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11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Tu, Geng Li, Qingyuan Jiang, Dazhi Department of Computer Science Shantou University Shantou515063 China Key Laboratory of Intelligent Manufacturing Technology Ministry of Education Shantou University Shantou100091 China Electrical Engineering and the Automation China University of Mining and Technology Beijing China

ISBN: (纸本)9789811555763

In recent years, gesture recognition that has the extensive application future is gradually the hotspot of the human-computer interaction. With the emergence of depth sensors such as Leap Motion, more depth information can be obtained, which provides a reliable data guarantee for recognizing complex gestures and better application scenarios. In this paper, five common dynamic gestures are defined firstly. Then, Leap Motion sensor is used to acquire the feature data of the hand. After comparing the recognition effects of HMM and DTW algorithms, a hybrid HMM-DTW model is proposed for dynamic gesture recognition. According to Kappa coefficient, it can be noticed that the model is substantial. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Gesture recognition

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Channel pruning via automatic structure search 29

Channel pruning via automatic structure search

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Lin, Mingbao Ji, Rongrong Zhang, Yuxin Zhang, Baochang Wu, Yongjian Tian, Yonghong Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University China School of Automation Science and Electrical Engineering Beihang University China Co. Ltd China School of Electronics Engineering and Computer Science Peking University Beijing China

ISBN: (纸本)9780999241165

Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did. To solve the intractably huge combinations of pruned structure for deep networks, we first propose to shrink the combinations where the preserved channels are limited to a specific space, thus the combinations of pruned structure can be significantly reduced. And then, we formulate the search of optimal pruned structure as an optimization problem and integrate the ABC algorithm to solve it in an automatic manner to lessen human interference. ABCPruner has been demonstrated to be more effective, which also enables the fine-tuning to be conducted efficiently in an end-to-end manner. The source codes can be available at https://***/lmbxmu/ABCPruner. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep neural networks

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Realtime bacteria detection and analysis in sterile liquid products using deep learning holographic imaging

npj Biosensing

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npj Biosensing 2024年第1期1卷 1-8页

作者： Nicholas Bravo-Frank Jiarong Hong Rushikesh Zende Lei Feng Nicolas Mesyngier Aditya Pachpute Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA Saint Anthony Falls Laboratory University of Minnesota Minneapolis MN USA Department of Mechanical Engineering University of Minnesota Minneapolis MN USA College of Science and Engineering Minnesota Robotics Institute Minneapolis MN USA

We introduce a digital inline holography (DIH) method combined with deep learning (DL) for real-time detection and analysis of bacteria in liquid suspension. Specifically, we designed a prototype that integrates DIH with fluorescence imaging to efficiently capture holograms of bacteria flowing in a microfluidic channel, utilizing the fluorescent signal to manually identify ground truths for validation. We process holograms using a tailored DL framework that includes preprocessing, detection, and classification stages involving three specific DL models trained on an extensive dataset that included holograms of generic particles present in sterile liquid and five bacterial species featuring distinct morphologies, Gram stain attributes, and viability. Our approach, validated through experiments with synthetic data and sterile liquid spiked with different bacteria, accurately distinguishes between bacteria and particles, live and dead bacteria, and Gram-positive and negative bacteria of similar morphology, all while minimizing false positives. The study highlights the potential of combining DIH with DL as a transformative tool for rapid bacterial analysis in clinical and industrial settings, with potential extension to other applications including pharmaceutical screening, environmental monitoring, and disease diagnostics.

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Using Fuzzy Cognitive Maps in Analyzing and Studying International Economic and Political Stability

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IFAC-PapersOnLine 2019年第25期52卷 23-28页

作者： Peter P. Groumpos Emeritus Professor Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rion 26504

Challenging Issues of International Economic and Political Stability is considered. Various models describing them are briefly presented and their strong and weak points are considered. Today’s world problem cannot be analysed and understood with present mathematical models. The approach of Fuzzy Cognitive Maps (FCM) is used to model the economic behaviour of a country. A new model is proposed for international affairs using FCM. Two cases are considered, and simulations are conducted. Obtained results are presented and discussed. Future research directions are presented.

关键词： International Stability Fuzzy logic Fuzzy Cognitive Maps Sustainable growth

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Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yang Wei, Xinhao Li, Yuanzheng Dong, Zhaoyang Shahidehpour, Mohammad The Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China The School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The Electrical and Computer Engineering Department Illinois Institute of Technology ChicagoIL60616 United States The ECE Department The King Abdulaziz University Saudi Arabia

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified. Copyright © 2022, The Authors. All rights reserved.

关键词： Smart power grids

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Evaluation of Robustness Metrics for Defense of Machine Learning Systems

Evaluation of Robustness Metrics for Defense of Machine Lear...

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International Conference on Military Communications and Information Systems (ICMCIS)

作者： J. DeMarchi R. Rijken J. Melrose B. Madahar G. Fumera F. Roli E. Ledda M. Aktaş F. Kurth P. Baggenstoss B. Pelzer L. Kanestad Collaborative Engineering Systems & Aerospace Systems Information Supremacy Royal Netherlands Aerospace Centre NLR Amsterdam NLD Cyber & Information Systems Division Defence Science and Technology Laboratory Portondown GBR Department of Electrical and Electronic Engineering University of Cagliari Cagliari ITA Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa ITA Department of Computer Control and Management Engineering Sapienza University of Rome Rome ITA Defence Systems Technologies Division ASELSAN Ankara TUR Fraunhofer Institute for Communication Information Processing and Ergonomics Bonn DEU Swedish Defence Research Agency Cyber Defence and C2 Technology Division Stockholm SWE

In this paper we explore some of the potential applications of robustness criteria for machine learning (ML) systems by way of tangible “demonstrator” scenarios. In each demonstrator, ML robustness metrics are applied to real-world scenarios with military relevance, indicating how they might be used to help detect and handle possible adversarial attacks on ML systems. We conclude by sketching promising future avenues of research in order to: (1) help establish useful verification methodologies to facilitate ML robustness compliance assessment; (2) support development of ML accountability mechanisms; and (3) reliably detect, repel, and mitigate adversarial attack.

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Multi-modal Intent Recognition Method for the Soft Hand Rehabilitation Exoskeleton

Multi-modal Intent Recognition Method for the Soft Hand Reha...

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第三十九届中国控制会议

作者： Wenyuan Chen Peng Yu Guangyong Li Wenxue Wang Chen Yao Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering Swanson School of Engineering University of Pittsburgh

Stroke has become the second most disabling disease in the *** to the intensive demand for physical therapists and the severe dependence on hospitals,the cost for the treatment of stroke patients is *** the most flexible limb of the human body,the hand faces more severe challenges,which has a much lower degree of recovery than the upper and lower *** the face of these challenges,a new treatment,exoskeleton-based rehabilitation,has demonstrated new *** paper proposes a novel design of the soft hand exoskeleton based on bionics and anatomy and the exoskeleton could help the users bend and extend their fingers,which would greatly improve the motor ability of stroke *** the control of the six drive motors,the exoskeleton could achieve most of the hand's freedom of *** the same time,we propose a multi-modal intent recognition method based on machine vision and machine *** specific rehabilitation training scenarios,both healthy subjects and patients could complete grasping tasks in the wearing of the exoskeleton,overcoming potential security risks caused by misidentification due to using the single-modal intent understanding method.

关键词： soft hand exoskeleton bionics anatomy Conference muti-modal intention recognition method

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Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autononous Driving

arXiv

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arXiv 2023年

作者： Zhang, Xinyu Wang, Li Chen, Jian Fang, Cheng Yang, Lei Song, Ziying Yang, Guangqi Wang, Yichen Zhang, Xiaofei Li, Jun Li, Zhiwei Yang, Qingshan Zhang, Zhenlin Ge, Shuzhi Sam State Key Laboratory of Automotive Safety and Energy the School of Vehicle and Mobility Ts-inghua University Beijing100084 China School of Mechanical and Electrical Engineering China University of Mining and Technology-Beijing Beijing100083 China School of Artificial Intelligence China University of Mining and Technology-Beijing Beijing100083 China College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China Beijing Jingwei Hirain Technologies Co. Inc. Beijing100191 China China Automotive Innovation Cooperation Nanjing211113 China Department of Electrical and Computer Engineering Interactive Digital Media Institute Social Robotics Laboratory National University of Singapore Singapore

Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D radars, latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception. However, due to the much higher noise than LiDAR, manufacturers choose different filtering strategies, resulting in a direct ratio between point cloud density and noise level. There is still a lack of comparative analysis on which method is beneficial for deep learning-based perception algorithms in autonomous driving. One of the main reasons is that current datasets only adopt one type of 4D radar, making it difficult to compare different 4D radars in the same scene. Therefore, in this paper, we introduce a novel large-scale multi-modal dataset featuring, for the first time, two types of 4D radars captured simultaneously. This dataset enables further research into effective 4D radar perception algorithms. Our dataset consists of 151 consecutive series, most of which last 20 seconds and contain 10,007 meticulously synchronized and annotated frames. Moreover, our dataset captures a variety of challenging driving scenarios, including many road conditions, weather conditions, nighttime and daytime with different lighting intensities and periods. Our dataset annotates consecutive frames, which can be applied to 3D object detection and tracking, and also supports the study of multi-modal tasks. We experimentally validate our dataset, providing valuable results for studying different types of 4D radars. This dataset is released on https://***/adeptthu/Dual-Radar. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Roadmap on Neuromorphic Photonics

arXiv

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arXiv 2025年

作者： Brunner, Daniel Shastri, Bhavin J. Al-Qadasi, Mohammed A. Ballani, H. Barbay, Sylvain Biasi, Stefano Bienstman, Peter Bilodeau, Simon Bogaerts, Wim Böhm, Fabian Brennan, G. Buckley, Sonia Cai, Xinlun Strinati, Marcello Calvanese Canakci, B. Charbonnier, Benoit Chemnitz, Mario Chen, Yitong Cheung, Stanley Chiles, Jeff Choi, Suyeon Christodoulides, Demetrios N. Chrostowski, Lukas Chu, J. Clegg, J.H. Cletheroe, D. Conti, Claudio Dai, Qionghai Di Lauro, Luigi Diamantopoulos, Nikolaos-Panteleimon Dinc, Niyazi Ulas Ewaniuk, Jacob Fan, Shanhui Fang, Lu Franchi, Riccardo Freire, Pedro Gentilini, Silvia Gigan, Sylvain Giorgi, Gian Luca Gkantsidis, C. Gladrow, J. Goi, Elena Goldmann, M. Grabulosa, A. Gu, Min Guo, Xianxin Hejda, Matěj Horst, F. Hsieh, Jih-Liang Hu, Jianqi Hu, Juejun Huang, Chaoran Hurtado, Antonio Jaurigue, Lina Kalinin, K.P. Kamalian-Kopae, Morteza Kelly, D.J. Khajavikhan, Mercedeh Kremer, H. Laydevant, Jeremie Lederman, Joshua C. Lee, Jongheon Lenstra, Daan Li, Gordon H.Y. Li, Mo Li, Yuhang Lin, Xing Lin, Zhongjin Lis, Mieszko Lüdge, Kathy Lugnan, Alessio Lupo, Alessandro Lvovsky, A.I. Manuylovich, Egor Marandi, Alireza Marchesin, Federico Massar, Serge McCaughan, Adam N. McMahon, Peter L. Moralis-Pegios, Miltiadis Morandotti, Roberto Moser, Christophe Moss, David J. Mukherjee, Avilash Nikdast, Mahdi Offrein, B.J. Oguz, Ilker Oripov, Bakhrom O'Shea, G. Ozcan, Aydogan Parmigiani, F. Pasricha, Sudeep Pavanello, Fabio Pavesi, Lorenzo Peserico, Nicola Pickup, L. Pierangeli, Davide Pleros, Nikos Porte, Xavier Primavera, Bryce A. Prucnal, Paul Psaltis, Demetri Puts, Lukas Qiao, Fei Rahmani, B. Raineri, Fabrice Ríos Ocampo, Carlos A. Robertson, Joshua Romeira, Bruno Roques-Carmes, Charles Rotenberg, Nir Rowstron, A. Schoenhardt, Steffen Schwartz, Russell L.T. Shainline, Jeffrey M. Shekhar, Sudip Skalli, A. Sohoni, Mandar M. Sorger, Volker J. Soriano, Miguel C. Spall, James Stabile, Ripalta Stiller, Birgit Sunada, Satoshi Tefas, Anastasios Tossoun, Bassem Tsakyridis, Apostolos Turitsyn, Sergei K. Van der Sande, G Université Marie et Louis Pasteur CNRS UMR 6174 Institut FEMTO-ST Besançon25000 France Centre for Nanophotonics Department of Physics Engineering Physics & Astronomy Queen's University Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Microsoft Research Cambridge United Kingdom Université Paris-Saclay CNRS Centre de Nanosciences et de Nanotechnologies France Nanoscience Laboratory Department of Physics University of Trento Italy Photonics Research Group Department of Information Technology Ghent University imec Belgium Princeton University NJ United States Hewlett Packard Labs Hewlett Packard Enterprise Böblingen Germany National Institute of Standards and Technology BoulderCO United States State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University China Enrico Fermi Research Center Rome Italy Université Grenoble-Alpes CEA Leti Grenoble France Leibniz-Institute of Photonic Technology Jena Germany Institute of Applied Optics and Biophysics Jena Germany Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering North Carolina State University NC United States Department of Electrical Engineering Stanford University CA United States University of Southern California Los AngelesCA United States Department of Physics Sapienza University Rome Italy Institute for Complex Systems National Research Council Rome Italy Varennes Canada NTT Device Technology Labs NTT Corporation Kanagawa Atsugi Japan Institute of Electrical and Microengineering School of Engineering École Polytechnique Fédérale de Lausanne Switzerland Edward L. Ginzton Laboratory Stanford University StanfordCA United States Department of Electronic Engineering Tsinghua University China Beijing National Research Center for Information Science and Technology Tsinghua University China Aston Univ

Neuromorphic photonics are processors inspired by the human brain and enabled by light (photons) instead of traditional electronics. Neuromorphic photonics and its associated concepts are experiencing a significant resurgence, building on foundational research from the 1980s and 1990s. This renewed momentum is driven by breakthroughs in photonic integration, nonlinear optics, and advanced materials, alongside the growing necessity of neuro-inspired computing in numerous applications of economic and societal relevance. The increasing demand for energy-efficient artificial intelligence (AI) solutions underscores the need for innovation and a cohesive vision to address key challenges, including scalability, energy efficiency, precision, and standardized performance benchmarks. Together, these efforts present an opportunity to establish a unique photonic advantage with practical, real-world applications. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. The roadmap introduces various approaches to embedding the high-complexity transformations central to neuromorphic computing, focusing on frequency, delay, and spectral embeddings. This is followed by a discussion of architectures of photonic neural networks (PNNs) and an in-depth analysis of methods for implementing these architectures in photonic hardware. Dedicated sections delve into integrated photonic hardware, the realization of photonic weights and memories, and the optimization of training processes for photonic neuromorphic architectures. The roadmap concludes by exploring numerous potential applications, highlighting the challenges and advances necessary to transition neuromorphic photonic computing from a primarily academic pursuit to a technolog

关键词： Philosophical aspects

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Background Flow Sensing for Autonomous Underwater Vehicles Using Model Reduction with Dynamic Mode Decomposition

Background Flow Sensing for Autonomous Underwater Vehicles U...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Fengying Dang Sanjida Nasreen Feitian Zhang Bioinspired Robotics and Intelligent Control Laboratory at the Department of Electrical and Computer Engineering George Mason University Fairfax VA USA

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Autonomous underwater vehicles (AUVs) have received increasing attention among scientific and engineering societies for their superior performances and promising future in marine environments. A key challenge to marine autonomy is the sensing of the background flow environment, which is important for navigation and motion control of AUVs. While existing flow estimation studies typically focus on (quasi-)steady flow fields for AUVs of well-defined shapes, this paper proposes a novel flow sensing algorithm that applies to any dynamic flows for arbitrary AUV shapes. The proposed flow sensing method assimilates distributed pressure measurements through coalescing recursive Bayesian estimation and dynamic mode decomposition (DMD)-based model reduction. To demonstrate the effectiveness of the proposed distributed flow sensing algorithm, both simulation and experimental results are presented on three testing prototypes of different shapes including a Joukowski foil, a triangle, and a rectangle.

关键词： Sensors Pressure measurement Estimation Heuristic algorithms Reduced order systems Vehicle dynamics Bayes methods

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