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arXiv 2023年

作者： Gao, Ying Wu, Qingqing Chen, Wen Wu, Celimuge Ng, Derrick Wing Kwan Al-Dhahir, Naofal The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai201210 China The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China The Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan The School of Electrical Engineering and Telecommunications University of New South Wales NSW2052 Australia The Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

This paper considers intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a multi-user multiple-input single-output (MISO) interference channel (IFC), where multiple transmitters (Txs) serve their corresponding receivers (Rxs) in a shared spectrum with the aid of IRSs. Our goal is to maximize the sum rate of the Rxs by jointly optimizing the transmit covariance matrices at the Txs, the phase shifts at the IRSs, and the resource allocation subject to the individual energy harvesting (EH) constraints at the Rxs. Towards this goal and based on the well-known power splitting (PS) and time switching (TS) receiver structures, we consider three practical transmission schemes, namely the IRS-aided hybrid TS-PS scheme, the IRS-aided time-division multiple access (TDMA) scheme, and the IRS-aided TDMA-D scheme. The latter two schemes differ in whether the Txs employ deterministic energy signals known to all the Rxs. Despite the non-convexity of the three optimization problems corresponding to the three transmission schemes, we develop computationally efficient algorithms to address them suboptimally, respectively, by capitalizing on the techniques of alternating optimization (AO) and successive convex approximation (SCA). Moreover, we conceive feasibility checking methods for these problems, based on which the initial points for the proposed algorithms are constructed. Simulation results demonstrate that our proposed IRS-aided schemes significantly outperform their counterparts without IRSs in terms of sum rate and maximum EH requirements that can be satisfied under various setups. In addition, the IRS-aided hybrid TS-PS scheme generally achieves the best sum rate performance among the three proposed IRS-aided schemes, and if not, increasing the number of IRS elements can always accomplish it. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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FOF-X: Towards Real-time Detailed Human Reconstruction from a Single Image

arXiv

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arXiv 2024年

作者： Feng, Qiao Liu, Yebin Lai, Yu-Kun Yang, Jingyu Li, Kun College of Intelligence and Computing Tianjin University Tianjin300350 China Department of Automation Tsinghua University Beijing100084 China School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom School of Electrical and Information Engineering Tianjin University Tianjin300072 China

We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image. Balancing real-time speed against high-quality results is a persistent challenge, mainly due to the high computational demands of existing 3D representations. To address this, we propose Fourier Occupancy Field (FOF), an efficient 3D representation by learning the Fourier series. The core of FOF is to factorize a 3D occupancy field into a 2D vector field, retaining topology and spatial relationships within the 3D domain while facilitating compatibility with 2D convolutional neural networks. Such a representation bridges the gap between 3D and 2D domains, enabling the integration of human parametric models as priors and enhancing the reconstruction robustness. Based on FOF, we design a new reconstruction framework, FOF-X, to avoid the performance degradation caused by texture and lighting. This enables our real-time reconstruction system to better handle the domain gap between training images and real images. Additionally, in FOF-X, we enhance the interconversion algorithms between FOF and mesh representations with a Laplacian constraint and an automaton-based discontinuity matcher, improving both quality and robustness. We validate the strengths of our approach on different datasets and real-captured data, where FOF-X achieves new state-of-the-art results. The code will be released for research purposes at https://***/faculty/likun/projects/FOFX/***. © 2024, CC BY.

关键词： Fourier series

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Wind turbine condition monitoring based on intra- and inter-farm federated learning

arXiv

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arXiv 2024年

作者： Grataloup, Albin Jonas, Stefan Meyer, Angela Bern University of Applied Sciences School of Engineering and Computer Science Quellgasse 21 Biel2501 Switzerland Università della Svizzera italiana Faculty of Informatics Via la Santa 1 Viganello Lugano6962 Switzerland Delft University of Technology Department of Geoscience and Remote Sensing Stevinweg 1 Delft2628 Netherlands

As wind energy adoption is growing, ensuring the efficient operation and maintenance of wind turbines becomes essential for maximizing energy production and minimizing costs and downtime. Many AI applications in wind energy, such as in condition monitoring and power forecasting, may benefit from using operational data not only from individual wind turbines but from multiple turbines and multiple wind farms. Collaborative distributed AI which preserves data privacy holds a strong potential for these applications. Federated learning has emerged as a privacy-preserving distributed machine learning approach in this context. We explore federated learning in wind turbine condition monitoring, specifically for fault detection using normal behaviour models. We investigate various federated learning strategies, including collaboration across different wind farms and turbine models, as well as collaboration restricted to the same wind farm and turbine model. Our case study results indicate that federated learning across multiple wind turbines consistently outperforms models trained on a single turbine, especially when training data is scarce. Moreover, the amount of historical data necessary to train an effective model can be significantly reduced by employing a collaborative federated learning strategy. Finally, our findings show that extending the collaboration to multiple wind farms may result in inferior performance compared to restricting learning within a farm, specifically when faced with statistical heterogeneity and imbalanced datasets. © 2024, CC BY-NC-ND.

关键词： Federated learning

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Chaotic American zebra search optimization algorithm for benchmark challenges

Chaotic American zebra search optimization algorithm for ben...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Vikram Kumar Kamboj Ardhala Bala Krishna Chaman Verma Maria Simona Raboaca Constantin Filote Maria Stepanova Schulich School of Engineering University of Calgary Alberta Canada Dept. of Electrcical and Electronics Engineering Malla Reddy Engineering College for Women Hyderabad India Dept. of Media and Edu. Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Engineering Business and Management Faculty Bauman Moscow State Technical University Russia

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges.

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Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search

arXiv

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arXiv 2023年

作者： Hong, Haokai Jiang, Min Yen, Gary G. The Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian361005 China The School of Electrical and Computer Engineering Oklahoma State University United States

The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engineering fields can be modeled as LSMOPs;simultaneously, engineering applications require insensitivity in performance. This requirement usually means that the results from the algorithm runs should not only be good for every run in terms of performance but also that the performance of multiple runs should not fluctuate too much, i.e., the algorithm shows good insensitivity. Considering that substantial computational resources are requested for each run, it is essential to improve upon the performance of the large-scale multiobjective optimization algorithm, as well as the insensitivity of the algorithm. However, existing large-scale multiobjective optimization algorithms solely focus on improving the performance of the algorithms, leaving the insensitivity characteristics unattended. In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems. The proposed method samples the decision variables to construct new nodes on the Monte Carlo tree for optimization and evaluation. It selects nodes with good evaluation for further search to reduce the performance sensitivity caused by large-scale decision variables. We compare the proposed algorithm with several state-of-the-art designs on different benchmark functions. We also propose two metrics to measure the sensitivity of the algorithm. The experimental results confirm the effectiveness and performance insensitivity of the proposed design for solving large-scale multiobjective optimization problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring

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Procedia CIRP 2022年 107卷 131-136页

作者： Mohamed-Ali Tnani Michael Feil Klaus Diepold Department of Factory of the Future Bosch Rexroth AG Lise-Meitner-Str. 4 89081 Ulm Germany Department of Electrical and Computer Engineering Technical University of Munich Arcisstr. 21 80333 Munich Germany Department of Informatics Technical University of Munich Boltzmannstr. 3 85748 Garching Germany

Manufacturing processes have undergone tremendous technological progress in recent decades. To meet the agile philosophy in industry, data-driven algorithms need to handle growing complexity, particularly in computer Numerical Control machining. To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data. The data is collected from a real-world production plant using a smart data collection system over a two-years period. In this work, the edge-to-cloud setup is presented followed by an extensive description of the different normal and abnormal processes. An analysis of the dataset highlights the challenges of machine learning in industry caused by the environmental and industrial factors. The new dataset is published with this paper and available at: https://***/boschresearch/CNC_Machining.

关键词： Smart manufacturing Internet of Things Machine learning Process monitoring Data mining Industry 4.0 Industrial dataset

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On Cyclic Solutions to the Min-Max Latency Multi-Robot Patrolling Problem

arXiv

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arXiv 2022年

作者： Afshani, Peyman De Berg, Mark Buchin, Kevin Gao, Jie Löffler, Maarten Nayyeri, Amir Raichel, Benjamin Sarkar, Rik Wang, Haotian Yang, Hao-Tsung Department Of Computer Science Aarhus University Denmark Department Of Mathematics And Computer Science TU Eindhoven Netherlands Department Of Computer Science TU Dortmund Germany Department Of Computer Science Rutgers University New BrunswickNJ08854 United States Department Of Information And Computing Sciences Utrecht University Netherlands School Of Electrical Engineering And Computer Science Oregon State University OR97330 United States Department Of Computer Science University Of Texas At Dallas RichardsonTX75080 United States School Of Informatics University Of Edinburgh Edinburgh United Kingdom

We consider the following surveillance problem: Given a set P of n sites in a metric space and a set R of k robots with the same maximum speed, compute a patrol schedule of minimum latency for the robots. Here a patrol schedule specifies for each robot an infinite sequence of sites to visit (in the given order) and the latency L of a schedule is the maximum latency of any site, where the latency of a site s is the supremum of the lengths of the time intervals between consecutive visits to s. When k = 1 the problem is equivalent to the travelling salesman problem (TSP) and thus it is NP-hard. For k 2 (which is the version we are interested in) the problem becomes even more challenging;for example, it is not even clear if the decision version of the problem is decidable, in particular in the Euclidean case. We have two main results. We consider cyclic solutions in which the set of sites must be partitioned into groups, for some k, and each group is assigned a subset of the robots that move along the travelling salesman tour of the group at equal distance from each other. Our first main result is that approximating the optimal latency of the class of cyclic solutions can be reduced to approximating the optimal travelling salesman tour on some input, with only a 1 + ϵ factor loss in the approximation factor and an O (k/ϵ)k) factor loss in the runtime, for any ϵ > 0. Our second main result shows that an optimal cyclic solution is a 2(1 - 1/k)-approximation of the overall optimal solution. Note that for k = 2 this implies that an optimal cyclic solution is optimal overall. We conjecture that this is true for k 3 as well. The results have a number of consequences. For the Euclidean version of the problem, for instance, combining our results with known results on Euclidean TSP, yields a PTAS for approximating an optimal cyclic solution, and it yields a (2(1 - 1/k) + ϵ)-approximation of the optimal unrestricted (not necessarily cyclic) solution. If the conjecture mentioned a

关键词： Motion planning

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Retraction note: Blockchain multi-objective optimization approach-enabled secure and cost-efficient scheduling for the Internet of Medical Things (IoMT) in fog-cloud system (Soft Computing, (2022), 26, 13, (6429-6442), 10.1007/s00500-022-07167-9)

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Soft Computing 2024年第2期28卷 921-921页

作者： Lakhan, Abdullah Mohammed, Mazin Abed Elhoseny, Mohamed Alshehri, Mohammad Dahman Abdulkareem, Karrar Hameed Department of Computer Science Dawood University of Engineering and Technology Sindh Karachi74800 Pakistan College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China College of Computer Science and Information Technology University of Anbar Mohamed Elhoseny Anbar31001 Iraq College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura Egypt Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia College of Agriculture Al-Muthanna University Samawah66001 Iraq

The publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher no longer has confidence in the results and conclusions of this article. Mazin Abed Mohammed has stated on behalf of all authors that all authors disagree with this retraction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Ecg Signal Analysis for Heart Disease Detection Using Wavelet Scalogram and Convolutional Neural Networks

SSRN

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SSRN 2023年

作者： El-Dahshan, El-Sayed A. Bassiouni, Mahmoud M. Tan, Ru-San Acharya, Rajendra U. Department of Physics Faculty of Science Ain Shams University CairoPostal Code: 11566 Egypt 33 El-messah Street Eldoki El-GizaPostal Code: 11261 Egypt Department of Cardiology National Heart Centre Singapore Singapore Duke-NUS Medical School Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic 599489 Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Cardiovascular diseases are the leading cause of death. Detecting heart diseases early can prevent heart damage or failure. In this study, we proposed a deep learning model methodology for a four-class classification of normal sinus rhythm, atrial fibrillation, congestive heart failure, and malignant ventricular arrhythmia based on five main phases. In the first phase, the study dataset comprised 294 ECGs recorded from 103 subjects that were collected from five public databases downloaded from PhysioNet. In the second phase, the ECGs were pre-processed to reduce signal noise using Daubechies wavelet transform and filters. In the third phase, the ECGs were then transformed using continuous wavelet transform (CWT) into 2D scalograms with CWT coefficient representations that contain complete time-frequency information of the signal. In the fourth phase, the 2D scalograms were converted into 3D scalograms using RGB, rescaled, and then fed as input to pre-trained convolution neural networks (CNNs)―AlexNet, Vgg16, and Vgg16- gated recurrent unit (GRU)―for fully autonomous transfer learning-based feature extraction. In the fifth phase, the extracted features were input into three different classifiers―CNN-Softmax, CNN-multi-layer perception, and CNN-support vector machine (SVM)―for four-class classification. The model attained excellent overall performance for four-class ECG classification with all three classifiers with >90% accuracy rates on a per-ECG recording basis on all three pre-trained networks. Vgg16-GRU with SVM classifier yielded the best accuracy of 98.3%. The model compared favorably with many recent state-of-the-art ECG classification models. The proposed methodology is able to diagnose several heart arrhythmias with high efficiency in accuracy performance. © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

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Developing call-adzan: A live streaming system for broadcasting the prayer calls

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AIP Conference Proceedings 2024年第1期3140卷

作者： Muh. Ahyar Mardawia M. Parenreng Muh. Fajri Raharjo Hafsah Nirwana Ibrahim Abduh Department of Informatics and Computer Engineering Politeknik Negeri Ujung Pandang Makassar Indonesia Department of Electrical Engineering Politeknik Negeri Ujung Pandang Makassar Indonesia

Loudspeakers or external speakers are frequently utilized to boost the mosque’s sound intensity when broadcasting the call to prayer as a call to prayer five times a day, lectures, religious dialogues, or readings from the holy Qur’an to reach the congregation gathered around it. The utilization of external loudspeakers confers advantages for disseminating information in public settings. Nevertheless, several nations have implemented measures to reduce the use of loudspeakers due to their perceived contribution to air pollution. Similarly, in Indonesia, the Ministry of Religious Affairs released recommendations in 2022 about using loudspeakers in mosques. One of the provisions under these laws is the regulation of sound amplification devices, specifically loudspeakers, utilized within mosques and prayer halls. Islamic principles advocate for the dissemination of religious broadcasting. In addition, it is essential to note that Islam espouses the direction of showing reverence towards our neighbours and discourages the use of loud voices that may disrupt the peace and tranquility of others. Hence, it is imperative to devise a resolution that enables the effective dissemination of the message while upholding the principles of harmonious coexistence and mutual empathy without causing disruptions or compromises to the integrity of neighbour relations. Hence, it is imperative to devise a technology that can effectively disseminate the sound of the mosque’s call to prayer, ensuring direct access for every congregation. This technique enables the real-time transmission of audio data. This study aims to develop and implement a live streaming system for broadcasting the sound of the mosque’s call to prayer. This system will utilize streaming technology, employing a microprocessor connected to an internet-based streaming server. A mobile application will also be developed to listen to real-time streaming of the call to prayer from the mosque. The solution allows local individ

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