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arXiv 2022年

作者： Jorge, Aurelienne A.S. da Silva Diniz, Iuri Freitas, Vander L.S. Costa, Izabelly C. Santos, Leonardo B.L. National Institute for Space Research SP Cachoeira Paulista Brazil Department of Computing Federal University of Ouro Preto Ouro Preto Brazil SP São José dos Campos Brazil Department of Control and Automation Engineering Federal University of Ouro Preto Ouro Preto Brazil

There are several criteria for building up networks from time series related to different points in geographical space. The most used criterion is the Global-Threshold (GT). Using a weather radar dataset, this paper shows that the Backbone (BB) - a local-threshold criterion - generates networks whose geographical configuration is complementary to the GT networks. We compare the results for two well-known similarities measures: the Pearson Correlation (PC) coefficient and the Mutual Information (MI). The extracted backbone network (miBB), whose number of links is the same as the global MI (miGT), has the lowest average shortest path and presents a small-world effect. Regarding the global PC (pcGT) and its corresponding BB network (pcBB), there is a significant linear relationship: R2 = 0.77 with a slope of 1.15 (p-value © 2022, CC BY-NC-SA.

关键词： Watersheds

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GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles

arXiv

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arXiv 2022年

作者： Liu, Jianan Bai, Liping Xia, Yuxuan Huang, Tao Zhu, Bing Han, Qing-Long Vitalent Consulting Gothenburg Sweden The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden College of Science and Engineering James Cook University SmithfieldQLD4878 Australia The School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia

Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the 3rd among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board at the time of submission. Our code is available at https://***/chisyliu/GnnPmbTracker. © 2022, CC BY.

关键词： Automotive industry

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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via Machine Learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： K Nattar Karman V. Velmurugan Kommisetti Murthy Raju T. Sajana V. Vijayalakshmi JoshuvaArockia Dhanraj Departmeru of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala R&D Institute of science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women West Godavari Andhra Pradesh India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective Data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management.

关键词： Costs Mobility models Machine learning Knowledge discovery Scheduling Energy efficiency Concurrent engineering

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Reinforcement Learning for Stochastic LQ Control of Discrete-Time Systems with Multiplicative Noises

arXiv

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arXiv 2023年

作者： Li, Hongdan Li, Lucky Qiaofeng Li, Xun Zhang, Zhaorong College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Qingdao266590 China College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Applied Mathematics The Hong Kong Polytechnic University Kowloon Hong Kong School of Computer Science and Technology Shandong University Qingdao250100 China

This paper considers a stochastic linear quadratic problem for discrete-time systems with multiplicative noises over an infinite horizon. To obtain the optimal solution, we propose an online iterative algorithm of reinforcement learning based on Bellman dynamic programming principle. The algorithm avoids the direct calculation of algebra Riccati equations. It merely takes advantage of state trajectories over a short interval instead of all iterations, significantly simplifying the calculation process. Under the stabilizable initial values, numerical examples shed light on our theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network

arXiv

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arXiv 2022年

作者： Wu, Xiaoshan He, Weihua Yao, Man Zhang, Ziyang Wang, Yaoyuan Li, Guoqi ZJU-UIUC Institute Zhejiang University Zhejiang Haining China Department of Precision Instrument Tsinghua University Beijing China School of Automation Science and Engineering Xi'an Jiaotong University Shaanxi Xi'An China Advanced Computing and Storage Lab Huawei Technologies Co. Ltd Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

Event cameras are considered to have great potential for computer vision and robotics applications because of their high temporal resolution and low power consumption characteristics. However, the event streams output from event cameras have asynchronous, sparse characteristics that cannot be handled by existing computer vision algorithms. Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks. However, direct training of deep SNNs suffers from degradation problems. This work addresses these problems by proposing a spiking nerual network architecture with novel residual block designed and multi-dimension attention modules combined, focusing on the problem of depth prediction. In addition, a novel event stream representation method is proposed specifically for SNNs. This model outperforms previous ANN networks of the same size on the MVSEC dataset and shows great computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Neural networks

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Exploring Metamorphic Testing for Fake-News Detection Software: A Case Study

Exploring Metamorphic Testing for Fake-News Detection Softwa...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Lin Miao Dave Towey Yingrui Ma Tsong Yueh Chen Zhi Quan Zhou Shanghai Branch Jiangsu Automation Research Institute Shanghai People’s Republic of China School of Computer Science University of Nottingham Ningbo China Zhejiang People’s Republic of China Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn VIC Australia School of Computing and Information Technology University of Wollongong Wollongong NSW Australia

Concerns have been growing over fake news and its impact. Software that can automatically detect fake news is becoming more popular. However, the accuracy and reliability of such fake-news detection software remains questionable, partly due to a lack of testing and verification. Testing this kind of software may face the oracle problem, which refers to difficulty (or inability) of identifying the correctness of the software’s output in a reasonable amount of time. Metamorphic testing (MT) has a record of effectively alleviating the oracle problem, and has been successfully applied to testing fake-news detection software. This paper reports on a study, extending previous work, exploring the use of MT for fake-news detection software. The study includes new metamorphic relations and additional experimental results and analysis. Some alternative MR-generation approaches are also explored. The study targets software where the output is a real/fake news decision, enhancing the applicability of MT to current fake-news detection software. The paper also explores the impact of the prediction accuracy of the fake-news detection software on the MT process. The study demonstrates the validity and applicability of MT to fake-news detection software. The prediction accuracy of the software has a greater impact on MT experiments with greater changes between the source and follow-up inputs, and less dependence on prediction stability. Some possible factors affecting the experimental results are discussed, and directions for future work are provided.

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A biosensing system employing nanowell microelectrode arrays to record the intracellular potential of a single cardiomyocyte

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Microsystems & Nanoengineering 2022年第3期8卷 185-198页

作者： Yuting Xiang Haitao Liu Wenjian Yang Zhongyuan xu Yue Wu Zhajian Tang Zhjing Zhu Zhiyong Zeng Depeng Wang Tianxing Wang Ning Hu Diming Zhang Department of Obstetrics and Gynecology Affiliated Dongguan People's HospitalSouthern Medical UniversityDongguan 523058China Research Center for Inelligent Sensing Systems Zhejiang LaboratoryHangzhou 311100China Research Center for Humanoid Sensing Zhejiang LaboratoryHangzhou 311100China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejiang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China School of Automation Nanjing University of Science and TechnologyNanjing 210094China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-Link Care Meditech Co. LtdHangzhou 310011China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemitryZhejiang UniversityHangzhou 310058China

Electrophysiological recording is a widely used method to investigate cardiovascular pathology,pharmacology and developmental *** arrays record the electrical potential of cells in a minimally invasive and highthroughput ***,commonly used microelectrode arays primarily employ planar microelectrodes and cannot work in applications that require a recording of the intracellular action potential of a single *** this study,we proposed a novel measuring method that is able to record the intracellular action potential of a single cardiomyocyte by using a nanowell patterned microelectrode array(NWMEA).The NWMEA consists of five nanoscale wells at the center of each circular planar *** pulse electroporation was applied to the NWMEA to penetrate the cardiomyocyte membrane,and the intracellular action potential was continuously *** intracellular potential recording of cardiomyocytes by the NWMEA measured a potential signal with a higher quality(213.76±25.85%),reduced noise root-mean-square(~33%),and higher signal-to-noise ratio(254.36±12.61%)when compared to those of the extracellular *** to previously reported nanopillar microelectrodes,the NWMEA could ensure single cell electroporation and acquire high-quality action potential of cardiomyocytes with reduced fabrication *** NWMEA-based biosensing system is a promising tool to record the intracellular action potential of a single cell to broaden the usage of microelectrode arrays in electrophysiological investigation.

关键词： arrays potential record

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Efficient Federated Learning in 6G-Satellite Systems: Deep Reinforcement Learning Based Multi-Objective Optimization

Efficient Federated Learning in 6G-Satellite Systems: Deep R...

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IEEE Conference on Wireless Communications and Networking

作者： Yu Zhou Jingjing Guo Haohui Li Jinjin Tian Xiaohui Zhao Lei Lei School of Information and Communications Engineering Xi'an Jiaotong University Xi'an P. R. China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR P. R. China School of Automation Central South University Changsha P. R. China National Mobile Communications Research Laboratory Southeast University Nanjing P. R. China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Wireless-based federated learning (FL), as an emerging distributed learning approach, has been widely studied for 6G systems. When the paradigm shifts from terrestrial to non-terrestrial networks (NTN), FL may need to address several open challengings, e.g., limited service time of low earth orbit (LEO) satellites and time-efficient uploading and aggregation for massive devices. In this work, we exploit the synergy of LEO and FL for future integrated 6G-satellite systems by taking advantage of ubiquitous wireless access provided by LEO and appealing characteristics of collaborative training and data privacy preservation in FL. The studied LEO-FL framework may need to improve multi-metric performance in practice. Different from most FL works, we simultaneously improve the communication-training efficiency and local training accuracy from a multi-objective optimization (MOO) perspective. To solve the problem, we propose a decomposition, deep reinforcement learning and transfer learning based MOO algorithm for FL (DRT-FL), aiming at adapting to the dynamic satellite-terrestrial environments, achieving efficient uploading and aggregation, and approaching Pareto optimal sets. Compared to the state-of-the-art MOO algorithms, the effectiveness of the proposed LEO-FL framework and DRT-FL algorithm are assessed on MNIST and CIFAR-IO datasets.

关键词： 6G mobile communication Training Satellites Federated learning Heuristic algorithms Transfer learning Low earth orbit satellites

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Task Scheduling Mechanism for Crowdsourcing in Mobile Social Networks

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IEEE Transactions on Mobile computing 2025年

作者： Zhang, Jiale Yi, Longhao Gao, Xiaofeng Bhatti, Shahzad Sarwar Yuan, Ting Chen, Guihai Shanghai Jiao Tong University Department of Computer Science and Engineering China Emerson University Faculty of Computing and Emerging Technologies Multan Pakistan Shanghai Jiao Tong University Department of Automation China

With the popularization of smart phones, mobile crowdsourcing emerged and gained growing attention in the recent years. Mobile users are now able to conduct complex tasks with the communication between each other. In this paper, we study the task scheduling problem in the mobile crowdsourcing systems based on the spontaneously formed mobile social networks (MSNs). We introduce two crowdsourcing task scheduling problems under this system model, with one problem aiming to minimize the total cost of some crowdsourcing tasks and the other focusing on minimizing the final completion time of the tasks belonging to the same project. We introduce a unified framework to solve the problems and propose two approximation algorithms for these two problems in the offline versions respectively and prove Their approximation ratios accordingly. Based on the two algorithms, we further design two online algorithms to deal with the workers' dynamism and also analyze their competitive ratios. Finally, we verify the effectiveness and efficiency of the proposed methods through numerical experiments on real and synthetic datasets. © 2002-2012 IEEE.

关键词： Approximation algorithms

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Simulations of the Performance of Maximum Power Point Tracking Algorithms Based on Experimental Data According to the Topologies of DC-DC Converters

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Journal of Power and Energy engineering 2021年第5期9卷 76-92页

作者： Abraham Dandoussou Pierre Kenfack Stève Ngoffe Perabi Martin Kamta University of Buea Department of Electrical and Power Engineering Higher Technical Teachers’ Training College (HTTTC) Kumba Cameroon University of Douala Department of Electrical Engineering and Industrial Computing Institut Universitaire de Technologie Douala Cameroon University of Ngaoundere Department of Electrical Engineering Energetic and Automation National School of Agro-Industrial Sci-ences Ngaoundere Cameroon

Maximum Power Point Tracking (MPPT) algorithms are now widely used in PV systems independently of the weather conditions. In function of the application, a DC-DC converter topology is chosen without any previous performance test under normal weather conditions. This paper proposes an experimental evaluation of MPPT algorithms according to DC-DC converters topologies, under normal operation conditions. Four widely used MPPT algorithms i.e. Perturb and Observe (P & O), Hill Climbing (HC), Fixed step Increment of Conductance (INCF) and Variable step Increment of Conductance (INCV) are implemented using two topologies of DC-DC converters i.e. buck and boost converters. As input variables to the PV systems, recorded irradiance and temperature, and extracted photovoltaic parameters (ideality factor, series resistance and reverse saturation current) were used. The obtained results show that buck converter has a lot of power losses when controlled by each of the four MPPT algorithms. Meanwhile, boost converter presents a stable output power during the whole day. Once more, the results show that INCV algorithm has the best performance.

关键词： MPPT Algorithms DC-DC Converters Photovoltaic Parameters Normal Operating Conditions

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