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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Automatic-SSD: Full Hardware Automation over New Memory for High Performance and Energy Efficient PCIe Storage Cards

Automatic-SSD: Full Hardware Automation over New Memory for ...

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IEEE International Conference on computer-Aided Design

作者： Gyuyoung Park Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology Daejeon Korea

ISBN: (数字)9781665423243

We propose Automatic-SSD that converts all storage management logic into hardware, which enable energy efficient, high performance fast memory based block storage. To achieve low operating power, Automatic-SSD directly reads or writes host-side data to underlying backend storage media without internal DRAM caches. To realize such DRAM-less approach with better performance and make it more energy efficient, Automatic-SSD also removes the internal processor(s) and firmware execution therein by fully automating the backend request management and data transfers over all pipelined hardware modules. We prototype Automatic-SSD on a middle-end FPGA custom board, employing massive numbers of phase change memories as representative of new memory technologies. Our evaluation results show that, compared to a conventional firmware-based approach, Automatic-SSD shows up 28.8× and 25.4× better bandwidth and latency behaviors, respectively, while consuming only 5% of the total energy, on overage.

关键词： Microprogramming Nonvolatile memory Hardware Phase change random access memory Memory management Power demand Media

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning architecture

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Journal of computer Science & Technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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KFS-LIO: Key-Feature Selection for Lightweight Lidar Inertial Odometry

KFS-LIO: Key-Feature Selection for Lightweight Lidar Inertia...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei Li Yu Hu Yinhe Han Xiaowei Li Research Center for Intelligent Computing Systems Institute of Computing Technology the Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology the Chinese Academy of Sciences Beijing China

Feature-based lidar odometry methods have attracted increasing attention due to their low computational cost. However, theoretically analysis of the effect of extracted features on pose estimation is still lacked. In this paper, we propose a method of key-feature selection for lightweight lidar inertial odometry, KFS-LIO, to further enhance the real-time performance by selecting the most effective subset of lidar feature constraints. Aiming at explaining the correlation between the feature distribution and state errors, a quantitative evaluation method of lidar constraints is introduced. In addition, to avoid recalculating the reprojection matrices in de-skewing step, we use the intermediate variables in IMU preintegration to compensate for lidar motion distortion. The experimental results demonstrate that KFS-LIO can reduce half of the LOAM features and provide comparable accuracy with the state-of-the-art odometry.

关键词： Laser radar Correlation Automation Conferences Pose estimation Feature extraction Distortion

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Non-convex potential games for finding global solutions to sensor network localization

arXiv

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arXiv 2023年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensor networks

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Internet-of-Things technology and applications for clean energy systems

Energy Conversion and Economics

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Energy Conversion and Economics 2021年第4期2卷 183-185页

作者： Strunz, Kai Wang, Xuanyuan Guo, Qinglai Xie, Le Zhang, Song Fang, Xin Wang, Jianxiao School of Electrical Engineering and Computer Science Berlin10587 Germany State Grid Jibei Electric Power Co. LTD. Beijing China Department of Electrical Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Business Architecture and Technology ISO New England Inc. HolyokeMA United States Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO United States School of Electrical and Electronic Engineering North China Electric Power University Beijing China

[...]future research interests regarding IESSs are summarised. A multi-stage PMU placement strategy was developed to alleviate power system vulnerability to possible false data injection attacks, where forward dynamic programming was used to distribute the capital cost of PMUs over a certain period. Three clustering methods, including linear regression, density-based spatial clustering of applications with noise (DBSCAN), and Gaussian mixture models (GMM), were combined for bad PMU data detection.

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Two-Stage Safe Reinforcement Learning for High-Speed Autonomous Racing

Two-Stage Safe Reinforcement Learning for High-Speed Autonom...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jingyu Niu Yu Hu Beibei Jin Yinhe Han Xiaowei Li Research Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture ICT CAS University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Decision making for autonomous driving is a safety-critical control problem. Prior works of safe reinforcement learning either tackle the problem with reward shaping or with modifying the reinforcement learning exploration process. However, the former cannot guarantee the safety during the learning process, while the latter relies heavily on expertise to design exquisite exploration policy. Currently, only short-term decision makings for low-speed driving were achieved in road scenes with basic geometries. In this paper, we propose a two-stage safe reinforcement learning algorithm to automatically learn a long-term policy for high-speed driving that guarantees safety during the entire training. In the first learning stage, model-free reinforcement learning is followed by a rule-based safeguard module to avoid danger at low speed without expert ne-tuning. In the second learning stage, the rule-based module is replaced with a data-driven counterpart to develop a closed-form analytical safety solution for high-speed driving. Moreover, an adaptive reward function is designed to match the different objectives of the two learning stages for faster convergence to an optimal policy. Experiments are conducted on a racing simulator TORCS which has complex racing tracks (e.g. sharp turns, hills). Compared with the state-of-the-art baselines, the results show that our method achieves zero safety violation and quickly converges to a more efficient and stable policy with an average speed of 127 km/h (3.3% higher than the best result of baselines) and an average swing of 3.96 degrees.

关键词： Training Geometry Roads Decision making Reinforcement learning Safety Cybernetics

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Faster than flash: An in-depth study of system challenges for emerging ultra-low latency SSDs

arXiv

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arXiv 2019年

作者： Koh, Sungjoon Jang, Junhyeok Lee, Changrim Kwon, Miryeong Zhang, Jie Jung, Myoungsoo Computer Architecture and Memory Systems Laboratory KAIST

Emerging storage systems with new flash exhibit ultra-low latency (ULL) that can address performance disparities between DRAM and conventional solid state drives (SSDs) in the memory hierarchy. Considering the advanced low-latency characteristics, different types of I/O completion methods (polling/hybrid) and storage stack architecture (SPDK) are proposed. While these new techniques are expected to take costly software interventions off the critical path in ULL-applied systems, unfortunately no study exists to quantitatively analyze system-level characteristics and challenges of combining such newly-introduced techniques with real ULL SSDs. In this work, we comprehensively perform empirical evaluations with 800GB ULL SSD prototypes and characterize ULL behaviors by considering a wide range of I/O path parameters, such as different queues and access patterns. We then analyze the efficiencies and challenges of the polled-mode and hybrid polling I/O completion methods (added into Linux kernels 4.4 and 4.10, respectively) and compare them with the efficiencies of a conventional interrupt-based I/O path. In addition, we revisit the common expectations of SPDK by examining all the system resources and parameters. Finally, we demonstrate the challenges of ULL SSDs in a real SPDK-enabled server-client system. Based on the performance behaviors that this study uncovers, we also discuss several system implications, which are required to take a full advantage of ULL SSD in the future. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamic random access storage

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Identifying common signatures and potential therapeutic biomarkers in COPD and lung cancer using miRNA-mRNA co-expression networks

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Informatics in Medicine Unlocked 2022年 34卷

作者： Fathinavid, Amirhossein Mousavian, Zaynab Najafi, Ali Nematzadeh, Sajjad Salimi, Mahdieh Masoudi-Nejad, Ali Department of Computer Engineering College of Engineering Hamedan Branch Islamic Azad University Hamedan Iran Laboratory of Systems Biology and Bioinformatics (LBB) Institute of Biochemistry and Biophysics University of Tehran Tehran Iran Department of Computer Science School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran Molecular Biology Research Center System Biology and Poisoning Institute Tehran Iran Computer Engineering Department Architecture and Engineering Faculty Nisantasi University Istanbul Turkey Department of Medical Genetics Institute of Medical Biotechnology National Institute of Genetic Engineering and Biotechnology (NIGEB) Tehran Iran

The association between non-small cell lung cancer (NSCLC) and chronic obstructive pulmonary disease (COPD) is now well established, as people with COPD are more likely to develop lung carcinoma. However, the evidence for this relationship is inconclusive and there is currently little information on the underlying molecular mechanisms. MicroRNAs (miRNAs) are one of the regulatory factors in NSCLC and COPD that their functions are widely studied in many chronic diseases and cancers. In this study, we applied systems biology approaches to identify and predict miRNAs that potentially play regulatory roles between COPD and NSCLC. We performed differential expression analysis on public miRNA and mRNA expression datasets, for both of diseases, and then we reconstructed two miRNA-mRNA co-expression networks and merged these two co-expression networks into a community co-expression network. Results indicated the existence of very common miRNAs (ex. hsa-miR-326 and hsa-miR-1293) and mRNAs (such as FAT2, ALOX5AP, and LDB2) between the two mentioned diseases. Moreover, we discovered specific miRNAs (hsa-miR-574-3p) that targeted common mRNAs. We utilized drug-target interaction networks to identify candidate drugs for common mRNAs, which can be considered in the treatment of two diseases (e.g. iloperidone). Generally, our study highlighted common miRNAs between COPD and NSCLC that as new signatures or biomarkers for therapeutic purposes. Investigating the miRNA biomarkers in this study improves our understanding about the shared mechanisms between COPD and NSCLC. © 2022

关键词： Co-expression networks Community networks COPD miRNA-mRNA network Non-small cell lung cancer

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Ameliorated Golden Jackal Optimization (Agjo) with Enhanced Movement and Multi-Angle Position Updating Strategy

SSRN

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SSRN 2023年

作者： Wahab, Magd Abdel Bai, Jianfu Khatir, Samir Abualigah, Laith Soete Laboratory Department of Electrical Energy Metals Mechanical Constructions and Systems Faculty of Engineering and Architecture Ghent University Belgium Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taiwan

Golden jackal optimization (GJO), a lately published meta-heuristic optimization algorithm, is inspired by the foraging behavior of pairs of golden jackals and shows an acceptable optimization performance. However, GJO exists a shortage in balancing exploration and exploitation, as it completely focused on exploitation in the later iterations. This study proposes a new variant of GJO, named Ameliorated Golden jackal optimization (AGJO). AGJO uses three strategies to alleviate the imbalance between the exploration and exploitation of GJO: the enhanced movement strategy, the global search strategy, and the multi-angle position update strategy for prey. An environmental disturbance factor is added to the third strategy to strengthen GJO's ability to evade the local optimal solution. The performance of AGJO is tested and compared with GJO and seven well-known meta-heuristic algorithms for 23 classical benchmark functions, CEC 2017 and CEC 2019. These results determined that AGJO performs better over 90% of these functions compared to GJO. AGJO is also highly competitive in terms of optimization capability and convergence speed compared with other algorithms. Finally, AGJO is applied to optimize five engineering design problems with constraints. Results show that AGJO is a more potential algorithm for solving real problems with constraints than GJO and other algorithms. © 2023, The Authors. All rights reserved.

关键词： Optimization

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