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Gao Z, Wang Y, Yu K, Dai Z, Song T, Zhang J, Huang C, Zhang H, Yang H. Cardiac Multi-Frequency Vibration Signal Sensor Module and Feature Extraction Method Based on Vibration Modeling. Sensors (Basel) 2024; 24:2235. [PMID: 38610445 PMCID: PMC11014338 DOI: 10.3390/s24072235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/20/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024]
Abstract
Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor's performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.
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Affiliation(s)
- Zhixing Gao
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuqi Wang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kang Yu
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
| | - Zhiwei Dai
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
| | - Tingting Song
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
| | - Jun Zhang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chengjun Huang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiying Zhang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Yang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (Z.G.); (Y.W.); (K.Y.); (Z.D.); (J.Z.); (C.H.); (H.Z.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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Zhu Y, Wei Y, Chen C, Chi N, Shi J. Minimalist Deployment of Neural Network Equalizers in a Bandwidth-Limited Optical Wireless Communication System with Knowledge Distillation. Sensors (Basel) 2024; 24:1612. [PMID: 38475151 DOI: 10.3390/s24051612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024]
Abstract
An equalizer based on a recurrent neural network (RNN), especially with a bidirectional gated recurrent unit (biGRU) structure, is a good choice to deal with nonlinear damage and inter-symbol interference (ISI) in optical communication systems because of its excellent performance in processing time series information. However, its recursive structure prevents the parallelization of the computation, resulting in a low equalization rate. In order to improve the speed without compromising the equalization performance, we propose a minimalist 1D convolutional neural network (CNN) equalizer, which is reconverted from a biGRU with knowledge distillation (KD). In this work, we applied KD to regression problems and explain how KD helps students learn from teachers in solving regression problems. In addition, we compared the biGRU, 1D-CNN after KD and 1D-CNN without KD in terms of Q-factor and equalization velocity. The experimental data showed that the Q-factor of the 1D-CNN increased by 1 dB after KD learning from the biGRU, and KD increased the RoP sensitivity of the 1D-CNN by 0.89 dB with the HD-FEC threshold of 1 × 10-3. At the same time, compared with the biGRU, the proposed 1D-CNN equalizer reduced the computational time consumption by 97% and the number of trainable parameters by 99.3%, with only a 0.5 dB Q-factor penalty. The results demonstrate that the proposed minimalist 1D-CNN equalizer holds significant promise for future practical deployments in optical wireless communication systems.
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Affiliation(s)
- Yiming Zhu
- Key Laboratory for Information Science of Electromagnetic Waves (MoE), Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China
| | - Yuan Wei
- Key Laboratory for Information Science of Electromagnetic Waves (MoE), Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China
| | - Chaoxu Chen
- Key Laboratory for Information Science of Electromagnetic Waves (MoE), Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China
| | - Nan Chi
- Key Laboratory for Information Science of Electromagnetic Waves (MoE), Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China
- Shanghai Engineering Research Center of Low-Earth-Orbit Satellite Communication and Applications, Shanghai 200433, China
- Shanghai Collaborative Innovation Center of Low-Earth-Orbit Satellite Communication Technology, Shanghai 200433, China
| | - Jianyang Shi
- Key Laboratory for Information Science of Electromagnetic Waves (MoE), Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China
- Shanghai Engineering Research Center of Low-Earth-Orbit Satellite Communication and Applications, Shanghai 200433, China
- Shanghai Collaborative Innovation Center of Low-Earth-Orbit Satellite Communication Technology, Shanghai 200433, China
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Shi Y, He T, Zhong J, Mei X, Li Y, Li M, Zhang W, Ji D, Su L, Lu T, Zhao X. Classification and rapid non-destructive quality evaluation of different processed products of Cyperus rotundus based on near-infrared spectroscopy combined with deep learning. Talanta 2024; 268:125266. [PMID: 37832457 DOI: 10.1016/j.talanta.2023.125266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 10/15/2023]
Abstract
The quality of traditional Chinese medicine is very important for human health, but the traditional quality control method is very tedious, which leads to the substandard quality of many traditional Chinese medicine. In order to solve the problem of time-consuming and laborious traditional quality control methods, this study takes traditional Chinese medicine Cyperus rotundus as an example, a comprehensive strategy of near-infrared (NIR) spectroscopy combined with One-dimensional convolutional neural network (1D-CNN) and chaotic map dung beetle optimization (CDBO) algorithm combined with BP neural network (BPNN) is proposed. This strategy has the advantages of fast and non-destructive. It can not only qualitatively distinguish Cyperus rotundus and various processed products, but also quantitatively predict two bioactive components. In classification, 1D-CNN successfully distinguished four kinds of processed products of Cyperus rotundus with 100 % accuracy. Quantitatively, a CDBO algorithm is proposed to optimize the performance of the BPNN quantitative model of two terpenoids, and compared with the BP, whale optimization algorithm (WOA)-BP, sparrow optimization algorithm (SSA)-BP, grey wolf optimization (GWO)-BP and particle swarm optimization (PSO)-BP models. The results show that the CDBO-BPNN model has the smallest error and has a significant advantage in predicting the content of active components in different processed products. To sum up, it is feasible to use near infrared spectroscopy to quickly evaluate the effect of processing methods on the quality of Cyperus rotundus, which provides a meaningful reference for the quality control of traditional Chinese medicine with many other processing methods.
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Affiliation(s)
- Yabo Shi
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Tianyu He
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Jiajing Zhong
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Xi Mei
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China.
| | - Yu Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Mingxuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Wei Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - De Ji
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Lianlin Su
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Tulin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China.
| | - Xiaoli Zhao
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China.
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Hasebe K, Kojima T, Fujimura S, Tamura K, Kawai Y, Kishimoto Y, Omori K. The Effect of Noise on Deep Learning for Classification of Pathological Voice. Laryngoscope 2024. [PMID: 38280184 DOI: 10.1002/lary.31303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 01/29/2024]
Abstract
OBJECTIVE This study aimed to evaluate the significance of background noise in machine learning models assessing the GRBAS scale for voice disorders. METHODS A dataset of 1406 voice samples was collected from retrospective data, and a 5-layer 1D convolutional neural network (CNN) model was constructed using TensorFlow. The dataset was divided into training, validation, and test data. Gaussian noise was added to test samples at various intensities to assess the model's noise resilience. The model's performance was evaluated using accuracy, F1 score, and quadratic weighted Cohen's kappa score. RESULTS The model's performance on the GRBAS scale generally declined with increasing noise intensities. For the G scale, accuracy dropped from 70.9% (original) to 8.5% (at the highest noise), F1 score from 69.2% to 1.3%, and Cohen's kappa from 0.679 to 0.0. Similar declines were observed for the remaining RBAS components. CONCLUSION The model's performance was affected by background noise, with substantial decreases in evaluation metrics as noise levels intensified. Future research should explore noise-tolerant techniques, such as data augmentation, to improve the model's noise resilience in real-world settings. LEVEL OF EVIDENCE This study evaluates a machine learning model using a single dataset without comparative controls. Given its non-comparative design and specific focus, it aligns with Level 4 evidence (Case-series) under the 2011 OCEBM guidelines Laryngoscope, 2024.
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Affiliation(s)
- Koki Hasebe
- Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tsuyoshi Kojima
- Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shintaro Fujimura
- Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Keiichi Tamura
- Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoshitaka Kawai
- Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yo Kishimoto
- Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koichi Omori
- Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Gao J, Guo J, Yuan F, Yi T, Zhang F, Shi Y, Li Z, Ke Y, Meng Y. An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism. Sensors (Basel) 2024; 24:390. [PMID: 38257483 DOI: 10.3390/s24020390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/18/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024]
Abstract
With the continuous operation of analog circuits, the component degradation problem gradually comes to the forefront, which may lead to problems, such as circuit performance degradation, system stability reductions, and signal quality degradation, which could be particularly evident in increasingly complex electronic systems. At the same time, due to factors, such as continuous signal transformation, the fluctuation of component parameters, and the nonlinear characteristics of components, traditional fault localization methods are still facing significant challenges when dealing with large-scale complex circuit faults. Based on this, this paper proposes a fault-diagnosis method for analog circuits using the ECWGEO algorithm, an enhanced version of the GEO algorithm, to de-optimize the 1D-CNN with an attention mechanism to handle time-frequency fusion inputs. Firstly, a typical circuit-quad op-amp dual second-order filter circuit is selected to construct a fault-simulation model, and Monte Carlo analysis is used to obtain a large number of samples as the dataset of this study. Secondly, the 1D-CNN network structure is improved for the characteristics of the analog circuits themselves, and the time-frequency domain fusion input is implemented before inputting it into the network, while the attention mechanism is introduced into the network. Thirdly, instead of relying on traditional experience for network structure determination, this paper adopts a parameter-optimization algorithm for network structure optimization and improves the GEO algorithm according to the problem characteristics, which enhances the diversity of populations in the late stage of its search and accelerates the convergence speed. Finally, experiments are designed to compare the results in different dimensions, and the final proposed structure achieved a 98.93% classification accuracy, which is better than other methods.
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Affiliation(s)
- Jiyuan Gao
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
| | - Jiang Guo
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
| | - Fang Yuan
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
| | - Tongqiang Yi
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
| | - Fangqing Zhang
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
| | - Yongjie Shi
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
| | - Zhaoyang Li
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
| | - Yiming Ke
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
| | - Yang Meng
- Key Lab Hydraul Machinery Transients, Wuhan University, Wuhan 430072, China
- School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
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Zhou X, Liu W, Li K, Lu D, Su Y, Ju Y, Fang Y, Yang J. Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy. Foods 2023; 12:4371. [PMID: 38231878 DOI: 10.3390/foods12234371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/19/2024] Open
Abstract
Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.
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Affiliation(s)
- Xuejian Zhou
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Wenzheng Liu
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Kai Li
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Dongqing Lu
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yuan Su
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yanlun Ju
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yulin Fang
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Jihong Yang
- College of Enology, Northwest A&F University, Yangling 712100, China
- College of Food Science and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, China
- Shaanxi Engineering Research Center for Viti-Viniculture, Yangling 712100, China
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Li Q, Zhou W, Zhang H. Integrating spectral and image information for prediction of cottonseed vitality. Front Plant Sci 2023; 14:1298483. [PMID: 38023899 PMCID: PMC10679674 DOI: 10.3389/fpls.2023.1298483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023]
Abstract
Cotton plays a significant role in people's lives, and cottonseeds serve as a vital assurance for successful cotton cultivation and production. Premium-quality cottonseeds can significantly enhance the germination rate of cottonseeds, resulting in increased cotton yields. The vitality of cottonseeds is a crucial metric that reflects the quality of the seeds. However, currently, the industry lacks a non-destructive method to directly assess cottonseed vitality without compromising the integrity of the seeds. To address this challenge, this study employed a hyperspectral imaging acquisition system to gather hyperspectral data on cottonseeds. This system enables the simultaneous collection of hyperspectral data from 25 cottonseeds. This study extracted spectral and image information from the hyperspectral data of cottonseeds to predict their vitality. SG, SNV, and MSC methods were utilized to preprocess the spectral data of cottonseeds. Following this preprocessing step, feature wavelength points of the cottonseeds were extracted using SPA and CARS algorithms. Subsequently, GLCM was employed to extract texture features from images corresponding to these feature wavelength points, including attributes such as Contrast, Correlation, Energy, and Entropy. Finally, the vitality of cottonseeds was predicted using PLSR, SVR, and a self-built 1D-CNN model. For spectral data analysis, the 1D-CNN model constructed after MSC+CARS preprocessing demonstrated the highest performance, achieving a test set correlation coefficient of 0.9214 and an RMSE of 0.7017. For image data analysis, the 1D-CNN model constructed after SG+CARS preprocessing outperformed the others, yielding a test set correlation coefficient of 0.8032 and an RMSE of 0.9683. In the case of fused spectral and image data, the 1D-CNN model built after SG+SPA preprocessing displayed the best performance, attaining a test set correlation coefficient of 0.9427 and an RMSE of 0.6872. These findings highlight the effectiveness of the 1D-CNN model and the fusion of spectral and image features for cottonseed vitality prediction. This research contributes significantly to the development of automated detection devices for assessing cottonseed vitality.
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Affiliation(s)
- Qingxu Li
- College of Computer Science, Anhui University of Finance & Economics, Bengbu, China
| | - Wanhuai Zhou
- College of Computer Science, Anhui University of Finance & Economics, Bengbu, China
| | - Hongzhou Zhang
- College of Mechanical and Electrical Engineering, Tarim University, Alar, China
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Ma J, Zhou X, Xie B, Wang C, Chen J, Zhu Y, Wang H, Ge F, Huang F. Application for Identifying the Origin and Predicting the Physiologically Active Ingredient Contents of Gastrodia elata Blume Using Visible-Near-Infrared Spectroscopy Combined with Machine Learning. Foods 2023; 12:4061. [PMID: 38002117 PMCID: PMC10670700 DOI: 10.3390/foods12224061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/16/2023] [Accepted: 10/20/2023] [Indexed: 11/26/2023] Open
Abstract
Gastrodia elata (G. elata) Blume is widely used as a health product with significant economic, medicinal, and ecological values. Due to variations in the geographical origin, soil pH, and content of organic matter, the levels of physiologically active ingredient contents in G. elata from different origins may vary. Therefore, rapid methods for predicting the geographical origin and the contents of these ingredients are important for the market. This paper proposes a visible-near-infrared (Vis-NIR) spectroscopy technology combined with machine learning. A variety of machine learning models were benchmarked against a one-dimensional convolutional neural network (1D-CNN) in terms of accuracy. In the origin identification models, the 1D-CNN demonstrated excellent performance, with the F1 score being 1.0000, correctly identifying the 11 origins. In the quantitative models, the 1D-CNN outperformed the other three algorithms. For the prediction set of eight physiologically active ingredients, namely, GA, HA, PE, PB, PC, PA, GA + HA, and total, the RMSEP values were 0.2881, 0.0871, 0.3387, 0.2485, 0.0761, 0.7027, 0.3664, and 1.2965, respectively. The Rp2 values were 0.9278, 0.9321, 0.9433, 0.9094, 0.9454, 0.9282, 0.9173, and 0.9323, respectively. This study demonstrated that the 1D-CNN showed highly accurate non-linear descriptive capability. The proposed combinations of Vis-NIR spectroscopy with 1D-CNN models have significant potential in the quality evaluation of G. elata.
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Affiliation(s)
- Jinfang Ma
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Xue Zhou
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
- Nansha Research Institute, Sun Yat-sen University, Guangzhou 511466, China
| | - Baiheng Xie
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Caiyun Wang
- Bijie Institute of Traditional Chinese Medicine, Bijie 551700, China
| | - Jiaze Chen
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Yanliu Zhu
- Nansha Research Institute, Sun Yat-sen University, Guangzhou 511466, China
| | - Hui Wang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Fahuan Ge
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China
- Nansha Research Institute, Sun Yat-sen University, Guangzhou 511466, China
| | - Furong Huang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
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9
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Zhou H, Chen J. An Enterprise Service Demand Classification Method Based on One-Dimensional Convolutional Neural Network with Cross-Entropy Loss and Enterprise Portrait. Entropy (Basel) 2023; 25:1211. [PMID: 37628241 PMCID: PMC10453757 DOI: 10.3390/e25081211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
To address the diverse needs of enterprise users and the cold-start issue of recommendation system, this paper proposes a quality-service demand classification method-1D-CNN-CrossEntorpyLoss, based on cross-entropy loss and one-dimensional convolutional neural network (1D-CNN) with the comprehensive enterprise quality portrait labels. The main idea of 1D-CNN-CrossEntorpyLoss is to use cross-entropy to minimize the loss of 1D-CNN model and enhance the performance of the enterprise quality-service demand classification. The transaction data of the enterprise quality-service platform are selected as the data source. Finally, the performance of 1D-CNN-CrossEntorpyLoss is compared with XGBoost, SVM, and logistic regression models. From the experimental results, it can be found that 1D-CNN-CrossEntorpyLoss has the best classification results with an accuracy of 72.44%. In addition, compared to the results without the enterprise-quality portrait, the enterprise-quality portrait improves the accuracy and recall of 1D-CNN-CrossEntorpyLoss model. It is also verified that the enterprise-quality portrait can further improve the classification ability of enterprise quality-service demand, and 1D-CNN-CrossEntorpyLoss is better than other classification methods, which can improve the precision service of the comprehensive quality service platform for MSMEs.
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Affiliation(s)
- Haixia Zhou
- School of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, China;
| | - Jindong Chen
- School of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, China;
- Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application, Beijing 100192, China
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Aldawsari H, Al-Ahmadi S, Muhammad F. Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals. Diagnostics (Basel) 2023; 13:2624. [PMID: 37627883 PMCID: PMC10453543 DOI: 10.3390/diagnostics13162624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/27/2023] Open
Abstract
EEG-based emotion recognition has numerous real-world applications in fields such as affective computing, human-computer interaction, and mental health monitoring. This offers the potential for developing IOT-based, emotion-aware systems and personalized interventions using real-time EEG data. This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features. This helped improve the overall efficiency of a deep learning model in terms of memory, time, and accuracy. Moreover, this work utilized a lightweight deep learning method, specifically one-dimensional convolutional neural networks (1D-CNN), to analyze EEG signals and classify emotional states. By capturing intricate patterns and relationships within the data, the 1D-CNN model accurately distinguished between emotional states (HV/LV and HA/LA). Moreover, an efficient method for data augmentation was used to increase the sample size and observe the performance deep learning model using additional data. The study conducted EEG-based emotion recognition tests on SEED, DEAP, and MAHNOB-HCI datasets. Consequently, this approach achieved mean accuracies of 97.6, 95.3, and 89.0 on MAHNOB-HCI, SEED, and DEAP datasets, respectively. The results have demonstrated significant potential for the implementation of a cost-effective IoT device to collect EEG signals, thereby enhancing the feasibility and applicability of the data.
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Affiliation(s)
- Haya Aldawsari
- Department of Computer Science, College of Arts and Science, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Saad Al-Ahmadi
- Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11543, Saudi Arabia;
- College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Farah Muhammad
- Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11543, Saudi Arabia;
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11
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Alarcón ÁS, Madrid NM, Seepold R, Ortega JA. Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor. Front Neurosci 2023; 17:1155900. [PMID: 37521695 PMCID: PMC10375719 DOI: 10.3389/fnins.2023.1155900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/16/2023] [Indexed: 08/01/2023] Open
Abstract
Background Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL). Materials and methods We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM). Results The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy. Conclusion The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI.
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Affiliation(s)
- Ángel Serrano Alarcón
- School of Informatics, Reutlingen University, Reutlingen, Germany
- Computer Languages and Systems, University of Seville, Sevilla, Spain
| | | | - Ralf Seepold
- Computer Science, HTWG Konstanz, Konstanz, Germany
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12
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Lim KM, Lee CP, Tan KS, Alqahtani A, Ali M. Fine-Tuned Temporal Dense Sampling with 1D Convolutional Neural Network for Human Action Recognition. Sensors (Basel) 2023; 23:s23115276. [PMID: 37300004 DOI: 10.3390/s23115276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/23/2023] [Accepted: 05/16/2023] [Indexed: 06/12/2023]
Abstract
Human action recognition is a constantly evolving field that is driven by numerous applications. In recent years, significant progress has been made in this area due to the development of advanced representation learning techniques. Despite this progress, human action recognition still poses significant challenges, particularly due to the unpredictable variations in the visual appearance of an image sequence. To address these challenges, we propose the fine-tuned temporal dense sampling with 1D convolutional neural network (FTDS-1DConvNet). Our method involves the use of temporal segmentation and temporal dense sampling, which help to capture the most important features of a human action video. First, the human action video is partitioned into segments through temporal segmentation. Each segment is then processed through a fine-tuned Inception-ResNet-V2 model, where max pooling is performed along the temporal axis to encode the most significant features as a fixed-length representation. This representation is then fed into a 1DConvNet for further representation learning and classification. The experiments on UCF101 and HMDB51 demonstrate that the proposed FTDS-1DConvNet outperforms the state-of-the-art methods, with a classification accuracy of 88.43% on the UCF101 dataset and 56.23% on the HMDB51 dataset.
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Affiliation(s)
- Kian Ming Lim
- Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
| | - Chin Poo Lee
- Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
| | - Kok Seang Tan
- Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
| | - Ali Alqahtani
- Department of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
- Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia
| | - Mohammed Ali
- Department of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
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13
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Hossain MS, Mahmud S, Khandakar A, Al-Emadi N, Chowdhury FA, Mahbub ZB, Reaz MBI, Chowdhury MEH. MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals. Bioengineering (Basel) 2023; 10:bioengineering10050579. [PMID: 37237649 DOI: 10.3390/bioengineering10050579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 04/26/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models' performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Nasser Al-Emadi
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Farhana Ahmed Chowdhury
- Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh
| | - Zaid Bin Mahbub
- Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Department of Electrical and Electronic Engineering, Independent University, Bashundhara, Dhaka 1229, Bangladesh
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14
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Huang Y, Yuan B, Wang X, Dai Y, Wang D, Gong Z, Chen J, Shen L, Fan M, Li Z. Industrial wastewater source tracing: The initiative of SERS spectral signature aided by a one-dimensional convolutional neural network. Water Res 2023; 232:119662. [PMID: 36738556 DOI: 10.1016/j.watres.2023.119662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/31/2022] [Accepted: 01/22/2023] [Indexed: 06/18/2023]
Abstract
The spectral fingerprint is a significant concept in nontarget screening of environmental samples to direct identification efforts to relevant and important features. Surface-enhanced Raman scattering (SERS) has long been recognized as an optical method that can provide fingerprint-like chemical information at the single-molecule level. Here, the advanced one-dimensional convolutional neural network (1D-CNN) approach was applied to accurately identify the SERS spectral signature of industrial wastewaters for source tracing. A total of 66,000 SERS spectra were acquired from wastewaters of 22 factories across 10 industrial categories at three excitation wavelengths after data augmentation. The dataset was used to train a 1D-CNN model consisting of three convolutional layers to achieve adequate feature extraction of SERS spectra. As a proof-of-concept, multimixed wastewater samples were used to simulate practical pollution scenarios and evaluate the application potential of the model. The SERS-1D-CNN platform can identify the amount and factory information of wastewaters in multimixed samples, which achieves a recognition accuracy rate of 97.33%. The results suggest that even in a complex and unknown water environment, the 1D-CNN model can accurately identify industrial wastewaters in precollected datasets, exhibiting excellent potential in pollution source tracing.
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Affiliation(s)
- Yuting Huang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Bingxue Yuan
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Xueqing Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Yongsheng Dai
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Dongmei Wang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Zhengjun Gong
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Junmin Chen
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Li Shen
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
| | - Meikun Fan
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
| | - Zhilin Li
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China
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15
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Yang H, Li X, Zhang S, Li Y, Zhu Z, Shen J, Dai N, Zhou F. A one-dimensional convolutional neural network based deep learning for high accuracy classification of transformation stages in esophageal squamous cell carcinoma tissue using micro-FTIR. Spectrochim Acta A Mol Biomol Spectrosc 2023; 289:122210. [PMID: 36508904 DOI: 10.1016/j.saa.2022.122210] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/08/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
Among the most frequently diagnosed cancers in developing countries, esophageal squamous cell carcinoma (ESCC) ranks among the top six causes of death. It would be beneficial if a rapid, accurate, and automatic ESCC diagnostic method could be developed to reduce the workload of pathologists and improve the effectiveness of cancer treatments. Using micro-FTIR spectroscopy, this study classified the transformation stages of ESCC tissues. Based on 6,352 raw micro-FTIR spectra, a one-dimensional convolutional neural network (1D-CNN) model was constructed to classify-five stages. Based on the established model, more than 93% accuracy was achieved at each stage, and the accuracy of identifying proliferation, low grade neoplasia, and ESCC cancer groups was achieved 99% for the test dataset. In this proof-of-concept study, the developed method can be applied to other diseases in order to promote the use of FTIR spectroscopy in cancer pathology.
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Affiliation(s)
- Haijun Yang
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Xianchang Li
- Huzhou College, Huzhou 313000, Zhejiang Province, China; Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China.
| | - Shiding Zhang
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Yuan Li
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Zunwei Zhu
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, Anyang Institute of Technology, Anyang 455000, Henan Province, China
| | - Jingwei Shen
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Ningtao Dai
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China
| | - Fuyou Zhou
- Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Henan Key Medical Laboratory of Precise Prevention and Treatment of Esophageal Cancer, Anyang 455001, Henan Province, China.
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16
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Kolappan Geetha G, Yang HJ, Sim SH. Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification. Sensors (Basel) 2023; 23:1419. [PMID: 36772459 PMCID: PMC9919036 DOI: 10.3390/s23031419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/19/2023] [Accepted: 01/22/2023] [Indexed: 06/18/2023]
Abstract
Existing deep learning (DL) models can detect wider or thicker segments of cracks that occupy multiple pixels in the width direction, but fail to distinguish the thin tail shallow segment or propagating crack occupying fewer pixels. Therefore, in this study, we proposed a scheme for tracking missing thin/propagating crack segments during DL-based crack identification on concrete surfaces in a computationally efficient manner. The proposed scheme employs image processing as a preprocessor and a postprocessor for a 1D DL model. Image-processing-assisted DL as a precursor to DL eliminates labor-intensive labeling and the plane structural background without any distinguishable features during DL training and testing; the model identifies potential crack candidate regions. Iterative differential sliding-window-based local image processing as a postprocessor to DL tracks missing thin cracks on segments classified as cracks. The capability of the proposed method is demonstrated on low-resolution images with cracks of single-pixel width, captured using unmanned aerial vehicles on concrete structures with different surface textures, different scenes with complicated disturbances, and optical variability. Due to the multi-threshold-based image processing, the overall approach is invariant to the choice of initial sensitivity parameters, hyperparameters, and the sequence of neuron arrangement. Further, this technique is a computationally efficient alternative to semantic segmentation that results in pixelated mapping/classification of thin crack regimes, which requires labor-intensive and skilled labeling.
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Affiliation(s)
- Ganesh Kolappan Geetha
- School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Hyun-Jung Yang
- Smart Convergence Research Department, Power Technology Research Institute, KEPCO E & C, Gimcheon 39660, Republic of Korea
| | - Sung-Han Sim
- School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Republic of Korea
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17
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Zhang N, Zhao LS. Method for real-time prediction of cutter wear during shield tunnelling: A new wear rate index and MCNN-GRU. MethodsX 2023; 10:102017. [PMID: 36713303 PMCID: PMC9880237 DOI: 10.1016/j.mex.2023.102017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
Cutter wear is one of the key factors influencing construction efficiency during shield tunnelling. Prediction of cutter wear can improve construction efficiency by reducing the times of cutter inspections in engineering practice. Evaluation of cutter life is vital for cutter wear prediction, however, existing cutter life indices can only estimate the health condition of all cutters on cutterhead on a holistic basis. A new index was proposed to evaluate cutter wear located at a specific installation position on cutterhead. A deep learning model integrating the index was developed for the estimation of accumulated cutter wear during real time shield tunnelling. The new index can be obtained by monitored field parameters and can predict cutter wear with historical wear patterns. The input and output data samples were reshaped for multi-step prediction. A shield tunnelling section in Guangzhou weathered granite was used for validation. The proposed method can help reduce the cost of cutter replacement by reducing the times of machine interventions. The method article is a companion paper to the original article [1].•Proposed index for prediction of cutter wear rate.•Deep learning model of 1D-CNN and GRU.•Multi-step cutter wear prediction.
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Affiliation(s)
- Nan Zhang
- Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lin-Shuang Zhao
- Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou 515063, China
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18
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Liu J, Fan S, Cheng W, Yang Y, Li X, Wang Q, Liu B, Xu Z, Wu Y. Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN. Foods 2023; 12. [PMID: 36673386 DOI: 10.3390/foods12020295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/02/2023] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds.
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Chen X, Cheng G, Liu S, Meng S, Jiao Y, Zhang W, Liang J, Zhang W, Wang B, Xu X, Xu J. Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish. Spectrochim Acta A Mol Biomol Spectrosc 2022; 279:121350. [PMID: 35609391 DOI: 10.1016/j.saa.2022.121350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 06/15/2023]
Abstract
Salmon and Cod are economically significant world-class fish that have high economic value. It is difficult to accurately sort and process them by appearance during harvest and transportation. Conventional chemical detection means are time-consuming and costly, which greatly affects the cost and efficiency of Fishery production. Therefore, there is an urgent need for smart Fisheries methods which use for the classification of mixed fish. In this paper, near-infrared spectroscopy (NIRS) was used to assess salmon and cod samples. This study aims to evaluate feasibility of a back-propagation neural network (BPNN) and a convolutional neural network (CNN) for identifying different species of fishes by the corresponding spectra in comparison to traditional chemometrics Partial Least Squares. After comparing the effects of different batch sizes, number of convolutional kernels, number of convolutional layers, and number of pooling layers on the classification of NIRS spectra comparing different structures of one-dimensional (1D)-CNN, we propose the 1D-CNN-8 model that is most suitable for the classification of mixed fish. Compared with the results of traditional chemometrics methods and BPNN, the prediction model of the 1D-CNN model can reach 98.00% Accuracy and the parameters are significantly better than others. Meanwhile, the parameters and floating-point operations of the optimal model are both small. Therefore, the improved CNN model based on the NIRS can effectively and quickly identify different kinds of fish samples and contribute to realizing edge computing at the same time.
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Affiliation(s)
- Xinghao Chen
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Gongyi Cheng
- The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Shuhan Liu
- The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Sizhuo Meng
- The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Yiping Jiao
- The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Wenjie Zhang
- The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Jing Liang
- The Key Laboratory of Weak-Light Nonlinear Photonics, Ministry of Education, School of Physics, Nankai University, Tianjin 300071, China
| | - Wang Zhang
- Lianyungang Customs P.R.C, Lianyungang 222042, China
| | - Bin Wang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Xiaoxuan Xu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
| | - Jing Xu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
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Patiyal S, Dhall A, Raghava GPS. A deep learning-based method for the prediction of DNA interacting residues in a protein. Brief Bioinform 2022; 23:6658239. [PMID: 35943134 DOI: 10.1093/bib/bbac322] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/01/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcription, regulation and splicing of genes. In this study, we trained our models on a training dataset of 646 DNA-binding proteins having 15 636 DNA interacting and 298 503 non-interacting residues. Our trained models were evaluated on an independent dataset of 46 DNA-binding proteins having 965 DNA interacting and 9911 non-interacting residues. All proteins in the independent dataset have less than 30% of sequence similarity with proteins in the training dataset. A wide range of traditional machine learning and deep learning (1D-CNN) techniques-based models have been developed using binary, physicochemical properties and Position-Specific Scoring Matrix (PSSM)/evolutionary profiles. In the case of machine learning technique, eXtreme Gradient Boosting-based model achieved a maximum area under the receiver operating characteristics (AUROC) curve of 0.77 on the independent dataset using PSSM profile. Deep learning-based model achieved the highest AUROC of 0.79 on the independent dataset using a combination of all three profiles. We evaluated the performance of existing methods on the independent dataset and observed that our proposed method outperformed all the existing methods. In order to facilitate scientific community, we developed standalone software and web server, which are accessible from https://webs.iiitd.edu.in/raghava/dbpred.
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Affiliation(s)
- Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
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Abstract
Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic-resistant bacteria. Although their huge diversity and virulence make them particularly attractive for targeting a wide range of bacterial pathogens, it is difficult to select suitable phages due to their high specificity which limits their host range. In addition, other challenges remain such as structural fragility under certain environmental conditions, immunogenicity of phage therapy, or development of bacterial resistance. The use of genetically engineered phages may reduce characteristics that hinder prophylactic and therapeutic applications of phages. Nowadays, there is no systematic method to modify a given phage genome conferring its sought characteristics. We explore the use of artificial intelligence for this purpose as it has the potential to both guide and accelerate genome modification to generate phage variants with unique properties that overcome the limitations of natural phages. We propose an original architecture composed of two deep learning-driven components: a phage-bacterium interaction predictor and a phage genome-sequence generator. The former is a multi-branch 1-D convolutional neural network (1D-CNN) that analyses phage and bacterial genomes to predict interactions. The latter is a recurrent neural network, more particularly a long short-term memory (LSTM), that performs genomic modifications to a phage to offer substantial host range improvement. For this component, we developed two different architectures composed of one or two stacked LSTM layers with 256 neurons each. These generators are used to modify, more precisely to rewrite, the genome sequence of 42 selected phages, while the predictor is used to estimate the host range of the modified bacteriophages across 46 strains of Pseudomonas aeruginosa. The proposed generators, trained with an average accuracy of 96.1%, are able to improve the host range for an average of 18 phages among the 42 under study, increasing both their average host range, by 73.0 and 103.7%, and the maximum host ranges from 21 to 24 and 29, respectively. These promising results showed that the use of deep learning methodologies allows genetic modification of phages to extend, for instance, their host range, confirming the potential of these approaches to guide bacteriophage engineering.
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Affiliation(s)
- Shabnam Ataee
- Institute of Information and Communication Technology (IICT), School of Management and Engineering Vaud (HEIG-VD), Yverdon-les-Bains, Switzerland
- HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
- CI4CB—Computational Intelligence for Computational Biology, SIB—Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Xavier Brochet
- Institute of Information and Communication Technology (IICT), School of Management and Engineering Vaud (HEIG-VD), Yverdon-les-Bains, Switzerland
- HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
- CI4CB—Computational Intelligence for Computational Biology, SIB—Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Carlos Andrés Peña-Reyes
- Institute of Information and Communication Technology (IICT), School of Management and Engineering Vaud (HEIG-VD), Yverdon-les-Bains, Switzerland
- HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
- CI4CB—Computational Intelligence for Computational Biology, SIB—Swiss Institute of Bioinformatics, Lausanne, Switzerland
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22
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Abboush M, Bamal D, Knieke C, Rausch A. Intelligent Fault Detection and Classification Based on Hybrid Deep Learning Methods for Hardware-in-the-Loop Test of Automotive Software Systems. Sensors (Basel) 2022; 22:4066. [PMID: 35684686 DOI: 10.3390/s22114066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/16/2022] [Accepted: 05/24/2022] [Indexed: 11/16/2022]
Abstract
Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%.
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23
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Wang Y, Zhu C, Wang Y, Sun J, Ling D, Wang L. Survival risk prediction model for ESCC based on relief feature selection and CNN. Comput Biol Med 2022; 145:105460. [PMID: 35364307 DOI: 10.1016/j.compbiomed.2022.105460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 01/10/2023]
Abstract
Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive system with poor prognosis and high mortality. It is of great significance to predict the prognosis risk of patients with cancer by using medical pathology information. To take full advantage of the clinic pathological information of ESCC patients and improve the accuracy of postoperative survival risk prediction, this paper proposes an ESCC survival risk prediction model based on Relief feature selection and convolutional neural network (CNN). Firstly, statistical analysis methods and relief feature selection algorithm are used to extract the important risk factors related to the survival risk of patients. Then, One-dimensional convolutional neural network (1D-CNN) is used to establish the survival risk prediction model of patients with esophageal cancer. Finally, the data of patients with esophageal cancer provided by the First Affiliated Hospital of Zhengzhou University is used to assess the performance of the model. The results show that the model proposed in this paper has a high accuracy rate, which can effectively predict the postoperative survival risk of the patient through the clinical phenotypic index of the patient.
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Affiliation(s)
- Yanfeng Wang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 45002, China
| | - Chuanqian Zhu
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 45002, China
| | - Yan Wang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 45002, China.
| | - Junwei Sun
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 45002, China
| | - Dan Ling
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 45002, China
| | - Lidong Wang
- State Key Laboratory of Esophageal Cancer Prevention, Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China
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24
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Wang S, He H, Lv R, He W, Li C, Cai N. Classification modeling method for hyperspectral stamp-pad ink data based on one-dimensional convolutional neural network. J Forensic Sci 2021; 67:550-561. [PMID: 34617278 DOI: 10.1111/1556-4029.14909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 08/25/2021] [Accepted: 09/21/2021] [Indexed: 11/28/2022]
Abstract
In the questioned document, the examination of stamp-pad ink is crucial scientific evidence to discern the difference between genuine and forged documents. In this study, a new method for rapid and non-destructive identification of types of stamp-pad inks by combining hyperspectral imaging (HSI) technology and deep learning was developed. Twenty stamp-pad inks of different brands and models were collected and numbered in turn, and then, each of them was sealed six times repeatedly on the A4 printing paper for the test. After that, the hyperspectral imager was used to collect the hyperspectral images and the reflectance spectral data were obtained after pixel fusion. Principal component analysis (PCA) and non-negative matrix factorization (NMF) were used to deal with the dataset, but visual results were not good. Then, back propagation neural network (BPNN) and one-dimensional convolutional neural network (1D-CNN) were constructed and their merits and drawbacks were compared. The final loss function of the BPNN of training set and validation set was stable at 0.27 and 0.42, and the classification accuracy of the training set and validation set reached 90.02% and 83.99%, respectively. Compared with the BPNN, the 1D-CNN had better stability and efficiency for the classification. The loss function of the training set and validation set was as low as 0.068 and 0.075, and the final classification accuracy reached 98.30% and 97.94%, respectively. Therefore, the combination of hyperspectral imaging technology and 1D-CNN represents a potentially simple, non-destructive, and rapid method for stamp-pad inks detection and classification.
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Affiliation(s)
- Shuyue Wang
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Hongyuan He
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Rulin Lv
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Weiwen He
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Chunyu Li
- School of Criminal Investigation, People's Public Security, University of China, Beijing, China
| | - Nengbin Cai
- Shanghai Key Laboratory of Criminal Scene Evidence, Shanghai, China
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25
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Osman RA, Saleh SN, Saleh YNM. A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-Enabled IoT. Sensors (Basel) 2021; 21:6555. [PMID: 34640869 DOI: 10.3390/s21196555] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 11/17/2022]
Abstract
The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.
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26
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Chai Q, Zeng J, Lin D, Li X, Huang J, Wang W. Improved 1D convolutional neural network adapted to near-infrared spectroscopy for rapid discrimination of Anoectochilus roxburghii and its counterfeits. J Pharm Biomed Anal 2021; 199:114035. [PMID: 33819697 DOI: 10.1016/j.jpba.2021.114035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 03/08/2021] [Accepted: 03/19/2021] [Indexed: 11/28/2022]
Abstract
Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae) is a rare traditional Chinese medicine. For seeking high profit, some traditional Chinese medicine sellers usually adulterated A. roxburghii with Goodyera Schlechtendaliana and Ludisia discolor or directly fake A. roxburghii using Anoectochilus formosanus. These counterfeits with similar appearance greatly influence the prescription efficacy. Therefore, there is an urgent need for an effective and fast authentication method to identify A. roxburghii and its counterfeits. In this paper, the near-infrared spectroscopy (NIRS) data of A. roxburghii and its counterfeits are mearsured. Then, an improved inception architecture based 1-dimensional convolutional neural network (Improved 1D-Inception-CNN) is designed for processing the NIRS data and identifying A. roxburghii and its counterfeits. The Improved 1D-Inception-CNN has less parameters and high calculation efficiency which makes the identification model more practical. The experimental results show that compared with traditional structured CNN models, the complexity of the Improved 1D-Inception-CNN is reduced by 40 %, the parameters are reduced by 50 % and the performances are improved by 1.01 %. Therefore, the Improved 1D-Inception-CNN model based on NIRS technology can effectively and quickly identify A. roxburghii and its counterfeits.
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Affiliation(s)
- Qinqin Chai
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian, 350108, China; Jinjiang Science and Education Park of Fuzhou University, Jinjiang, Fujian, 362251, China; Ministry of Education Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou University, Fuzhou, Fujian, 350108, China.
| | - Jian Zeng
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian, 350108, China
| | - Donghong Lin
- Medical Technology and Engineering College, Fujian Medical University, Fuzhou, Fujian, 350004, China
| | - Xianghui Li
- Medical Technology and Engineering College, Fujian Medical University, Fuzhou, Fujian, 350004, China
| | - Jie Huang
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian, 350108, China.
| | - Wu Wang
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian, 350108, China; Ministry of Education Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou University, Fuzhou, Fujian, 350108, China
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27
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Mendonça F, Mostafa SS, Morgado-Dias F, Ravelo-García AG. A method based on cardiopulmonary coupling analysis for sleep quality assessment with FPGA implementation. Artif Intell Med 2021; 112:102019. [PMID: 33581831 DOI: 10.1016/j.artmed.2021.102019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/06/2020] [Accepted: 01/10/2021] [Indexed: 11/22/2022]
Abstract
The relevance of sleep quality examination for clinical diagnosis is increasing with the discovery of new relationships with several diseases and the overall wellness. This assessment is commonly performed by conducting interviews with the subjects, evaluating the self-report and psychological variables. However, this approach has a major constraint since the subject is a poor self-observer of sleep behaviors. To address this issue, a method based on the examination of a physiological signal was developed. Specifically, the single-lead electrocardiogram signal was examined to estimate the cardiopulmonary coupling between the electrocardiogram derived respiration signal and the normal-to-normal sinus interbeat interval series. A one dimensional array was created from the coupling signal and was fed to a convolutional neural network to estimate the sleep quality. The age-related cyclic alternating pattern rate percentages in healthy subjects was considered as the classification reference. An accuracy of 91 % was attained by the developed model, with an area under the receiver operating characteristic curve of 97 %. The performance is in the upper range of the reported performance by the works presented in the state of the art, advocating the relevance of the proposed method. The model was implemented in a small field programmable gate array board. Hence, a home monitoring device was created, composed of a processing unit, a sensing module and a display unit. The device is resilient, easy to self-assemble and operate, and can conceivably be employed for clinical analysis.
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