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Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

Complex System Modeling and Simulation 2021年第3期1卷 198-217页

作者： Xue Han Yuyan Han Qingda Chen Junqing Li Hongyan Sang Yiping Liu Quanke Pan Yusuke Nojima the School of Computer Science Liaocheng UniversityLiaocheng 252000China the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China the School of Information Science and Engineering Shandong Normal UniversityJinan 252000China the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China the Department of Computer Science and Intelligent Systems Osaka Prefecture UniversityOsaka 599-8531Japan

To meet the multi-cooperation production demand of enterprises,the distributed permutation flow shop scheduling problem(DPFSP)has become the frontier research in the field of manufacturing *** this paper,we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup *** solve DPFSPs,significant developments of some metaheuristic algorithms are *** this context,a simple and effective improved iterated greedy(NIG)algorithm is proposed to minimize makespan in *** to the features of DPFSPs,a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed *** compare the proposed algorithm with state-of-the-art algorithms,including the iterative greedy algorithm(2019),iterative greedy proposed by Ruiz and Pan(2019),discrete differential evolution algorithm(2018),discrete artificial bee colony(2018),and artificial chemical reaction optimization(2017).Simulation results show that NIG outperforms the compared algorithms.

关键词： distributed permutation flow shop iterated greedy local search swapping strategy

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Model LEGO: creating models like disassembling and assembling building blocks 24

Model LEGO: creating models like disassembling and assemblin...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Jiacong Hu Jing Gao Jingwen Ye Yang Gao Xingen Wang Zunlei Feng Mingli Song College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University Robotics Institute School of Computer Science Carnegie Mellon University Electrical and Computer Engineering National University of Singapore Bangsheng Technology Co. Ltd. College of Computer Science and Technology Zhejiang University and Bangsheng Technology Co. Ltd. School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components. The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline, demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more. For more information, please visit https://***/.

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Integrated sensing and full-duplex communication: Joint transceiver beamforming and power allocation

arXiv

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arXiv 2023年

作者： He, Zhenyao Xu, Wei Shen, Hong Ng, Derrick Wing Kwan Eldar, Yonina C. You, Xiaohu National Mobile Communications Research Laboratory Southeast University Bangladesh School of Electrical Engineering and Telecommunications University of New South Wales Australia Faculty of Mathematics and Computer Science Weizmann Institute of Science

Beamforming design has been widely investigated for integrated sensing and communication (ISAC) systems with full-duplex (FD) sensing and half-duplex (HD) communication. To achieve higher spectral efficiency, in this paper, we extend existing ISAC beamforming design by considering the FD capability for both radar and communication. Specifically, we consider an ISAC system, where the base station (BS) performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problem is formulated to minimize the total transmit power of the system while guaranteeing the communication and sensing requirements. The downlink and uplink transmissions are tightly coupled, making the joint optimization challenging. To handle this issue, we first determine the receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power and then suggest an iterative solution to the remaining problem. We demonstrate via numerical results that the optimized FD communication-based ISAC leads to power efficiency improvement compared to conventional ISAC with HD communication. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Attention-based efficient robot grasp detection network

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Frontiers of Information Technology & Electronic engineering 2023年第10期24卷 1430-1444页

作者： Xiaofei QIN Wenkai HU Chen XIAO Changxiang HE Songwen PEI Xuedian ZHANG School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China College of Science University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System Shanghai 200093China Key Laboratory of Biomedical Optical Technology and Devices of Ministry of Education Shanghai 200093China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201210China

To balance the inference speed and detection accuracy of a grasp detection algorithm,which are both important for robot grasping tasks,we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network(AE-GDN).Three spatial attention modules are introduced in the encoder stages to enhance the detailed information,and three channel attention modules are introduced in the decoder stages to extract more semantic *** lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN.A high intersection over union(IoU)value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration,but might cause a *** is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the *** design a new IoU loss calculation method based on an hourglass box matching mechanism,which will create good correspondence between high IoUs and high-quality grasp *** achieves the accuracy of 98.9%and 96.6%on the Cornell and Jacquard datasets,*** inference speed reaches 43.5 frames per second with only about 1.2×10^(6)*** proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping *** are available at https://***/robvincen/robot_gradet.

关键词： Robot grasp detection Attention mechanism Encoder-decoder Neural network

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Understanding Dynamic Associations: Gait Recognition via Cross-view Spatiotemporal Aggregation Network

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IEEE Transactions on Circuits and systems for Video Technology 2022年 1-1页

作者： Chen, Jianyu Wang, Zhongyuan Zheng, Caixia Zeng, Kangli Zou, Qin Xiong, Zixiang School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China School of Computer Science Wuhan University Wuhan China Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

Gait recognition is a technique to recognize subjects under long-distance conditions. Existing gait recognition methods generally rely on extracting different motion representations of body parts to capture the gait information. However, the differences in appearance silhouettes between subjects are not discriminative enough to identify the distinct gaits. Therefore, exploring the representations of unique motion differences of subjects from the temporal domain is an effective way to boost gait recognition performance. In this paper, we propose a cross-view spatiotemporal aggregation network (CSTAN) to learn the most discriminative motion representations of subjects from multi-scale features. Particularly, CSTAN uses temporal contextual feature aggregation (TCFA) block to interact multi-scale features, increasing the diversity of temporal representations in feature sequences and producing suitable motion representations. Besides, a potential spatial feature assessment (PSFA) block is proposed to address the problem of missing silhouettes due to occlusion. Considering the multi-view context, we propose a cross-view prediction learning (CVPL) block to mitigate the impact of the view itself. By aggregating features in the spatiotemporal and view domains, CSTAN can learn a more appropriate representation of human motion differences. Extensive experiments on two public gait recognition datasets demonstrate the state-of-the-art performance of our proposed CSTAN. It is worth mentioning that the performance of CSTAN achieves a breakthrough in some complex environments, e.g., the subjects wearing different coats or carrying a bag. IEEE

关键词： Data mining

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Collaborative Multiagent Decision Making for Lane-Free Autonomous Driving 21

Collaborative Multiagent Decision Making for Lane-Free Auton...

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International Conference on Autonomous Agents and Multiagent systems

作者： Dimitrios Troullinos Georgios Chalkiadakis Ioannis Papamichail Markos Papageorgiou Dynamic Systems and Simulation Laboratory Technical University of Crete School of Electrical and Computer Engineering Technical University of Crete

ISBN: (纸本)9781713832621

This paper addresses the problem of collaborative multi-agent autonomous driving of connected and automated vehicles (CAVs) in lane-free highway scenarios. We eliminate the lane-changing task, i.e., CAVs may be located in any arbitrary lateral position within the road boundaries, hence allowing for better utilization of the available road capacity. As a consequence, vehicles operate in a much more complex environment, and the need for the individual CAVs to select actions that are efficient for the group as a whole is highly desired. We formulate this environment as a multiagent collaboration problem represented via a coordination graph, thus decomposing the problem with local utility functions, based on the interactions between vehicles. We produce a tractable and scalable solution by estimating the joint action of all vehicles via the anytime max-plus algorithm, with local utility functions provided by potential fields, designed to promote collision avoidance. Specifically, the fields have an ellipsoid form that is most suitable for lane-free highway environments. This novel use of max-plus with potential fields gives rise to a coordinated control policy that exploits only local information specific to each CAV. Our experimental evaluation confirms the effectiveness of our approach: lane-free movement allows for increased traffic flow rates, and vehicles are able to achieve speeds that are both high and close to their desired ones, even in demanding environments with high traffic flow.

关键词： Autonomous driving Lane-free traffic Coordination graphs Max-plus algorithm Artificial potential fields potential field vehicle Utility functions Traffic flow Freeway Road capacity Environments Chicken anemia virus Collision avoidance constant angular velocity Ellipsoid Automobile driving

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Kernel Principal Component Analysis Improvement based on Data-Reduction via Class Interval

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IFAC-PapersOnLine 2024年第4期58卷 390-395页

作者： Mohammed Tahar Habib Kaib Abdelmalek Kouadri Mohamed Faouzi Harkat Abderazak Bensmail Majdi Mansouri Mohamed Nounou Signals and Systems Laboratory Institute of Electrical and Electronic Engineering University M’Hamed Bougara of Boumerdes Avenue of Independence 35000 Boumerdes Algeria Systems and Advanced Material Laboratory Badji Mokhtar - Annaba University BP 12 23000-Annaba Algeria Ain El Kebira Cement Plant SCAEK BP 01 19400-Ain El Kebira Algeria Electrical and Computer Engineering Program Texas A&M University at Qatar Doha P.O. Box 23874 Qatar Chemical Engineering Program Texas A&M University at Qatar Doha P.O. Box 23874 Qatar

Kernel Principal Component Analysis (KPCA) is an effective nonlinear extension of the Principal Component Analysis for fault detection. For large-sized data, KPCA may drop its detection performance, occupy more storage space for the monitoring model, and take more execution time in the online part. Reduced KPCA pre-processes the training data before applying the KPCA method, the proposed approach selects samples based on class interval to reduce the number of observations in the training data set while maintaining decent detection performance. This approach is applied to the Tennessee Eastman Process and then compared to some of the existing approaches.

关键词： Fault Detection (FD) data-driven techniques Principal Component Analysis (PCA) Kernel Principal Component Analysis (KPCA) Histogram Tennessee Eastman Process

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A comprehensive evaluation of polygon-based hologram algorithms 14

A comprehensive evaluation of polygon-based hologram algorit...

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Holography, Diffractive Optics, and Applications XIV 2024

作者： Wang, Fan Wang, Pin Zhang, Yaping Blinder, David Gupta, Anuj Poon, Ting-Chung Ito, Tomoyoshi Shimobaba, Tomoyoshi Graduate School of Engineering Chiba University 1-33 Yayoi-cho Inage-ku Chiba Japan Yunnan Provincial Key Laboratory of Modern Information Optics Kunming University of Science and Technology Kunming Yunnan650500 China Pleinlaan 2 BrusselB-1050 Belgium IMEC Kapeldreef 75 LeuvenB-3001 Belgium CSIR-Central Scientific Instruments Organisation Chandigarh160030 India Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States

ISBN: (纸本)9781510682085

Polygon-based computer-generated holography algorithms can achieve high realism at low computational cost. Many recent contributions improve the hologram generation efficiency and solve many issues of 3D realistic rendering. This manuscript provides a comprehensive evaluation of their performance and potential through a detailed classification and comparison. Although the numerical-based algorithms using spectral interpolation offer greater flexibility in achieving realistic 3D displays, further phase-optimization algorithms are needed to render 3D reconstructions with higher definition, reduced noise, and faster updates. © 2024 SPIE. All rights reserved.

关键词： computer generated holography

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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao ...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Song, Xianxin Xu, Jie Liu, Fan Han, Tony Xiao Eldar, Yonina C. School of Science and Engineering Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence China Southern University of Science and Technology Department of Electrical and Electronic Engineering China Wireless Technology Lab 2012 Laboratories Huawei China Weizmann Institute of Science Faculty of Mathematics and Computer Science Israel

ISBN: (纸本)9781665459754

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the CramérRao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming. © 2022 IEEE.

关键词： Beamforming

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Lightweight Hyperspectral Image Classification with Lifelong Learning

TechRxiv

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TechRxiv 2023年

作者： Yuan, Anran Wang, Dingchen Bai, Jing Xiao, Zhu Wang, Zhongrui Li, Jianqing Jiao, Licheng The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The School of Computer Science and Engineering Macau University of Science and Technology China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Advancements in hyperspectral imaging (HSI) technology have led to the emergence of new application scenarios and application-specific land cover classes, necessitating the training and updating of numerous HSI classification models. However, this process incurs substantial computational overhead and hinders fast deployment, highlighting the need for lightweight models with lifelong learning capabilities in HSI classification. Existing approaches require optimizing a large number of trainable parameters for training and updating the feature extractors of HSI classification models, posing challenges in achieving lightweight classification and enabling lifelong learning. To address those challenges, in this paper, we propose an Attention-based Reservoir Computing (ARC) method. Specifically, the proposed ARC method leverages an attention leaky function within a reservoir computing model to exploit the spatial correlation and full bandwidth of spectral information in HSI. ARC contains a training-free feature extractor and a small classifier needs to be trained, eliminating the need for tedious feature extractor training and updating. We validate the classification ability of ARC on three benchmark datasets: Indian Pines, PaviaU, and Salinas. Our experiments demonstrate that ARC achieves excellent performance with overall accuracies of 93.85%, 99.07%, and 99.44%, with only 4k, 3k, and 4k trainable parameters, respectively. Furthermore, we demonstrate the lifelong learning capability of ARC by incorporating a substantial number of new classes, which is up to 7 times greater than the number of old classes, with an accuracy degradation of only 10.5%. This work opens the path towards advanced lightweight classification with lifelong learning abilities, enabling HSI technology to keep pace with the latest advancements in the field. © 2023, CC BY.

关键词： Classification (of information)

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