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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Gu, Tianchen Lyu, Ruiyu Bi, Zhaori Yan, Changhao Yang, Fan Zhou, Dian Cui, Tao Liu, Xin Zhang, Zaikun Zeng, Xuan State Key Laboratory of Integrated Chips and Systems School of Microelectronics Fudan University Shanghai China Academy of Mathematics and Systems Science Chinese Academy of Sciences The University of Chinese Academy of Sciences Beijing China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Department of Electrical Engineering The University of Texas at Dallas RichardsonTX United States University of Texas Dallas United States

ISBN: (纸本)9798400706011

This study presents a novel high-dimensional multi-objective optimization method via adaptive gradient-based subspace sampling for analog circuit sizing. To handle constrained multi-objective optimization, we exploit promising regions from a non-crowded Pareto front, with lightweight Bayesian optimization (BO) based on a novel approximate constrained expected hypervolume improvement. This lightweight BO is computational efficient with constant complexity concerning simulation numbers. To tackle high-dimensional challenges, we reduce the effective dimensionality around promising regions by sampling candidates in an adaptive subspace. The subspace is constructed with gradients and previous success steps with their significance decaying over iterations. The gradients are approximated by sparse regression without additional simulations. The experiments on synthetic benchmarks and analog circuits illustrate advantages of the proposed method over Bayesian and evolutionary baselines. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Constrained optimization

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Thermal management challenges and approaches for liquid hydrogen-fueled aircraft

Thermal management challenges and approaches for liquid hydr...

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AIAA Aviation Forum and ASCEND, 2024

作者： Ordonez, Juan C. Sailabada, Chaianan Guo, Wei Cooley, Lance Cheetham, Peter Kim, Chul Zheng, Jim P. Gladin, Jonathan Cattafesta, Louis FAMU-FSU College of Engineering Department of Mechanical Engineering Center for Advanced Power Systems and Energy and Sustainability Center Florida State University TallahasseeFL32310 United States Electrical and Computer Engineering FAMU-FSU College of Engineering and Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Center for Advanced Power Systems FAMU-FSU College of Engineering and Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Department of Electrical Engineering The State University of New York at Buffalo BuffaloNY14260 United States School of Aerospace Engineering Aerospace Systems Design Laboratory Georgia Institute of Technology AtlantaGA30332 United States Department of Mechanical Materials and Aerospace Engineering Illinois Institute of Technology ChicagoIL60616 United States

ISBN: (数字)9781624107160

ISBN: (纸本)9781624107160

This paper presents an overview of thermal management challenges associated with aircraft that rely on liquid hydrogen as the sole fuel source. The paper discusses strategies that can enhance the overall cooling capacity and enable a larger contribution of fuel cells to the overall power. Design considerations for heat exchangers to integrate cryogenic hydrogen with secondary thermal management loops, outer mold line cooling, and additional thermal management strategies are presented. Cooling requirement estimates for a blended-wing aircraft (112 passengers, 2400 nautical miles) are reported. A finite element simulation using RANS suggests a cooling capacity rate of the order of 10 kW/m2 is available through the Outer Mold Line (OML). The paper also reports on the cooling capacity available by using hydrogen as a heat sink to cool the power train. For the IZEA concept, the hydrogen stream to the fuel cell can handle close to 6% of the fuel cell heat generation. © 2024, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hydrogen fuels

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Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition 16

Geometric Parameterized Macromodel of Uniaxial Capacitance A...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Gridchin, Aleksandr V. Koleda, Aleksei N. Barbin, Evgenii S. Nesterenko, Tamara G. Kolchuzhin, Vladimir A. Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

ISBN: (纸本)9798350330885

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer's dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters. © 2023 IEEE.

关键词： Capacitance

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Robust Design of IRS-Aided Multi-Group Multicast System With Imperfect CSI

Robust Design of IRS-Aided Multi-Group Multicast System With...

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作者： Jiang, Weiheng Xiong, Peiyun Nie, Jiangtian Ding, Zhiguo Pan, Cunhua Xiong, Zehui Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering Jurong West639798 Singapore Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates The University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Tampines487372 Singapore

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design. © 2002-2012 IEEE.

关键词： Beamforming

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Facile Semiconductor p-n Homojunction Nanowires with Strategic p-Type Doping engineering Combined with Surface Reconstruction for Biosensing Applications

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Nano-Micro Letters 2024年第10期16卷 15-30页

作者： Liuan Li Shi Fang Wei Chen Yueyue Li Mohammad Fazel Vafadar Danhao Wang Yang Kang Xin Liu Yuanmin Luo Kun Liang Yiping Dang Lei Zhao Songrui Zhao Zongzhi Yin Haiding Sun iGaN Laboratory School of MicroelectronicsUniversity of Science and Technology of ChinaHefei 230026People's Republic of China Department of Obstetrics and Gynecology The First Affiliated Hospital of Anhui Medical UniversityNo 218 Jixi RoadHefei 230022People's Republic of China Department of Electrical and Computer Engineering McGill University3480 University StreetMontrealQC H3A 0E9Canada Union Hospital Tongji Medical CollegeHuazhong University of Science and TechnologyNo 1277 Jiefang Ave.Wuhan 430022People's Republic of China

Photosensors with versatile functionalities have emerged as a cornerstone for breakthroughs in the future optoelectronic systems across a wide range of *** particular,emerging photoelectrochemical(PEC)-type devices have recently attracted extensive interest in liquid-based biosensing applications due to their natural electrolyte-assisted operating ***,a PEC-type photosensor was carefully designed and constructed by employing gallium nitride(GaN)p-n homojunction semiconductor nanowires on silicon,with the p-GaN segment strategically doped and then decorated with cobalt-nickel oxide(CoNiO_(x)).Essentially,the p-n homojunction configuration with facile p-doping engineering improves carrier separation efficiency and facilitates carrier transfer to the nanowire surface,while CoNiO_(x)decoration further boosts PEC reaction activity and carrier dynamics at the nanowire/electrolyte ***,the constructed photosensor achieves a high responsivity of 247.8 mA W^(-1)while simultaneously exhibiting excellent operating ***,based on the remarkable stability and high responsivity of the device,a glucose sensing system was established with a demonstration of glucose level determination in real human *** work offers a feasible and universal approach in the pursuit of high-performance bio-related sensing applications via a rational design of PEC devices in the form of nanostructured architecture with strategic doping engineering.

关键词： p-n GaN nanowires Strategic p-doping Surface decoration Photoelectrochemical sensor Glucose sensing

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Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity

Straggler-Resilient Federated Learning: Leveraging the Inter...

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作者： Reisizadeh, Amirhossein Tziotis, Isidoros Hassani, Hamed Mokhtari, Aryan Pedarsani, Ramtin Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA02139 United States The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States University of California at Santa Barbara Department of Electrical and Computer Engineering Santa BarbaraCA93106 United States

Federated learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local. It is, however, known that federated learning is prone to multiple system challenges including system heterogeneity where clients have different computation and communication capabilities. Such heterogeneity in clients' computation speed has a negative effect on the scalability of federated learning algorithms and causes significant slow-down in their runtime due to slow devices (stragglers). In this paper, we propose FLANP, a novel straggler-resilient federated learning meta-algorithm that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure. The key idea of FLANP is to start the training procedure with faster nodes and gradually involve the slower ones in the model training once the statistical accuracy of the current participating nodes' data is reached, while the final model for each stage is used as a warm-start model for the next stage. Our theoretical results characterize the speedup provided by the meta-algorithm FLANP in comparison to standard federated benchmarks for strongly convex losses and i.i.d. samples. For particular instances, FLANP slashes the overall expected runtime by a factor of O((Ns)) , where N and s denote the total number of nodes and the number of samples per node, respectively. In experiments, FLANP demonstrates significant speedups in wall-clock time -up to 6 - compared to standard federated learning benchmarks. © 2020 IEEE.

关键词： Learning algorithms

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Hierarchical Classification Strategy for Mitigating the Impact of The Presence of Pain in fNIRS-based BCIs

Hierarchical Classification Strategy for Mitigating the Impa...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ashwini Subramanian Laleh Najafizadeh Department of Electrical and Computer Engineering Integrated Systems and NeuroImaging Laboratory Rutgers University NJ USA

Brain computer interfaces (BCIs) can find applications in assistive systems for patients who experience conditions that impede their motor abilities. A BCI uses signals acquired from the brain to control external devices. As physical pain influences cortical signals, the presence of pain can negatively impact the performance of the BCI. In this work, we propose a strategy to mitigate this negative impact. Cortical signals are acquired from test subjects while they performed two mental arithmetic tasks, in the presence and the absence of painful stimuli. The task of the BCI is to reliably classify the two mental arithmetic tasks from the cortical recordings, irrespective of the presence or the absence of pain. We propose to do this classification, hierarchically, in two levels. In the first level, the data is classified into those captured in the presence and the absence of pain. Depending on the results of the classification from the first level, in the second level, the BCI performs the classification of tasks using a classifier trained either in the presence or the absence of pain. A 1-dimensional convolutional neural network (1D-CNN) is used for classification at both levels. It is observed that using this hierarchical strategy, the BCI is able to classify the tasks with an accuracy greater than 90%, irrespective of the presence or the absence of pain. Given that the presence of physical pain has shown previously to reduce the classification accuracy of a BCI to almost chance levels, this mitigation strategy will be a significant step towards enhancing the performance of BCIs when they are used in assistive systems for patients.

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ON INFINITE-HORIZON MINIMUM ENERGY CONTROL

arXiv

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arXiv 2025年

作者： Belabbas, Mohamed-Ali Chen, Xudong Electrical and Computer Engineering Department and Coordinated Science Laboratory University of Illinois Urbana-Champaign United States Electrical and Systems Engineering Washington University in St. Louis United States

We address the infinite-horizon minimum energy control problem for linear time-invariant finite-dimensional systems (A, B). We show that the problem admits a solution if and only if (A, B) is stabilizable and A does n... 详细信息

关键词： Linear time-invariant system

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A Scenario-Oriented Approach to Energy-Reserve Joint Procurement and Pricing

A Scenario-Oriented Approach to Energy-Reserve Joint Procure...

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作者： Shi, Jiantao Guo, Ye Tong, Lang Wu, Wenchuan Sun, Hongbin Tsinghua University Smart Grid and Renewable Energy Laboratory Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Shenzhen518071 China Tsinghua University State Key Laboratory of Power Systems Department of Electrical Engineering Beijing100084 China Cornell University School of Electrical and Computer Engineering IthacaNY14853 United States

We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources. These issues have so far been largely addressed empirically. In this paper, we first develop a scenario-oriented energy-reserve co-optimization model, which directly connects reserve procurement with possible outages and load/renewable power fluctuations without the need for empirical reserve requirements. Accordingly, reserve can be optimally procured system-wide to handle all possible future uncertainties with the minimum expected system total cost. Based on the proposed model, marginal pricing approaches are developed for energy and reserve, respectively. Locational uniform pricing is established for energy, and the similar property is also established for the combination of reserve and re-dispatch. In addition, properties of cost recovery for generators and revenue adequacy for the system operator (SO) are also proven. Simulations validate the effectiveness of the proposed mechanism. © 1969-2012 IEEE.

关键词： Costs

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Not so griddy: Internal representations of RNNs path integrating more than one agent 38

Not so griddy: Internal representations of RNNs path integra...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Redman, William T. Acosta, Francisco Acosta-Mendoza, Santiago Miolane, Nina Intelligent Systems Center Johns Hopkins Applied Physics Laboratory United States Department of Physics University of California Santa Barbara United States Dynamical Neuroscience University of California Santa Barbara United States Department of Electrical & Computer Engineering University of California Santa Barbara United States

Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self's position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses, stronger border responses, and tuning for the relative position of the two agents. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation. © 2024 Neural information processing systems foundation. All rights reserved.

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