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Robotic fish:Opening a new era of underwater detection

The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling*

Robust Stochastic Shortest-Path Planning via Risk-Sensitive ...

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IEEE Conference on Decision and Control

作者： Clinton Enwerem Erfaun Noorani John S. Baras Brian M. Sadler Department of Electrical & Computer Engineering Institute for Systems Research University of Maryland College Park MD University of Texas at Austin TX

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion while mitigating hazardous outcomes. Mainstream chance-constrained incremental sampling techniques for solving SSP problems tend to be overly conservative and typically do not consider the likelihood of undesirable tail events. We propose an alternative risk-aware approach inspired by the asymptoticallyoptimal Rapidy-Exploring Random Trees (RRT*) planning algorithm, which selects nodes along path segments with minimal Conditional Value-at-Risk (CVaR). Our motivation rests on the step-wise coherence of the CVaR risk measure and the optimal substructure of the SSP problem. Thus, optimizing with respect to the CVaR at each sampling iteration necessarily leads to an optimal path in the limit of the sample size. We validate our approach via numerical path planning experiments in a two-dimensional grid world with obstacles and stochastic pathsegment lengths. Our simulation results show that incorporating risk into the tree growth process yields paths with lengths that are significantly less sensitive to variations in the noise parameter, or equivalently, paths that are more robust to environmental uncertainty. Algorithmic analyses reveal similar query time and memory space complexity to the baseline RRT* procedure, with only a marginal increase in processing time. This increase is offset by significantly lower noise sensitivity and reduced planner failure rates.

关键词： Industries Uncertainty Supply chain management Sensitivity Simulation Noise Tail Path planning Planning Complexity theory

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A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter 13

A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectr...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Hanson, Nathaniel Demirkaya, Ahmet Erdogmus, Deniz Stubbins, Aron Padir, Taskin Imbiriba, Tales Northeastern University Institute for Experiential Robotics BostonMA United States Northeastern University Marine Science Center BostonMA United States Northeastern University Department of Electrical and Computer Engineering BostonMA United States Northeastern University Institute for Experiential Ai BostonMA United States

ISBN: (纸本)9798350395570

Plastic waste entering the riverine harms local ecosystems, leading to negative ecological and economic impacts. Large parcels of plastic waste are transported from inland to oceans leading to a global scale problem of floating debris fields. In this context, efficient and automated monitoring of mismanaged plastic waste is paramount. To address this problem, we analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios. We enable near-real-time tracking of partially submerged plastics by using snapshot Visible-Shortwave Infrared hyperspectral imaging. Our experiments indicate that imaging strategies coupled with machine learning classification approaches can lead to high detection accuracy even in challenging scenarios, especially when leveraging hyperspectral data and nonlinear classifiers. All code, data, and models are available online: https://***/RIVeR-Lab/hyperspectral_macro_plastic_detection © 2023 IEEE.

关键词： Hyperspectral imaging

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TReCiM: Lower Power and Temperature-Resilient Multibit 2FeFET-1T Compute-in-Memory Design 24

TReCiM: Lower Power and Temperature-Resilient Multibit 2FeFE...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Zhou, Yifei Kämpfe, Thomas Ni, Kai Amrouch, Hussam Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Fraunhofer IPMS Dresden Germany Department of Electrical Engineering University of Notre Dame Notre Dame United States Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

ISBN: (纸本)9798400710773

Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the"memory wall" issue. By utilizing nonvolatile memory (NVM) devices in a crossbar structure, CiM efficiently accelerates multiply-accumulate (MAC) computations, the crucial operations in neural networks and other AI models. Among various NVM devices, Ferroelectric FET (FeFET) is particularly appealing for ultra-low-power CiM arrays due to its CMOS compatibility, voltage-driven write/read mechanisms and high ION/IOFF ratio. Moreover, subthreshold-operated FeFETs, which operate at scaling voltages in the subthreshold region, can further minimize the power consumption of CiM array. However, subthreshold-FeFETs are susceptible to temperature drift, resulting in computation accuracy degradation. Existing solutions exhibit weak temperature resilience at larger array size and only support 1-bit. In this paper, we propose TReCiM, an ultra-low-power temperature-resilient multibit 2FeFET-1T CiM design that reliably performs MAC operations in the subthreshold-FeFET region with temperature ranging from 0°C to 85°C at scale. We benchmark our design using NeuroSim framework in the context of VGG-8 neural network architecture running the CIFAR-10 dataset. Benchmarking results suggest that when considering temperature drift impact, our proposed TReCiM array achieves 91.31% accuracy, with 1.86% accuracy improvement compared to existing 1-bit 2T-1FeFET CiM array. Furthermore, our proposed design achieves 48.03 TOPS/W energy efficiency at system level, comparable to existing designs with smaller technology feature sizes. © 2024 Copyright is held by the owner/author(s).

关键词： MISFET devices

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Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent systems: A Distributed Optimization Approach (extended version)

arXiv

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arXiv 2025年

作者： Mehdifar, Farhad Dhullipalla, Mani H. Bechlioulis, Charalampos P. Dimarogonas, Dimos V. The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Division of Systems and Control The Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positions of other agents with whom they may or may not have direct communication. These constraints need to be satisfied asymptotically or after an unknown finite time. The agents’ objective is to collectively achieve a formation that fulfills all constraints. The problem is initially framed as a centralized unconstrained optimization, where the solution yields the optimal configuration by maximizing an objective function that reflects the degree of constraint satisfaction. This function encourages collaboration, ensuring agents help each other meet their constraints while fulfilling their own. When the constraints are infeasible, agents converge to a least-violating solution. A distributed consensus-based optimization scheme is then introduced, which approximates the centralized solution, leading to the development of distributed controllers for single-integrator agents. Finally, simulations validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi agent systems

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Intelligent Control of Power Converters using Reinforcement Learning 9

Intelligent Control of Power Converters using Reinforcement ...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Krishna, R. Sathya Barna Sudharani, R. Devi, S. Rukmani Kulkarni, Pramodkumar H. Rohini, G. Sindhu, M. Sathyabama Institute of Science And Technology Department of Computer Science Engineering Tamil Nadu Chennai600119 India Panimalar Engineering College Department of Mathematics Tamil Nadu Poonamallee600123 India Saveetha College of Liberal Arts And Sciences Deemed To Be University SIMATS Department of Computer Science Tamil Nadu Chennai600124 India Dr. D. Y. Patil Institute of Technology Department of Electronics And Telecommunications Maharashtra Pimpri411018 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Thiruverkadu600077 India R.M.K. Engineering College Department of Computer Science And Business Systems Tamil Nadu Thiruvallur601206 India

ISBN: (纸本)9798350365092

Power converter control techniques must evolve to accommodate power system complexity and renewable energy integration. Traditional control methods may be difficult to adapt to these dynamic and uncertain systems. Reinforcement learning (RL) can build optimal control policies in complex and unpredictable situations, making it a promising intelligent control solution in this scenario. This research examines how reinforcement learning can control power converters in energy systems. Advanced reinforcement learning methods, notably deep reinforcement learning, are used to improve power converter efficiency in varied operational scenarios. This research examines the integration of reinforcement learning (RL)-based controllers into power converter systems, focusing on their autonomous learning and adaptation. The explored method uses Reinforcement Learning (RL) flexibility to address power system time-varying conditions, non-linearities, and uncertainties. This research considers practical concerns such RL-based control's effects on real-time execution, processing requirements, and safety. The report concludes with suggestions for future research including expanding RL-based intelligent control systems to power system applications. Reinforcement learning keywords include stability, efficiency, reliability, renewable energy, deep reinforcement learning, power converters, intelligent control, and energy systems. © 2024 IEEE.

关键词： Power converters

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A Cooperative Security Monitoring Mechanism Aided by Optimal Multiple Slave Cluster Heads for UASNs

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China Communications 2023年第5期20卷 148-169页

作者： Yougan Chen Wei Wang Xiang Sun Yi Tao Zhenwen Liu Xiaomei Xu Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Xiamen UniversityXiamen 361005China Shenzhen Research Institute of Xiamen University Shenzhen 518000China Dongshan Swire Marine Station College of Ocean and Earth SciencesXiamen UniversityXiamen 361102China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM 87131USA College of Harbour and Coastal Engineering Jimei UniversityXiamen 361021China

As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very *** this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a *** designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security *** addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is *** simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing *** addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.

关键词： underwater acoustic sensor networks data security cluster head nodes optimal location distribution of multiple slave cluster head nodes

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An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

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Plant Phenomics 2023年第2期5卷 268-284页

作者： Yubao Deng Haoran Xi Guoxiong Zhou Aibin Chen Yanfeng Wang Liujun Li Yahui Hu College of Computer&Information Engineering Central South University of Forestry and TechnologyChangsha410004HunanChina College of Mechanical&Electrical Engineering Central South University of Forestry and TechnologyChangsha410004HunanChina National University of Defense Technology 410015ChangshaHunanChina Department of Soil and Water Systems University of IdahoMoscowID83844USA Plant Protection Research Institute Academy of Agricultural Sciences410125ChangshaHunanChina

Tomato disease control is an urgent requirement in the field of intellectual agriculture,and one of the keys to it is quantitative identification and precise segmentation of tomato leaf *** diseased areas on tomato leaves are tiny and may go unnoticed during *** edge also makes the segmentation accuracy *** on UNet,we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module(MC-UNet).First,a Multi-scale Convolution Module is *** module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes,and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation ***,a Cross-layer Attention Fusion Mechanism is *** mechanism highlights tomato leaf disease locations via gating structure and fusion ***,we employ SoftPool rather than MaxPool to retain valid information on tomato ***,we use the SeLU function appropriately to avoid network neuron *** compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32%accuracy and 6.67M *** method achieves good results for tomato leaf disease segmentation,which demonstrates the effectiveness of the proposed methods.

关键词： network precise sizes

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TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator

arXiv

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arXiv 2024年

作者： Zhang, Meng Yin, Dennis Gangi, Nicholas Begović, Amir Chen, Alexander Huang, Zhaoran Rena Gu, Jiaqi Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency of optics, especially for real-time, low-energy deep neural network (DNN) inference tasks on resource-restricted edge platforms. However, current optical neural accelerators based on foundry-available devices and conventional system architecture still encounter a performance gap compared to highly customized electronic counterparts. To bridge the performance gap due to lack of domain specialization, we present a time-multiplexed dynamic photonic tensor accelerator, dubbed TeMPO, with cross-layer device/circuit/architecture customization. At the device level, we present foundry-compatible, customized photonic devices, including a slow-light electro-optic modulator with experimental demonstration, optical splitters, and phase shifters that significantly reduce the footprint and power in input encoding and dot-product calculation. At the circuit level, partial products are hierarchically accumulated via parallel photocurrent aggregation, lightweight capacitive temporal integration, and sequential digital summation, considerably relieving the analog-to-digital conversion bottleneck. We also employ a multi-tile, multi-core architecture to maximize hardware sharing for higher efficiency. Across diverse edge AI workloads, TeMPO delivers digital-comparable task accuracy with superior quantization/noise tolerance. We achieve a 368.6 TOPS peak performance, 22.3 TOPS/W energy efficiency, and 1.2 TOPS/mm2 compute density, pushing the Pareto frontier in edge AI hardware. This work signifies the power of cross-layer co-design and domain-specific customization, paving the way for future electronic-photonic accelerators with even greater performance and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Memory architecture

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ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning

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IEEE Transactions on Neural Networks and Learning systems 2024年第6期36卷 11399-11413页

作者： Kun Wu Yinuo Zhao Zhiyuan Xu Zhengping Che Chengxiang Yin Chi Harold Liu Feifei Feng Jian Tang Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY USA Beijing Institute of Technology Beijing China Beijing Innovation Center of Humanoid Robotics Beijing China Midea Group Shanghai China

Offline reinforcement learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a safe and promising control policy. The prevailing methods typically learn a conservative policy to mitigate the problem of Q-value overestimation, but it is prone to overdo it, leading to an overly conservative policy. Moreover, they optimize all samples equally with fixed constraints, lacking the nuanced ability to control conservative levels in a fine-grained manner. Consequently, this limitation results in a performance decline. To address the above two challenges in a united way, we propose a framework, adaptive conservative level in Q-learning (ACL-QL), which limits the Q-values in a mild range and enables adaptive control on the conservative level over each state-action pair, i.e., lifting the Q-values more for good transitions and less for bad transitions. We theoretically analyze the conditions under which the conservative level of the learned Q-function can be limited in a mild range and how to optimize each transition adaptively. Motivated by the theoretical analysis, we propose a novel algorithm, ACL-QL, which uses two learnable adaptive weight functions to control the conservative level over each transition. Subsequently, we design a monotonicity loss and surrogate losses to train the adaptive weight functions, Q-function, and policy network alternatively. We evaluate ACL-QL on the commonly used datasets for deep data-driven reinforcement learning (D4RL) benchmark and conduct extensive ablation studies to illustrate the effectiveness and state-of-the-art performance compared with existing offline DRL baselines.

关键词： Adaptive systems Reinforcement learning Q-learning Weight measurement Loss measurement Training Propagation losses Navigation Learning systems Imitation learning

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