A Novel Reconstruction Method for Temperature Distribution Measurement Based on Ultrasonic Tomography

An Adaptive Optimization Method for Acoustic Temperature Measurement Topology Based on Multiple Sub-Objectives

Recent years have seen a surge in study interest in acoustic temperature measurement because of its exceptional non-invasiveness, high precision, and fast response characteristics. Its main benefit is that it may rely on the temperature field reconstruction technique to obtain the entire temperature distribution information, circumventing the limitations of point-type thermometry. Studies have shown that the acoustic wave transducer topology is a key factor affecting the reconstruction effect. In engineering, a simple uniform placement or trial-and-error methods are often used to determine the transducer topology. However, these approaches lack adaptability in complex temperature fields, resulting in poor accuracy and stability. In this paper, based on the previous research on high-precision temperature field reconstruction algorithms, an adaptive optimization method of acoustic temperature measurement topology based on multiple sub-objectives is proposed. The method further improves the reconstruction of asymmetric complex temperature fields by constructing a new optimization variable and a new optimization objective. Comparison experiments with existing optimization methods demonstrate the effectiveness of the new variables and objectives. Additionally, the reconstruction performance of the proposed method is thoroughly evaluated. The results indicate that the method enables adaptive optimization of transducer topology. Moreover, the optimized results exhibit high accuracy and stability in reconstructing complex, asymmetric temperature fields.

Simultaneous Reconstruction of Gas Concentration and Temperature Using Acoustic Tomography

Acoustic tomography utilizes sensor arrays to collect sound wave signals, enabling non-contact measurement of physical parameters within an area of interest. Compared to optical technologies, acoustic tomography offers the advantages of low cost, low maintenance, and easy installation. Current research in acoustic tomography mainly focuses on reconstruction algorithms for temperature fields, while monitoring the composition and concentration of gases is significant for ensuring safety and improving efficiency, such as in scenarios like boiler furnaces and aviation engine nozzles. In excitable gases, the speed of sound exhibits an S-shaped curve that changes with frequency, a characteristic that could be potentially useful for acoustic tomography. Therefore, this study primarily discusses the quantitative calculation of gas concentration and temperature based on the dispersion of the speed of sound. By employing graphic processing and pattern matching methods, a coupled relationship of the dispersion of the speed of sound with gas concentration and temperature is established. The projection intersection method is used to calculate the concentration and temperature of binary and ternary gas mixtures. Combined with the inversion method, a joint reconstruction method for gas concentration fields and temperature fields based on the dispersion of the speed of sound is developed. The feasibility of the proposed simultaneous reconstruction method for temperature and concentration fields is validated using numerical simulations. Additionally, an acoustic tomography experimental system was set up to conduct reconstruction experiments for binary gas concentration fields and temperature fields, confirming the effectiveness of the proposed method.

Hierarchical Harris hawks optimizer for feature selection

INTRODUCTION

The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection.

OBJECTIVES

To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features.

METHODS

We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets.

RESULTS

Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms.

CONCLUSION

Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.

Estimation of ecological footprint based on tourism development indicators using neural networks and multivariate regression

The ecological footprint has attracted a lot of attention in the top tourism destination countries, and this issue may be worrying. This study aims to estimate the ecological footprint, using such indicators as economic growth, natural resources, human capital, and the number of tourists in top tourism destination countries. For this purpose, artificial neural network models and multivariate regression were used for a period of 24 years (1995-2019). The results of the study showed a significant positive correlation between economic growth and ecological footprint. Multivariate regression estimation (R = 0.75) is weaker than neural network models (R = 96.3). Regarding predicting the ecological footprint, neural network models have better performance in comparison with the multivariate regression statistical methods. Accordingly, one can say that for planning ecological footprint, deeper look at neural networks can be more effective in predicting top tourism destination countries.

Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.

Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

Advances in Single-component inorganic nanostructures for photoacoustic imaging guided photothermal therapy

Phototheranostic based on photothermal therapy (PTT) and photoacoustic imaging (PAI), as one of avant-garde medical techniques, have sparked growing attention because it allows noninvasive, deeply penetrative, and highly selective and effective therapy. Among a variety of phototheranostic nanoagents, single-component inorganic nanostructures are found to be novel and attractive PAI and PTT combined nanotheranostic agents and received tremendous attention, which not only exhibit structural controllability, high tunability in physiochemical properties, size-dependent optical properties, high reproducibility, simple composition, easy functionalization, and simple synthesis process, but also can be endowed with multiple therapeutic and imaging functions, realizing the superior therapy result along with bringing less foreign materials into body, reducing systemic side effects and improving the bioavailability. In this review, according to their synthetic components, conventional single-component inorganic nanostructures are divided into metallic nanostructures, metal dichalcogenides, metal oxides, carbon based nanostructures, upconversion nanoparticles (UCNPs), metal organic frameworks (MOFs), MXenes, graphdiyne and other nanostructures. On the basis of this category, their detailed applications in PAI guide PTT of tumor treatment are systematically reviewed, including synthesis strategies, corresponding performances, and cancer diagnosis and therapeutic efficacy. Before these, the factors to influence on photothermal effect and the principle of in vivo PAI are briefly presented. Finally, we also comprehensively and thoroughly discussed the limitation, potential barriers, future perspectives for research and clinical translation of this single-component inorganic nanoagent in biomedical therapeutics.

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields. Its aim is to improve the under-determination of the inverse problem by the sparse representation of the sound slowness signal (i.e., reciprocal of sound velocity). In the MDL algorithm, the K-SVD dictionary learning algorithm is used to construct corresponding sparse dictionaries for sound slowness signals of different types of temperature fields; the KNN peak-type classifier is employed for the joint use of multiple dictionaries; the orthogonal matching pursuit (OMP) algorithm is used to obtain the sparse representation of sound slowness signal in the sparse domain; then, the temperature distribution is obtained by using the relationship between sound slowness and temperature. Simulation and actual temperature distribution reconstruction experiments show that the MDL algorithm has smaller reconstruction errors and provides more accurate information about the temperature field, compared with the compressed sensing and improved orthogonal matching pursuit (CS-IMOMP) algorithm, which is an algorithm based on compressed sensing and improved orthogonal matching pursuit (in the CS-IMOMP, DFT dictionary is used), the least square algorithm (LSA) and the simultaneous iterative reconstruction technique (SIRT).

An accurate estimation algorithm for structural change points of multi-dimensional stochastic models

In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic Graphical Modeling model based on multivariate normal hypothesis is constructed, and the relationship between the Graphical Gaussian model and the linear regression model is determined. The parameters of the multi-dimensional stochastic model are estimated by using the parameter estimation algorithm of the multi-dimensional stochastic model containing intermediate variables. According to the parameter estimation results of the multi-dimensional stochastic model, the structural change point estimation results of the multi-dimensional stochastic model are obtained by using the accurate estimation algorithm of the structural change point based on the MLE identification local drift time. The experimental results show that the proposed algorithm has higher estimation accuracy of structural change points than the control algorithms, which shows that it can effectively estimate the structural change points of multi-dimensional random models and has higher practicability.

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

This paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods.

Interactive Display of Images in Digital Exhibition Halls under Artificial Intelligence and Mixed Reality Technology

The attractiveness of traditional exhibition halls to young people is gradually decreasing. Combining modern digital technology to improve the display effect of the exhibition hall can effectively enhance the effect of cultural publicity. This article introduces the technology of image interaction and mixed reality (MR) to improve the historical and cultural propaganda level of the Shaanxi exhibition hall. The advantages of MR technology in applying digital exhibition halls are theoretically expounded. A theoretical plan for Shaanxi history and culture-related display areas is designed using artificial intelligence combined with  MR  technology. In addition, the survey respondent's evaluation of the effect of the new exhibition hall is obtained using a questionnaire survey. The survey results show that 97% of people like the history and culture of Shaanxi but only 13% of the people say they know or know very well about the history and culture of Shaanxi. In addition, 60% of the tourists say they are satisfied with the cultural experience of Shaanxi, and only 27% of the tourists are very satisfied. Also, 96% of tourists are willing to experience Shaanxi's history and culture through digital exhibition halls, and 93% are willing to participate in cultural experience activities based on MR technology. The survey results prove that tourists are satisfied with the effect of the new exhibition hall. Tourists want to add a distinctive form of cultural experience to the exhibition hall. They are willing to accept digital exhibition halls incorporating MR technology and are very happy to participate in the exhibition method of image interaction. This shows that the use of image interactive display based on MR technology in the layout of the exhibition hall is recognized by people and has strong feasibility. This article has reference significance for the digital upgrade of the exhibition hall and the development of the cultural tourism industry.

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

The paper aims to apply the deep learning-based image visualization technology to extract, recognize, and analyze human skeleton movements and evaluate the effect of the deep learning-based human-computer interaction (HCI) system. Dance education is researched. Firstly, the Visual Geometry Group Network (VGGNet) is optimized using Convolutional Neural Network (CNN). Then, the VGGNet extracts the human skeleton movements in the OpenPose database. Secondly, the Long Short-Term Memory (LSTM) network is optimized and recognizes human skeleton movements. Finally, an HCI system for dance education is designed based on the extraction and recognition methods of human skeleton movements. Results demonstrate that the highest extraction accuracy is 96%, and the average recognition accuracy of different dance movements is stable. The effectiveness of the proposed model is verified. The recognition accuracy of the optimized F-Multiple LSTMs is increased to 88.9%, suitable for recognizing human skeleton movements. The dance education HCI system’s interactive accuracy built by deep learning-based visualization technology reaches 92%; the overall response time is distributed between 5.1 s and 5.9 s. Hence, the proposed model has excellent instantaneity. Therefore, the deep learning-based image visualization technology has enormous potential in human movement recognition, and combining deep learning and HCI plays a significant role.

Optimization Design of Street Public Space Layout on Account of Internet of Things and Deep Learning

With the gradual improvement of material living standards, people have higher and higher requirements for the livability of modern cities. As an important component of urban construction, the optimal layout of street public space has gradually received more and more attention. In the development stage of the new era, it is very important to improve the image of the city by transforming the street construction, optimizing the urban public space, and building a place full of vitality. Implementing the people-oriented connotation and improving the green travel components in the city, such as encouraging walking and increasing bicycles, are of great significance for optimizing the street public space. This article studies the relevant content of the optimization design of street public space layout based on the Internet of Things and deep learning and expounds the solutions for the optimization design of street public space layout based on the Internet of Things and deep learning. Design research provides cutting-edge scientific theories and evidence. This paper uses data to prove that based on the Internet of Things and deep learning technology, the optimized design of street public space layout has increased the latter’s recognition among residents by an average of 21.7%. The designed model has both space utilization and environmental protection. Very good results have been obtained.

A comprehensive and systematic literature review on the employee attendance management systems based on cloud computing

Attendance is critical to the success of any business or industry. As a result, most businesses and institutions require a system to track staff attendance. On the other hand, cloud computing technology is being utilized in the human resource management sector. It may be an excellent option for processing and storing large amounts of data and improving management effectiveness to a desirable level. Hence, this paper examines cloud infrastructures for employee attendance management in which the articles are categorized into three groups. The results show that cloud infrastructure has a significant and positive impact on the management of employee attendance systems. Also, the results reveal that the radio frequency identification authentication protocol protects the privacy of tags and readers against database memory. When references operate properly, they help the people concerned and society by making workplaces more efficient and safer.

Construction of Emergency Procurement System and System Improvement Based on Convolutional Neural Network

At this stage, countries around the world have their own operating management model for the procurement system of emergency equipment. This article analyzes the influencing factors affecting the operation of the emergency procurement system through a convolutional neural network analysis method, and the contract management of the emergency procurement system is realized. Management and monitoring and balance of interests on supply and demand also meet the requirements of the construction and improvement of emergency procurement systems at this stage. During the construction and improvement of the emergency procurement system, through the monitoring and management of the procurement system, standardize the management of emergency procurement contracts, and implement the management of the memorandum of emergency procurement contracts to maximize the benefits of supply and demand of emergency equipment, and meet the requirements of different emergency levels in the future equipment procurement requirements.

Function Mechanism of Intellectual Property Capability on Relay Innovation Based on CWLBGSO-DAG-Bootstrap SEM: Mediating Effect of Knowledge Matching and Moderating Effect of Relationship Learning

Taking unicorn enterprises as the research objects, this study constructs the conceptual model of function mechanism of intellectual property capability on relay innovation performance and introduces knowledge matching as the mediating variable and relationship learning as the moderating variable. Hybrid methods composed of fusion of rough set and binary firefly algorithm based on community weak connection mechanism (CWLBGSO), directed acyclic graph (DAG), and Bootstrap structural equation modeling (Bootstrap SEM) are used to carry out empirical analysis of the conceptual model of function mechanism. The empirical analysis results show that strong intellectual property capability has a positive role in promoting the relay innovation performance of unicorn enterprises, knowledge matching plays the positive mediating role between intellectual property capability and relay innovation, relationship learning plays the moderating role of the relationships between intellectual property capability and relay innovation, relationship learning plays the moderating role of the relationships between intellectual property capability and knowledge matching, and relationship learning plays the moderating role of the relationships between knowledge matching and relay innovation.