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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Liao, Junpeng Tian, Ye Yang, Zirong Kang, Zhe Jin, Qinghui Zhang, Xiaowei Department of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China Centre for Optical and Electromagnetic Research College of Optical Science and Engineering State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou 310058 China

Adjoint shape optimization method is implemented to design SOI-based power splitters with arbitrary ratios. Splitters with ratios of 1:2, 1:4 and 1:8 are demonstrated with loss below 0.28 dB over a bandwidth of 100 nm... 详细信息

关键词： Shape optimization

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Highly efficient power splitter with arbitrary ratios based on inverse shape optimization

Highly efficient power splitter with arbitrary ratios based ...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

ISBN: (纸本)9798350350012

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Benjamin Poole Rajan Ratnakumar Dulip Tharaka Madurasinghe Christian Kümmerle Ganesh Kumar Venayagamoorthy Minwoo Lee Department of Computer Science University of North Carolina Charlotte USA Real-Time Power and Intelligent Systems Laboratory Clemson University USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases.

关键词： Robust control Network topology Data models Real-time systems Phasor measurement units Power systems Topology Power system reliability Reliability Load flow

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Blockchain in Environmental Sustainability Measures: a Survey

arXiv

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arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy

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An energy efficient EdgeAI autoencoder accelerator for reinforcement learning

IEEE Open Journal of Circuits and Systems

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IEEE Open Journal of circuits and systems 2021年 2卷 182-195页

作者： Manjunath, Nitheesh Kumar Shiri, Aidin Hosseini, Morteza Prakash, Bharat Waytowich, Nicholas R. Mohsenin, Tinoosh Department of Computer Science and Electrical Engineering University of Maryland BaltimoreMD21250 United States U.S. Army Research Laboratory AberdeenMD United States

In EdgeAI embedded devices that exploit reinforcement learning (RL), it is essential to reduce the number of actions taken by the agent in the real world and minimize the compute-intensive policies learning process. Convolutional autoencoders (AEs) has demonstrated great improvement for speeding up the policy learning time when attached to the RL agent, by compressing the high dimensional input data into a small latent representation for feeding the RL agent. Despite reducing the policy learning time, AE adds a significant computational and memory complexity to the model which contributes to the increase in the total computation and the model size. In this article, we propose a model for speeding up the policy learning process of RL agent with the use of AE neural networks, which engages binary and ternary precision to address the high complexity overhead without deteriorating the policy that an RL agent learns. Binary Neural Networks (BNNs) and Ternary Neural Networks (TNNs) compress weights into 1 and 2 bits representations, which result in significant compression of the model size and memory as well as simplifying multiply-accumulate (MAC) operations. We evaluate the performance of our model in three RL environments including DonkeyCar, Miniworld sidewalk, and Miniworld Object Pickup, which emulate various real-world applications with different levels of complexity. With proper hyperparameter optimization and architecture exploration, TNN models achieve near the same average reward, Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) performance as the full-precision model while reducing the model size by 10 × compared to full-precision and 3 × compared to BNNs. However, in BNN models the average reward drops up to 12% - 25% compared to the full-precision even after increasing its model size by 4 ×. We designed and implemented a scalable hardware accelerator which is configurable in terms of the number of processing elements (PEs) and memory data width

关键词： Energy efficiency

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A lightweight high-voltage boost circuit for soft-actuated micro-aerial-robots

A lightweight high-voltage boost circuit for soft-actuated m...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhijian Ren Jiahui Yang Suhan Kim Yi-Hsuan Hsiao Jeffrey Lang Yufeng Chen Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology (MIT) Cambridge MA USA

Flight is an energetically expensive task. While aerial insects can effortlessly fly through natural environments, achieving power autonomous flights in insect-scale robots remains a major challenge. In prior works, we developed soft-actuated insect-scale aerial robots that demonstrated unique capabilities such as in-flight collision recovery and somersaults. However, the soft dielectric elastomer actuators (DEAs) have low efficiency (< 20%) and require a high driving voltage (>600 V). These properties represent formidable obstacles for soft aerial robots to achieve power autonomous flights. In this work, we developed a 127 mg boost circuit that can convert a 7.7 V DC input into a 600 V and 400 Hz output for driving a 120 mg DEA. It has an equivalent capacitance and resistance of 20 nF and 5 $\mathbf{k}\Omega$ , respectively. The DEA is assembled into a 158 mg aerial robot, which can demonstrate liftoff while carrying the boost circuit as a payload. Although the robot remains tethered to an off-board power supply, this result represents a first step towards achieving power autonomy in soft aerial robots.

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Quantum Hardware Roofline: Evaluating the Impact of Gate Expressivity on Quantum Processor Design

arXiv

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arXiv 2024年

作者： Kalloor, Justin Weiden, Mathias Younis, Ed Kubiatowicz, John De Jong, Bert Iancu, Costin Department of Electrical Engineering and Computer Science University of California Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

The design space of current quantum computers is expansive with no obvious winning solution. This leaves practitioners with a clear question: "What is the optimal system configuration to run an algorithm?". This paper explores hardware design trade-offs across NISQ systems to guide algorithm and hardware design choices. The evaluation is driven by algorithmic workloads and algorithm fidelity models which capture architectural features such as gate expressivity, fidelity, and crosstalk. We also argue that the criteria for gate design and selection should be extended from maximizing average fidelity to a more comprehensive approach that takes into account the gate expressivity with respect to algorithmic structures. We consider native entangling gates (CNOT, ECR, CZ, ZZ, XX, Sycamore, √iSWAP), proposed gates (B Gate, √4 CNOT, √8 CNOT), as well as parameterized gates (FSim, XY). Our methodology is driven by a custom synthesis driven circuit compilation workflow, which is able to produce minimal circuit representations for a given system configuration. By providing a method to evaluate the suitability of algorithms for hardware platforms, this work emphasizes the importance of hardware-software co-design for quantum computing. © 2024, CC BY.

关键词： Economic and social effects

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National laboratory, Lemont, USA." Argonne National laboratory was left off the original publication. © The Author(s) 2025.

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Heading Control of a Long-Endurance Insect-Scale Aerial Robot Powered by Soft Artificial Muscles

Heading Control of a Long-Endurance Insect-Scale Aerial Robo...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yi-Hsuan Hsiao Suhan Kim Zhijian Ren YuFeng Chen Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology (MIT) Cambridge MA USA

Aerial insects demonstrate fast and precise heading control when they perform body saccades and rapid escape maneuvers. While insect-scale micro-aerial-vehicles (IMAVs) have demonstrated early results on heading control, their flight endurance and heading angle tracking accuracy remain far inferior to that of natural fliers. In this work, we present a long endurance sub-gram aerial robot that can demonstrate effective heading control during hovering flight. Through using a tilted wing stroke-plane design, our robot demonstrates a 10-second flight where it tracks a desired yaw trajectory with maximum and root-mean-square (RMS) error of $\boldsymbol{14.2^{\circ}}$ and $\boldsymbol{5.8}^{\mathrm{o}}$ . The new robot design requires 7% higher lift forces for enabling heading angle control, which creates higher stress on wing hinges and adversely influences robot endurance. To address this challenge, we developed novel 3-layered wing hinges that exhibit 1.82 times improvement of lifetime. With the new wing hinges, our robot demonstrates a 40-second hovering flight - the longest among existing sub-gram IMAVs. These results represent substantial improvement of flight capabilities in soft-actuated IMAVs, showing the potential of operating these insect-like fliers in cluttered natural environments.

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