraction of feature data, which restricts their ability to detect optical filter surfaces with multiple defects. In order to solve the problems of low detection efficiency and poor detection accuracy caused by defects too minor and too numerous types of defects, this paper proposes a real-time batch optical filter surface quality inspection method based on deep learning and image processing techniques. The first part proposes an optical filter surface defect detection and identification method for seven typical defects. A deep learning model is trained for defect detection and recognition by constructing a dataset. The second part uses image processing techniques to locate the accurate position of the defect, determine whether the defect is located within the effective aperture, and analyze the critical eigenvalue data of the defect. The experimental results show that the method improves productivity and product quality and reduces the manual workload by 90%. The proposed model and method also compare the results of surface defect detection with the actual measurement data in the field, verifying that the method has good recognition accuracy while improving efficiency.
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17
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Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2549683. [PMID: 36225540 PMCID: PMC9550437 DOI: 10.1155/2022/2549683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/28/2022] [Indexed: 11/18/2022]
Abstract
The development of industry is inseparable from the support of steel materials, and the modern industry has increasingly high requirements for the quality of steel plates. But the process of steel plate production produces many types of defects, such as roll marks, scratches, and scars. These defects will directly affect the quality and performance of the steel plate, so it is necessary to effectively detect them. Steel plate surface defects are characterized by their types, shape, and size: the same defect can have different morphologies, and similarities can exist between different defects. In this paper, industrial steel plate surface defect samples are analyzed, and a sample set is established by screening the collected defect images. Then, annotation and classification are performed. A multilayer feature extraction framework is developed in experiments to train a neural network on the sample set of defects. To address the problems of low automation, slow detection speed, and low accuracy of the traditional defect detection methods, the attention graph convolution network (AGCN) is investigated in this paper. Firstly, faster R-CNN is used as the basic network model for defect detection, and the visual features are jointly refined by combining attention mechanism and graph convolution neural network. The latter network enriches the contextual information in the visual features of steel plates and explores the semantic association between vision and defect types for different kinds of defects using the attention mechanism to achieve intelligent detection of defects, thus enabling our method to meet the practical needs of steel plate production.
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18
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Li H, Jiang W, Deng J, Yu R, Pan Q. A Sensitive Frequency Range Method Based on Laser Ultrasounds for Micro-Crack Depth Determination. SENSORS (BASEL, SWITZERLAND) 2022; 22:7221. [PMID: 36236319 PMCID: PMC9570705 DOI: 10.3390/s22197221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/16/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
The laser ultrasonic method using the characteristics of transmitted Rayleigh waves in the frequency domain to determine micro-crack depth is proposed. A low-pass filter model based on the interaction between Rayleigh waves and surface cracks is built and shows that the stop band, called the sensitive frequency range, is sensitive to the depth of surface cracks. The sum of transmission coefficients in the sensitive frequency range is defined as an evaluated parameter to determine crack depth. Moreover, the effects of the sensitive frequency range and measured distance on the evaluated results are analyzed by the finite-element method to validate the robustness of this depth-evaluating method. The estimated results of surface cracks with depths ranging from 0.08 mm to ~0.5 mm on the FEM models and aluminum-alloy samples demonstrate that the laser ultrasounds using the characteristics of Rayleigh waves in the frequency domain do work for quantitative crack depth.
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Affiliation(s)
- Haiyang Li
- Key Laboratory of Advanced Manufacturing Technology, North University of China, Taiyuan 030051, China
| | - Wenxin Jiang
- Key Laboratory of Advanced Manufacturing Technology, North University of China, Taiyuan 030051, China
| | - Jin Deng
- China Special Equipment Inspection and Research Institute, Beijing 100029, China
| | - Ruien Yu
- Key Laboratory of Advanced Manufacturing Technology, North University of China, Taiyuan 030051, China
| | - Qianghua Pan
- China Special Equipment Inspection and Research Institute, Beijing 100029, China
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19
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Klarák J, Andok R, Hricko J, Klačková I, Tsai HY. Design of the Automated Calibration Process for an Experimental Laser Inspection Stand. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22145306. [PMID: 35890987 PMCID: PMC9315713 DOI: 10.3390/s22145306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 06/01/2023]
Abstract
This paper deals with the concept of the automated calibration design for inspection systems using laser sensors. The conceptual solution is based on using a laser sensor and its ability to scan 3D surfaces of inspected objects in order to create a representative point cloud. Problems of scanning are briefly discussed. The automated calibration procedure for solving problems of errors due to non-precise adjustment of the mechanical arrangement, possible tolerances in assembly, and their following elimination is proposed. The main goal is to develop a system able to measure and quantify the quality of produced objects in the environment of Industry 4.0. Laboratory measurements on the experimental stand, including the principal software solution for automated calibration of laser sensors suitable for gear wheel inspection systems are presented. There is described design of compensation eccentricity by Fourier transform and sinusoidal fitting to identify and suppress the first harmonic component in the data with high precision measuring.
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Affiliation(s)
- Jaromír Klarák
- Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia; (R.A.); (J.H.)
| | - Robert Andok
- Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia; (R.A.); (J.H.)
| | - Jaroslav Hricko
- Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia; (R.A.); (J.H.)
| | - Ivana Klačková
- Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia;
| | - Hung-Yin Tsai
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan;
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20
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Peng Y, Braun B, McAlpin C, Broadway M, Colegrove B, Chiang L. Contamination classification for pellet quality inspection using deep learning. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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21
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A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115608] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The presented research addresses the real-time object detection problem with small and moving objects, specifically the surface-mount component on a conveyor. Detecting and counting small moving objects on the assembly line is a challenge. In order to meet the requirements of real-time applications, state-of-the-art electronic component detection and classification algorithms are implemented into powerful hardware systems. This work proposes a low-cost system with an embedded microcomputer to detect surface-mount components on a conveyor belt in real time. The system detects moving, packed, and unpacked surface-mount components. The system’s performance was experimentally investigated by implementing several object-detection algorithms. The system’s performance with different algorithm implementations was compared using mean average precision and inference time. The results of four different surface-mount components showed average precision scores of 97.3% and 97.7% for capacitor and resistor detection. The findings suggest that the system with the implemented YOLOv4-tiny algorithm on the Jetson Nano 4 GB microcomputer achieves a mean average precision score of 88.03% with an inference time of 56.4 ms and 87.98% mean average precision with 11.2 ms inference time on the Tesla P100 16 GB platform.
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22
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Defect Detection for Metal Base of TO-Can Packaged Laser Diode Based on Improved YOLO Algorithm. ELECTRONICS 2022. [DOI: 10.3390/electronics11101561] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Defect detection is an important part of the manufacturing process of mechanical products. In order to detect the appearance defects quickly and accurately, a method of defect detection for the metal base of TO-can packaged laser diode (metal TO-base) based on the improved You Only Look Once (YOLO) algorithm named YOLO-SO is proposed in this study. Firstly, convolutional block attention mechanism (CBAM) module was added to the convolutional layer of the backbone network. Then, a random-paste-mosaic (RPM) small object data augmentation module was proposed on the basis of Mosaic algorithm in YOLO-V5. Finally, the K-means++ clustering algorithm was applied to reduce the sensitivity to the initial clustering center, making the positioning more accurate and reducing the network loss. The proposed YOLO-SO model was compared with other object detection algorithms such as YOLO-V3, YOLO-V4, and Faster R-CNN. Experimental results demonstrated that the YOLO-SO model reaches 84.0% mAP, 5.5% higher than the original YOLO-V5 algorithm. Moreover, the YOLO-SO model had clear advantages in terms of the smallest weight size and detection speed of 25 FPS. These advantages make the YOLO-SO model more suitable for the real-time detection of metal TO-base appearance defects.
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23
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Determination of Methanol Loss Due to Vaporization in Gas Hydrate Inhibition Process Using Intelligent Connectionist Paradigms. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-021-05679-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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24
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An Intelligent Solution for Automatic Garment Measurement Using Image Recognition Technologies. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094470] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Global digitization trends and the application of high technology in the garment market are still too slow to integrate, despite the increasing demand for automated solutions. The main challenge is related to the extraction of garment information-general clothing descriptions and automatic dimensional extraction. In this paper, we propose the garment measurement solution based on image processing technologies, which is divided into two phases, garment segmentation and key points extraction. UNet as a backbone network has been used for mask retrieval. Separate algorithms have been developed to identify both general and specific garment key points from which the dimensions of the garment can be calculated by determining the distances between them. Using this approach, we have resulted in an average 1.27 cm measurement error for the prediction of the basic measurements of blazers, 0.747 cm for dresses and 1.012 cm for skirts.
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25
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Popov VV, Kudryavtseva EV, Kumar Katiyar N, Shishkin A, Stepanov SI, Goel S. Industry 4.0 and Digitalisation in Healthcare. MATERIALS 2022; 15:ma15062140. [PMID: 35329592 PMCID: PMC8953130 DOI: 10.3390/ma15062140] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/03/2022] [Accepted: 03/10/2022] [Indexed: 02/04/2023]
Abstract
Industry 4.0 in healthcare involves use of a wide range of modern technologies including digitisation, artificial intelligence, user response data (ergonomics), human psychology, the Internet of Things, machine learning, big data mining, and augmented reality to name a few. The healthcare industry is undergoing a paradigm shift thanks to Industry 4.0, which provides better user comfort through proactive intervention in early detection and treatment of various diseases. The sector is now ready to make its next move towards Industry 5.0, but certain aspects that motivated this review paper need further consideration. As a fruitful outcome of this review, we surveyed modern trends in this arena of research and summarised the intricacies of new features to guide and prepare the sector for an Industry 5.0-ready healthcare system.
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Affiliation(s)
- Vladimir V. Popov
- Department of Materials Science and Engineering, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
- Higher School of Engineering, Ural Federal University, 620002 Ekaterinburg, Russia;
- Correspondence:
| | - Elena V. Kudryavtseva
- Obstetrics and Gynecology Department, Ural State Medical University, 620000 Ekaterinburg, Russia;
| | - Nirmal Kumar Katiyar
- School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK; (N.K.K.); (S.G.)
| | - Andrei Shishkin
- Rudolfs Cimdins Riga Biomaterials Innovations and Development Centre of RTU, Institute of General Chemical Engineering, Faculty of Materials Science and Applied Chemistry, Riga Technical University, 1007 Riga, Latvia;
| | - Stepan I. Stepanov
- Higher School of Engineering, Ural Federal University, 620002 Ekaterinburg, Russia;
| | - Saurav Goel
- School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK; (N.K.K.); (S.G.)
- Department of Mechanical Engineering, University of Petroleum and Energy Studies, Dehradun 248007, India
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26
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Concatenated Network Fusion Algorithm (CNFA) Based on Deep Learning: Improving the Detection Accuracy of Surface Defects for Ceramic Tile. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The low accuracy of detection algorithms is one impediment in detecting ceramic tile’s surface defects online utilizing intelligent detection instead of human inspection. The purpose of this paper is to present a CNFA for resolving the obstacle. Firstly, a negative sample set is generated online by non-defective images of ceramic tiles, and a comparator based on a modified VGG16 extracts a reference image from it. Disguised rectangle boxes, including defective and non-defective, are acquired from the image to be inspected by a detector. A reference rectangle box most similar to the disguised rectangle box is extracted from the reference image. A discriminator is constituted with a modified MobileNetV3 network serving as the backbone and a metric learning loss function strengthening feature recognition, distinguishing the true and false of disguised and reference rectangle boxes. Results exhibit that the discriminator appears to have an accuracy of 98.02%, 13% more than other algorithms. Furthermore, the CNFA performs an average accuracy of 98.19%, and the consumption time of a single image extends by only 64.35 ms, which has little influence on production efficiency. It provides a theoretical and practical reference for surface defect detection of products with complex and changeable textures in industrial environments.
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27
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Recognition of Human Face Regions under Adverse Conditions—Face Masks and Glasses—In Thermographic Sanitary Barriers through Learning Transfer from an Object Detector. MACHINES 2022. [DOI: 10.3390/machines10010043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The COVID-19 pandemic has detrimentally affected people’s lives and the economies of many countries, causing disruption in the health, education, transport, and other sectors. Several countries have implemented sanitary barriers at airports, bus and train stations, company gates, and other shared spaces to detect patients with viral symptoms in an effort to contain the spread of the disease. As fever is one of the most recurrent disease symptoms, the demand for devices that measure skin (body surface) temperature has increased. The thermal imaging camera, also known as a thermal imager, is one such device used to measure temperature. It employs a technology known as infrared thermography and is a noninvasive, fast, and objective tool. This study employed machine learning transfer using You Only Look Once (YOLO) to detect the hottest temperatures in the regions of interest (ROIs) of the human face in thermographic images, allowing the identification of a febrile state in humans. The algorithms detect areas of interest in the thermographic images, such as the eyes, forehead, and ears, before analyzing the temperatures in these regions. The developed software achieved excellent performance in detecting the established areas of interest, adequately indicating the maximum temperature within each region of interest, and correctly choosing the maximum temperature among them.
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28
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Rajaei H, Esmaeilzadeh F, Mowla D. Synthesis and Characterization of Nano-Sized Pt/HZSM–5 Catalyst for Application in the Xylene Isomerization Process. Catal Letters 2022. [DOI: 10.1007/s10562-021-03604-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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29
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Ahmed KR. Smart Pothole Detection Using Deep Learning Based on Dilated Convolution. SENSORS (BASEL, SWITZERLAND) 2021; 21:8406. [PMID: 34960498 PMCID: PMC8704745 DOI: 10.3390/s21248406] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/13/2021] [Accepted: 12/14/2021] [Indexed: 01/03/2023]
Abstract
Roads make a huge contribution to the economy and act as a platform for transportation. Potholes in roads are one of the major concerns in transportation infrastructure. A lot of research has proposed using computer vision techniques to automate pothole detection that include a wide range of image processing and object detection algorithms. There is a need to automate the pothole detection process with adequate accuracy and speed and implement the process easily and with low setup cost. In this paper, we have developed efficient deep learning convolution neural networks (CNNs) to detect potholes in real-time with adequate accuracy. To reduce the computational cost and improve the training results, this paper proposes a modified VGG16 (MVGG16) network by removing some convolution layers and using different dilation rates. Moreover, this paper uses the MVGG16 as a backbone network for the Faster R-CNN. In addition, this work compares the performance of YOLOv5 (Large (Yl), Medium (Ym), and Small (Ys)) models with ResNet101 backbone and Faster R-CNN with ResNet50(FPN), VGG16, MobileNetV2, InceptionV3, and MVGG16 backbones. The experimental results show that the Ys model is more applicable for real-time pothole detection because of its speed. In addition, using the MVGG16 network as the backbone of the Faster R-CNN provides better mean precision and shorter inference time than using VGG16, InceptionV3, or MobilNetV2 backbones. The proposed MVGG16 succeeds in balancing the pothole detection accuracy and speed.
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Affiliation(s)
- Khaled R Ahmed
- School of Computing, Southern Illinois University, Carbondale, IL 62901, USA
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30
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Traditional Artificial Neural Networks Versus Deep Learning in Optimization of Material Aspects of 3D Printing. MATERIALS 2021; 14:ma14247625. [PMID: 34947222 PMCID: PMC8707385 DOI: 10.3390/ma14247625] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/28/2021] [Accepted: 12/09/2021] [Indexed: 12/04/2022]
Abstract
3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing.
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31
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Li H, An F, Ebrahimiasl S. Evolution the properties of C3N monolayer as anodes for lithium-ion batteries with density functional theory. Struct Chem 2021. [DOI: 10.1007/s11224-021-01799-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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32
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Yang J, Qi Q. Study on Meso-Structure Evolution in Granular Matters Based on the Contact Loop Recognition and Determination Technique. MATERIALS 2021; 14:ma14216542. [PMID: 34772068 PMCID: PMC8585250 DOI: 10.3390/ma14216542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 10/25/2021] [Accepted: 10/28/2021] [Indexed: 11/16/2022]
Abstract
On the mesoscopic scale, granular matter is tessellated into contact loops by a contact network. The stability of granular matter is highly dependent on the evolution of contact loops, including the number and area evolutions of contact loops with different geometric shapes (which can reflect the mechanical variables in the macroscale). For the features of numerous loops with complex geometry shapes in contact network images, a contact loop recognition and determination technique was developed in this study. Then, numerical biaxial compression tests were performed by the discrete element method (DEM) to investigate how the meso-structural indexes evolve along with the macro-mechanical indexes. The results show that the proposed Q-Y algorithm is effective in determining the geometric types of contact loops from contact network images. The evolution of contact loops is most active in the hardening stage, during which the number percentages of L3 (loops with three sides) and L6+ (loops with six or more sides) show opposite evolution patterns. For the area percentage, only L6+ increases while others decrease. Considering the meso-structural indexes (number percentage and area percentage of loops) are sensitive to the change of macro-mechanical indexes (deviatoric stress, axial strain, and volumetric strain) in the hardening stage. Multivariate models were established to build a bridge between the meso-structure and the macro-mechanics.
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33
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Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning. MATERIALS 2021; 14:ma14216314. [PMID: 34771841 PMCID: PMC8585172 DOI: 10.3390/ma14216314] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/05/2021] [Accepted: 10/18/2021] [Indexed: 12/29/2022]
Abstract
Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed into a decision tree model for predicting the MOE and MOR values of the wood samples. The results indicated a reduction in the mechanical properties of the samples, where fir and alder were the most and least degraded wood under weathering conditions, respectively. The mechanical degradation was correlated with the color change, where the most resistant wood to color change exhibited less reduction in the mechanical properties. The predictive machine learning model estimated the MOE and MOR values with a maximum R2 of 0.87 and 0.88, respectively. Thus, variations in the color parameters of wood can be considered informative features linked to the mechanical properties of small-sized and clear wood. Further research could study the effectiveness of the model when analyzing large-sized timber.
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34
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Song Q, Li S, Bai Q, Yang J, Zhang X, Li Z, Duan Z. Object Detection Method for Grasping Robot Based on Improved YOLOv5. MICROMACHINES 2021; 12:mi12111273. [PMID: 34832685 PMCID: PMC8625549 DOI: 10.3390/mi12111273] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/10/2021] [Accepted: 10/18/2021] [Indexed: 01/24/2023]
Abstract
In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.
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Affiliation(s)
- Qisong Song
- College of Mechanical Engineering, Guizhou University, Guiyang 550025, China; (Q.S.); (Q.B.); (J.Y.); (Z.L.)
| | - Shaobo Li
- College of Mechanical Engineering, Guizhou University, Guiyang 550025, China; (Q.S.); (Q.B.); (J.Y.); (Z.L.)
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China;
- Correspondence:
| | - Qiang Bai
- College of Mechanical Engineering, Guizhou University, Guiyang 550025, China; (Q.S.); (Q.B.); (J.Y.); (Z.L.)
| | - Jing Yang
- College of Mechanical Engineering, Guizhou University, Guiyang 550025, China; (Q.S.); (Q.B.); (J.Y.); (Z.L.)
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China;
| | - Xingxing Zhang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China;
| | - Zhiang Li
- College of Mechanical Engineering, Guizhou University, Guiyang 550025, China; (Q.S.); (Q.B.); (J.Y.); (Z.L.)
| | - Zhongjing Duan
- Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China;
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35
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Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:8025730. [PMID: 34630554 PMCID: PMC8494556 DOI: 10.1155/2021/8025730] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 09/07/2021] [Accepted: 09/11/2021] [Indexed: 11/18/2022]
Abstract
The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.
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Imbalance Modelling for Defect Detection in Ceramic Substrate by Using Convolutional Neural Network. Processes (Basel) 2021. [DOI: 10.3390/pr9091678] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The complexity of defect detection in a ceramic substrate causes interclass and intraclass imbalance problems. Identifying flaws in ceramic substrates has traditionally relied on aberrant material occurrences and characteristic quantities. However, defect substrates in ceramic are typically small and have a wide variety of defect distributions, thereby making defect detection more challenging and difficult. Thus, we propose a method for defect detection based on unsupervised learning and deep learning. First, the proposed method conducts K-means clustering for grouping instances according to their inherent complex characteristics. Second, the distribution of rarely occurring instances is balanced by using augmentation filters. Finally, a convolutional neural network is trained by using the balanced dataset. The effectiveness of the proposed method was validated by comparing the results with those of other methods. Experimental results show that the proposed method outperforms other methods.
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A Versatile Punch Stroke Correction Model for Trial V-Bending of Sheet Metals Based on Data-Driven Method. MATERIALS 2021; 14:ma14174790. [PMID: 34500879 PMCID: PMC8432557 DOI: 10.3390/ma14174790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/15/2021] [Accepted: 08/18/2021] [Indexed: 11/24/2022]
Abstract
During air bending of sheet metals, the correction of punch stroke for springback control is always implemented through repeated trial bending until achieving the forming accuracy of bending parts. In this study, a modelling method for correction of punch stroke is presented for guiding trial bending based on a data-driven technique. Firstly, the big data for the model are mainly generated from a large number of finite element simulations, considering many variables, e.g., material parameters, dimensions of V-dies and blanks, and processing parameters. Based on the big data, two punch stroke correction models are developed via neural network and dimensional analysis, respectively. The analytic comparison shows that the neural network model is more suitable for guiding trial bending of sheet metals than the dimensional analysis model, which has mechanical significance. The actual trial bending tests prove that the neural-network-based punch stroke correction model presents great versatility and accuracy in the guidance of trial bending, leading to a reduction in the number of trial bends and an improvement in the production efficiency of air bending.
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Surface Defect Detection Methods for Industrial Products: A Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167657] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology.
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Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation. SENSORS 2021; 21:s21165446. [PMID: 34450888 PMCID: PMC8400866 DOI: 10.3390/s21165446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 11/24/2022]
Abstract
As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.
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Liu C. Based on MCM nanomaterials: Recoverable metallic nanocatalysts in oxidation of sulfides and oxidative coupling of thiols. SYNTHETIC COMMUN 2021. [DOI: 10.1080/00397911.2021.1912769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Can Liu
- School of Electronic Engineering, Xi’an Shiyou University, Xi’an, PR China
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Zhou SS, Almarashi A, Dara RN, Issakhov A, Ge-JiLe H, Selim MM, Hajizadeh MR. Effect of permeability and MHD on nanoparticle transportation. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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42
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Free convection simulation of hybrid nanomaterial in permeable cavity with inclusion of magnetic force. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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43
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Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11156721] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.
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Qu S, Shi H, Zhao H, Yu L, Yu Y. Research on enterprise business model and technology innovation based on artificial intelligence. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2021; 2021:145. [PMID: 34249123 PMCID: PMC8254430 DOI: 10.1186/s13638-021-02025-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
Small- and medium-sized enterprises (SEMs) are the important part of economic society whose innovation activities are of great significance for building innovative country. In order to investigate how technological innovation (TI) and business model design (BMD) affect the business performance of SMEs, samples of 268 SMEs in the artificial intelligence industry and hierarchical regression models are used in the analysis. The results indicate that TI, BMD, and the matching of them have different effects on the innovation of SMEs of different sizes. These findings are helpful for enriching the theory of the fit between TI and BMD and providing theoretical guidance for the innovation activities in SEMs.
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Affiliation(s)
- Sunping Qu
- School of Management, JiangSu University, Zhenjiang, 212013 China
- College of Management, Wuxi Institute of Technology, Wuxi, 214121 China
| | - Hongwei Shi
- School of Management, JiangSu University, Zhenjiang, 212013 China
| | - Huanhuan Zhao
- College of Management, Wuxi Institute of Technology, Wuxi, 214121 China
| | - Lin Yu
- College of Management, Wuxi Institute of Technology, Wuxi, 214121 China
| | - Yunbo Yu
- College of Management, Wuxi Institute of Technology, Wuxi, 214121 China
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Zhou SS, Almarashi A, Talabany ZJ, Selim MM, Issakhov A, Li YM, Yao SW, Li Z. Augmentation of performance of system with dispersion of nanoparticles inside PCM. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.115921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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46
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47
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Liu H, Lu J, Zhao X, Xu T. Ionic liquids immobilized on nanomaterials: An efficient strategy in catalytic reactions. SYNTHETIC COMMUN 2021. [DOI: 10.1080/00397911.2021.1936057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Haitao Liu
- School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang, China
| | - Jingjing Lu
- School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang, China
| | - Xiudan Zhao
- School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang, China
| | - Tiejun Xu
- School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang, China
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Xu X, Wang W, Luo J, Mohamadi A. Theoretical investigation of chemical functionalization BxCyNz (x = z = 1, y = 2) nanotube with pralines amino acid. COMPUT THEOR CHEM 2021. [DOI: 10.1016/j.comptc.2021.113239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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49
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Bahadoran A, Liu Q, Liu B, Gu J, Zhang D, Fakhri A, Kumar Gupta V. Preparation of Sn/Fe nanoparticles for Cr (III) detection in presence of leucine, photocatalytic and antibacterial activities. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 253:119592. [PMID: 33640626 DOI: 10.1016/j.saa.2021.119592] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/31/2021] [Accepted: 02/03/2021] [Indexed: 06/12/2023]
Abstract
In this project, Sn-Fe bimetallic nanoparticles were prepared by a facile method. The bimetallic nanoparticles of it could be well established by a field emission scanning electron microscope micrographs. Due to the excellent synergistic influence between Sn-Fe nanoparticles and leucine indicated a great performance for determination of Cr3+. The material was characterized using the XRD, DLS, and zetasizer for theevaluation of crystal structure and morphologyinformation.The potential and effective size of Sn-Fe NPs was -29.10 mV and 30 nm, respectively. Cr3+ ions interaction with the Sn-Fe NPs-leucine probe was carried out in 1 min as response time. The limit of detection of Sn-Fe NPs for Cr(III) colorimetric method was 0.25 nM. The prepared nanoparticles showed impressive photocatalysis efficiency for degradation of MO was about 95.1% in 35 min, thus the prepared nanoparticles may be developed for the detoxification of pollution. The prepared nanoparticles depicted effective antibacterial activity againstC. botulinum and, H. pylori bacteria.
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Affiliation(s)
- Ashkan Bahadoran
- State Key Laboratory of Metal Matrix Composite, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qinglei Liu
- State Key Laboratory of Metal Matrix Composite, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Bowen Liu
- State Key Laboratory of Metal Matrix Composite, Shanghai Jiao Tong University, Shanghai 200240, China
| | - JiaJun Gu
- State Key Laboratory of Metal Matrix Composite, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Di Zhang
- State Key Laboratory of Metal Matrix Composite, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Ali Fakhri
- Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran; Department of Chemistry, Nano Smart Science Institute (NSSI), Tehran, Iran
| | - Vinod Kumar Gupta
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
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50
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Hassanin H, Zweiri Y, Finet L, Essa K, Qiu C, Attallah M. Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo Alloy and Properties Prediction Using Deep Learning Approaches. MATERIALS 2021; 14:ma14082056. [PMID: 33921804 PMCID: PMC8073357 DOI: 10.3390/ma14082056] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/05/2021] [Accepted: 04/14/2021] [Indexed: 11/16/2022]
Abstract
Ti-6Al-2Sn-4Zr-6Mo is one of the most important titanium alloys characterised by its high strength, fatigue, and toughness properties, making it a popular material for aerospace and biomedical applications. However, no studies have been reported on processing this alloy using laser powder bed fusion. In this paper, a deep learning neural network (DLNN) was introduced to rationalise and predict the densification and hardness due to Laser Powder Bed Fusion of Ti-6Al-2Sn-4Zr-6Mo alloy. The process optimisation results showed that near-full densification is achieved in Ti-6Al-2Sn-4Zr-6Mo alloy samples fabricated using an energy density of 77–113 J/mm3. Furthermore, the hardness of the builds was found to increase with increasing the laser energy density. Porosity and the hardness measurements were found to be sensitive to the island size, especially at high energy density. Hot isostatic pressing (HIP) was able to eliminate the porosity, increase the hardness, and achieve the desirable α and β phases. The developed model was validated and used to produce process maps. The trained deep learning neural network model showed the highest accuracy with a mean percentage error of 3% and 0.2% for the porosity and hardness. The results showed that deep learning neural networks could be an efficient tool for predicting materials properties using small data.
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Affiliation(s)
- Hany Hassanin
- School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK
- Correspondence:
| | - Yahya Zweiri
- Faculty of Science, Engineering and Computing, Kingston University London, London SW15 3DW, UK;
- Khalifa University Center for Autonomous Robotic Systems, Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Laurane Finet
- School of Materials and Metallurgy, University of Birmingham, Birmingham B15 2TT, UK; (L.F.); (K.E.); (M.A.)
| | - Khamis Essa
- School of Materials and Metallurgy, University of Birmingham, Birmingham B15 2TT, UK; (L.F.); (K.E.); (M.A.)
| | - Chunlei Qiu
- Department of Materials Processing Engineering and Automation, Beihang University, Xueyuan Road, Haidian District, Beijing 100191, China;
| | - Moataz Attallah
- School of Materials and Metallurgy, University of Birmingham, Birmingham B15 2TT, UK; (L.F.); (K.E.); (M.A.)
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