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International Conference on Biomimetic Intelligence and Robotics & Medical Robotics Forum, ICBIR+MRF 2024

作者： Lei, Yu Du, Pengkun Xie, Minghao Chen, Zheng Gu, Jason Ocean College Zhejiang University Zhoushan310058 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China Hainan Institute Zhejiang University Sanya572025 China Leicester International Institute Dalian University of Technology Panjin124221 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

Soft robotics has gained considerable attention in recent years for its structural flexibility and inherent safety in environmental interactions. To address the pitfalls of pneumatic actuation systems, namely sluggish response and inefficiencies, this paper introduces the design and fabrication of a Hydraulic-Driven Soft Robotic Arm (HDSRA). The study establishes a comprehensive platform that integrates actuation, sensing, and control software to provide an experimental prototype for validating control algorithms. A novel fabrication technique utilizing water-soluble PVA for single-step mold creation enhances the structural reliability of soft actuators. Through closed-loop control experiments, the HDSRA demonstrates rapid and precise tracking of 1Hz signals, encompassing sine, square, and ramp waves, thus confirming the platform's reliability. This foundational work lays a robust foundation for future research and the verification of control algorithms in HDSRAs. © 2024 The Authors.

关键词： Robotic arms

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Risk-Averse Self-Scheduling of Storage in Decentralized Markets

Risk-Averse Self-Scheduling of Storage in Decentralized Mark...

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IEEE General Meeting Power& Energy Society

作者： Ogun Yurdakul Farhad Billimoria Energy Systems and Infrastructure Analysis Division Argonne National Laboratory Lemont IL USA Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Department of Engineering Science University of Oxford Oxford United Kingdom

Storage is expected to be a critical source of firming in low-carbon grids. A common concern raised from ex-post assessments is that storage resources can fail to respond to strong price signals during times of scarcity. While commonly attributed to forecast error or failures in operations, we posit that this behavior can be explained from the perspective of risk-averse scheduling. Using a stochastic self-scheduling framework and real-world data harvested from the Australian National Electricity Market, we demonstrate that risk-averse storage resources tend to have a myopic operational perspective, that is, they typically engage in near-term price arbitrage and chase only few extreme price spikes and troughs, thus remaining idle in several time periods with markedly high and low prices. This has important policy implications given the non-transparency of unit risk aversion and apparent risk in intervention decision-making.

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A Material Identification Approach Based on Wi-Fi Signal

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computers, Materials & Continua 2021年第12期69卷 3383-3397页

作者： Chao Li Fan Li Wei Du Lihua Yin Bin Wang Chonghua Wang Tianjie Luo Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510700China PCL Research Center of Cyberspace Security Peng Cheng LaboratoryShenzhen518052China Department of Computer Science and Computer Engineering University of ArkansasFayetteville72701USA College of Electrical Engineering Zhejiang UniversityHangzhou310058China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Material identification is a technology that can help to identify the type of target *** approaches depend on expensive instruments,complicated pre-treatments and professional *** is difficult to find a substantial yet effective material identification method to meet the daily use *** this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level *** practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi *** that,the amplitude ratios and phase differences can be more stably utilized to classify the *** implement our system and evaluate its ability to identify materials in indoor *** result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

关键词： Internet of Things Wi-Fi signal channel state information material identification noise elimination

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

arXiv

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arXiv 2024年

作者： Breukelman, Enno Hall, Sophie Belgioioso, Giuseppe Dörfler, Florian The KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Decision and Control Systems Malvinas väg 10 StockholmSE-100 44 Sweden The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zürich8092 Switzerland

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach successfully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling. © 2024, CC BY-NC-ND.

关键词： Carbon

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FeReX: A Reconfigurable Design of Multi-bit Ferroelectric Compute-in-Memory for Nearest Neighbor Search

arXiv

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arXiv 2024年

作者： Xu, Zhicheng Liu, Che-Kai Li, Chao Mao, Ruibin Yang, Jianyi Kämpfe, Thomas Imani, Mohsen Li, Can Zhuo, Cheng Yin, Xunzhao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Zhejiang University Hangzhou China School of Electrical and Computer Engineering Georgia Institute of Technology United States Center Nanoelectric Technologies Fraunhofer IPMS Dresden Germany Department of Computer Science University of California Irvine United States Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

Rapid advancements in artificial intelligence have given rise to transformative models, profoundly impacting our lives. These models demand massive volumes of data to operate effectively, exacerbating the data-transfer bottleneck inherent in the conventional von-Neumann architecture. Compute-in-memory (CIM), a novel computing paradigm, tackles these issues by seamlessly embedding in-memory search functions, thereby obviating the need for data transfers. However, existing non-volatile memory (NVM)-based accelerators are application specific. During the similarity based associative search operation, they only support a single, specific distance metric, such as Hamming, Manhattan, or Euclidean distance in measuring the query against the stored data, calling for reconfigurable in-memory solutions adaptable to various applications. To overcome such a limitation, in this paper, we present FeReX, a reconfigurable associative memory (AM) that accommodates various distance metrics including Hamming, Manhattan, and Euclidean distances. Leveraging multibit ferroelectric field-effect transistors (FeFETs) as the proxy and a hardware-software co-design approach, we introduce a constrained satisfaction problem (CSP)-based method to automate AM search input voltage and stored voltage configurations for different distance based search functions. Device-circuit co-simulations first validate the effectiveness of the proposed FeReX methodology for reconfigurable search distance functions. Then, we benchmark FeReX in the context of k-nearest neighbor (KNN) and hyperdimensional computing (HDC), which highlights the robustness of FeReX and demonstrates up to 250× speedup and 104 energy savings compared with GPU. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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cVTS: A Constrained Voronoi Tree Search Method for High Dimensional Analog Circuit Synthesis

cVTS: A Constrained Voronoi Tree Search Method for High Dime...

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Design Automation Conference

作者： Aidong Zhao Xianan Wang Zixiao Lin Zhaori Bi Xudong Li Changhao Yan Fan Yang Li Shang Dian Zhou Xuan Zeng Microelectronics Department State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Department of Electrical Engineering University of Texas Dallas USA Department of Electrical and Computer Engineering University of California San Diego USA School of Data Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

A constrained Voronoi tree-based domain decomposition method for high-dimensional Bayesian optimization is proposed to solve large scale analog circuit synthesis problems, which can be formulated as high-dimensional heterogeneous black-box optimization. Hierarchical Voronoi tree progressively breaks down the design space into partitions with implicit performance boundaries such that promising regions are efficiently explored. Fast exploitation is ensured in Voronoi nest via local Bayesian optimization with a few observations. A slice-enhanced Gibbs sampling method is proposed to sample acquisition function cMES in irregular polyhedrons with design constraints. Compared with state-of-the-art methods, cVTS achieves significant speed up without loss of accuracy.

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MIM and MIIM-Based Optical Rectennas for Infrared Energy Harvesting at 10.6 μm

MIM and MIIM-Based Optical Rectennas for Infrared Energy Har...

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Mediterrannean Microwave Symposium (MMS)

作者： Ali Yahyaoui Ahmad Elsharabasy Jawad Yousaf Hatem Rmili Taoufik Aguili University of Tunis El Manar (UTM) National Engineering School of Tunis (ENIT) Communications Systems Laboratory (SysCom) Tunis Tunisia Electrical and Computer Engineering Department McMaster University Hamilton ON Canada Department of Electrical Computer and Biomedical Engineering Abu Dhabi University United Arab Emirates Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

This work presents a comparative study between metal-insulator-metal (MIM) and metal-insulator-insulator-metal (MIIM) based optical log spiral rectennas for infrared IR energy harvesting at 10.6 μm. We have considered the spiral antenna terminals as the electrodes of the rectifying diode and we have integrated the insulators between the two arms (electrodes) to enhance the harvested energy with the proposed nano-antennas. The study presents a comparison between the performance (E-field, I/V, responsivity, and resistivity) of the two proposed rectennas.

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IRS Channel Estimation in Cell-free MIMO Network: A Coalition Formation Guided Federated Learning Approach

IRS Channel Estimation in Cell-free MIMO Network: A Coalitio...

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IEEE Conference on Wireless Communications and networking

作者： Haoxuan Liu Nan Qi Xiaojie Li Alexandros A. A. Boulogeorgos Theodoros A. Tsiftsis Ming Xiao Juha Röning Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunications University of Thessaly Lamia Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden Biomimetics and Intelligent Systems Group University of Oulu Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

关键词： Training Data privacy Accuracy Federated learning Transfer learning Channel estimation Deep reinforcement learning Data models Surface treatment Convergence

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FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method

arXiv

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arXiv 2024年

作者： Bai, Yanbing Li, Siao Ju, Rui-Yang Yang, Zihao Yu, Jinze Chiang, Jen-Shiun Center for Applied Statistics School of Statistics Renmin University of China Beijing100872 China Graduate Institute of Networking and Multimedia National Taiwan University Taipei City106335 Taiwan Department of Mechanical Systems Engineering Tokyo University of Agriculture and Technology Tokyo183-8538 Japan Department of Electrical and Computer Engineering Tamkang University New Taipei City251301 Taiwan

Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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