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arXiv 2024年

作者： Zhang, Qi Wang, Huamin Shen, Hangchi Duan, Shukai Wen, Shiping Huang, Tingwen College of Artificial Intelligence Southwest University Chongqing400715 China Chongqing Key Laboratory of Brain Inspired Computing and Intelligent Chips Chongqing400715 China College of Information Engineering University of Technology Sydney Australian Institute of Artificial Intelligence Sydney2007 Australia Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Recently, it can be noticed that most models based on spiking neural networks (SNNs) only use a same level temporal resolution to deal with speech classification problems, which makes these models cannot learn the information of input data at different temporal scales. Additionally, owing to the different time lengths of the data before and after the sub-modules of many models, the effective residual connections cannot be applied to optimize the training processes of these models. To solve these problems, on the one hand, we reconstruct the temporal dimension of the audio spectrum to propose a novel method named as Temporal Reconstruction (TR) by referring the hierarchical processing process of the human brain for understanding speech. Then, the reconstructed SNN model with TR can learn the information of input data at different temporal scales and model more comprehensive semantic information from audio data because it enables the networks to learn the information of input data at different temporal resolutions. On the other hand, we propose the Non-Aligned Residual (NAR) method by analyzing the audio data, which allows the residual connection can be used in two audio data with different time lengths. We have conducted plentiful experiments on the Spiking Speech Commands (SSC), the Spiking Heidelberg Digits (SHD), and the Google Speech Commands v0.02 (GSC) datasets. According to the experiment results, we have achieved the state-of-the-art (SOTA) result 81.02% on SSC for the test classification accuracy of all SNN models, and we have obtained the SOTA result 96.04% on SHD for the classification accuracy of all models. Furthermore, on the non-spiking dataset GSC, we can achieve a test classification accuracy of 95.63%, which surpasses the original baseline SNN-Delays. Meanwhile, the method proposed in this paper can speed up the model’s processing speed and reduce its energy consumption. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

SSRN

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SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Filtering of Discrete Linear Repetitive Processes with H and l2-l Performance

Filtering of Discrete Linear Repetitive Processes with H and...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Ligang Wu James Lam Wojciech Paszke Krzysztof Galkowski Eric Rogers Anton Kummert Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin P. R. China The University of Hong Kong Hong Kong Hong Kong China Institute of Control and Computation Engineering University of Zielona Góra Góra Poland School of Electronics and Computer Science University of Southampton Southampton U.K Faculty of Electrical Information and Media Engineering Communication Theory University of Wuppertal Germany

This paper considers the general filtering problem for a distinct class of two-dimensional (2-D) discrete linear systems, i.e. information propagation in two independent directions, known as discrete linear repetitive processes which are of both system-theoretic and applications interest. In particular, new results on the design of filters with guaranteed levels of performance are developed. These take the form of algorithms for the design of an H infin and l 2 -l infin dynamic output feedback filter which guarantees that the resulting filtering error process is stable and has prescribed disturbance attenuation performance as measured by H infin and l 2 -l infin norms.

关键词： Nonlinear filters Filtering Symmetric matrices Space technology Communication system control Attenuation measurement Algorithm design and analysis Iterative algorithms Optimal control Stability

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Towards an inclusive Parkinson's screening system

Towards an inclusive Parkinson's screening system

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Oana Geman Saeid Sanei Iuliana Chiuchisan Adrian Graur Aleš Procházka Oldřich Vyšata Department of Health and Human Development “Stefan eel Mare” University of Suceava Romania Department of Computer Science Electronics and Automation “Stefan cel Mare” University of Suceava Romania Faculty of Engineering and Physical Sciences University of Surrey United Kingdom Department of Computing and Control Engineering Institute of Chemical Technology Czech Republic Department of Neurology Charles University Czech Republic

In this study, brain, and gait dynamic information were combined and used for diagnosis and monitoring of Parkinson's disease (the most important Neurodegenerative Disorder). Analysis of the information corresponding to a prescribed movement involving tremor, and the related changes in brain connectivity is novel and original. Analytically, developing a space-time nonlinear adaptive system which fuses brain and gait information algorithmically is proposed here for the first time. The overall dynamic system will be constrained by the clinical impressions of the patient symptoms embedded in a knowledge-based system. The entire complex constrained problem were solved to enable a powerful model for recognition and monitoring of Parkinson's disease and establishing appropriate rules for its clinical following up.

关键词： Brain modeling Adaptation models Parkinson's disease Chaos Signal processing algorithms Nonlinear dynamical systems

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Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

arXiv

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

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Machine-Learned Models for Power Magnetic Material Characteristics

arXiv

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arXiv 2023年

作者： Leszczyński, Pawel Kutorasiński, Kamil Szewczyk, Marcin Pawlowski, Jaroslaw Division of Power Apparatus Protection and Control Faculty of Electrical Engineering Electrical Power Engineering Institute Warsaw University of Technology Koszykowa 75 St. Warszawa00-662 Poland Department of Condensed Matter Physics Faculty of Physics and Applied Computer Science AGH University of Krakow Reymonta 19 St. Kraków30-059 Poland Institute of Theoretical Physics Wroclaw University of Science and Technology Wyb. Wyspiańskiego 27 St. Wroclaw50-370 Poland

We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics (that mimic measurements) are used to train the neural autoencoder model in an unsupervised manner. The encoder is trying to predict the material parameters of a theoretical model, which is then used in a decoder part. The decoder, using the predicted parameters, reconstructs the input characteristics. The neural model is trained to capture a synthetically generated set of characteristics that can cover a broad range of material behaviors, leading to a model that can generalize on the underlying physics rather than just optimize the model parameters for a single measurement. After setting up the model, we prove its usefulness in the complex problem of modeling magnetic materials in the frequency and current (out-of-linear range) domains simultaneously, for which we use measured characteristics obtained for frequency up to 10 MHz and H-field up to saturation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics

引用

Nature communications 2025年第1期16卷 4900页

作者： Dhammika Leshan Wannigama Mohan Amarasiri Phatthranit Phattharapornjaroen Cameron Hurst Charin Modchang John Jefferson V Besa Kazuhiko Miyanaga Longzhu Cui Stefan Fernandez Angkana T Huang Puey Ounjai W K C P Werawatte Ali Hosseini Rad S M Porames Vatanaprasan Dylan John Jay Thammakorn Saethang Sirirat Luk-In Phitsanuruk Kanthawee Wanwara Thuptimdang Ratana Tacharoenmuang Bernadina Cynthia S P H Spencer Vitharana Natharin Ngamwongsatit Hitoshi Ishikawa Takashi Furukawa Yangzhong Wang Andrew C Singer Naveen Kumar Devanga Ragupathi Tanittha Chatsuwan Kazunari Sei Asuka Nanbo Asada Leelahavanichkul Talerngsak Kanjanabuch Hiroshi Hamamoto Paul G Higgins Daisuke Sano Anthony Kicic José O Valdebenito Jonas Bonnedahl Sam Trowsdale Parichart Hongsing Aisha Khatib Kenji Shibuya Shuichi Abe Department of Infectious Diseases Faculty of Medicine Yamagata University Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Pathogen Hunter's Research Collaborative Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Yamagata Prefectural University of Health Sciences Kamiyanagi Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. The Lygodium Ceylon Health and Environmental Policy Research Center Colombo Sri Lanka. leshanwannigama@med.id.yamagata-u.ac.jp. Biofilms and Antimicrobial Resistance Consortium of ODA receiving countries The University of Sheffield Sheffield UK. leshanwannigama@med.id.yamagata-u.ac.jp. Pathogen Hunter's Research Collaborative Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. Department of Civil and Environmental Engineering Graduate School of Engineering Tohoku University Sendai Miyagi Japan. Faculty of Health Science Technology Chulabhorn Royal Academy Bangkok Thailand. HRH Princess Chulabhorn Disaster and Emergency Medicine Center Chulabhorn Royal Academy Bangkok Thailand. Department of Clinical Epidemiology Faculty of Medicine Thammasat University Rangsit Thailand. Center of Excellence in Applied Epidemiology Thammasat University Rangsit Thailand. Mater Research Institute University of Queensland Brisbane QLD Australia. Biophysics Group Department of Physics Faculty of Science Mahidol University Bangkok Thailand. Centre of Excellence in Mathematics MHESI Bangkok Thailand. Thailand Center of Excellence in Physics Ministry of Higher Education Science Research and Innovation Bangkok Thailand. Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. Co

Avian influenza viruses (AIVs) pose a growing global health threat, particularly in low- and middle-income countries (LMICs), where limited surveillance capacity and under-resourced healthcare systems hinder timely detection and response. Migratory birds play a significant role in the transboundary spread of AIVs, yet data from key regions along migratory flyways remain sparse. To address these surveillance gaps, we conducted a study between December 2021 and February 2023 using fresh bird guano collected across 10 countries in the Global South. Here, we show that remote, uninhabited regions in previously unsampled areas harbor a high diversity of AIV strains, with H5N1 emerging as the most prevalent. Some of these H5N1 samples also carry mutations that may make them less responsive to the antiviral drug oseltamivir. Our findings documented the presence of AIVs in several underrepresented regions and highlighted critical transmission hotspots where viral evolution may be accelerating. These results underscore the urgent need for geographically targeted surveillance to detect emerging variants, inform public health interventions, and reduce the risk of zoonotic spillover.

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

引用

arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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H∞ Filtering for a class of 2D systems

H∞ Filtering for a class of 2D systems

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European Signal Processing Conference (EUSIPCO)

作者： Ligang Wu James Lam Wojciech Paszke Krzysztof Gałkowski Eric Rogers Anton Kummert Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin P. R. China Department of Mechanical Engineering The University of Hong Kong Hong Kong Institute of Control and Computation Engineering University of Zielona Góra 65–246 Zielona Góra Poland Uniwersytet Zielonogorski Zielona Gora Lubuskie PL Department of Electronics and Computer Science University of Southampton Southampton U.K. Information and Media Engineering University of Wuppertal Faculty of Electrical

The unique characteristic of a repetitive process is a series of sweeps or passes through a set of dynamics defined over a finite duration known as the pass length. At the end of each pass, the process is reset and the next time through the output, or pass profile, produced on the previous pass acts as a forcing function on, an hence contributes to, the dynamics of the new pass profile. They are hence a class of systems with information propagation in two independent directions where here the dynamics over the finite pass length are described by a matrix linear differential equation and that from pass to pass has a discrete updating structure. This means that filtering/estimation theory/algorithms for, in particular, 2D discrete linear systems is not applicable. In this paper we solve a general filtering problem with a view toward use in the many applications where such action will be required.

关键词： Stability analysis Asymptotic stability Process control Signal processing Linear systems Filtering State-space methods

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Feature selection in linear Support Vector Machines via a hard cardinality constraint: a scalable conic decomposition approach

arXiv

引用

arXiv 2024年

作者： Bomze, Immanuel D’Onofrio, Federico Palagi, Laura Peng, Bo Faculty of Mathematics and Research Network Data Science University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy VGSCO University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria

In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to an interpretable classification model. The problem is NP-hard due to the presence of the cardinality constraint, even though the original linear SVM amounts to a problem solvable in polynomial time. To handle the hard problem, we first introduce two mixed-integer formulations for which novel semidefinite relaxations are proposed. Exploiting the sparsity pattern of the relaxations, we decompose the problems and obtain equivalent relaxations in a much smaller cone, making the conic approaches scalable. To make the best usage of the decomposed relaxations, we propose heuristics using the information of its optimal solution. Moreover, an exact procedure is proposed by solving a sequence of mixed-integer decomposed semidefinite optimization problems. Numerical results on classical benchmarking datasets are reported, showing the efficiency and effectiveness of our *** Codes 90C22, 90C11 © 2024, CC BY-NC-ND.

关键词： Mixed-integer linear programming

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