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11th International Conference on Learning Representations, ICLR 2023

作者： Rosenberg, Aviv A. Vedula, Sanketh Romano, Yaniv Bronstein, Alex M. Department of Computer Science Technion Israel Department of Electrical and Computer Engineering Technion Israel Sibylla United Kingdom

Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable Y given explanatory features X.A limitation of QR is that it is only defined for scalar target variables, due to the formulation of its objective function, and since the notion of quantiles has no standard definition for multivariate ***, vector quantile regression (VQR) was proposed as an extension of QR for vector-valued target variables, thanks to a meaningful generalization of the notion of quantiles to multivariate distributions via optimal *** its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target Y given the features X;(ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles;(iii) no fast or scalable solvers for VQR currently *** this work we fully address these limitations, namely: (i) We extend VQR to the non-linear case, showing substantial improvement over linear VQR;(ii) We propose vector monotone rearrangement, a method which ensures the quantile functions estimated by VQR are monotone functions;(iii) We provide fast, GPU-accelerated solvers for linear and nonlinear VQR which maintain a fixed memory footprint, and demonstrate that they scale to millions of samples and thousands of quantile levels;(iv) We release an optimized python package of our solvers as to widespread the use of VQR in real-world applications. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Vectors

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An In-Context Learning Approach for LLM-Enabled Multi-Robot Task Allocation

An In-Context Learning Approach for LLM-Enabled Multi-Robot ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Huibo Chen, Shengkang Yin, Huan Zhang, Fumin School of Electrical and Computer Engineering Hong Kong University of Science and Technology SAR Hong Kong Hong Kong School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781665481090

In-context learning for large language models (LLMs) is employed to design a novel hybrid optimization framework for multi-robot task allocation. Results show that this approach can improve existing approaches, such as the market based scheme or the speedup and slow-down (SUSD) scheme by leveraging the contextual understanding ability of a LLM. Incorporating LLM removes the need for detailed information regarding the utility function for multi-robot task allocation. LLM helps the algorithm to escape local optima without relying on any human feedback. However, in-context learning is necessary to allow the LLM to agree with the underlying optimization algorithms. This paper proposes an method to instigate in-context learning for LLMs and demonstrate convergence to consensus between an LLM and the underlying optimization algorithms. The proposed method reduces the need for expert intervention and enhances the applicability and scalability for complex task allocation problems. Experimental results verify that the hybrid approach can improve the effectiveness of task allocations compared with recent SUSD-only methods. © 2024 IEEE.

关键词： Multipurpose robots

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Adaptive Control Strategy for Primary Frequency Regulation of BESS in Wind Farm 9

Adaptive Control Strategy for Primary Frequency Regulation o...

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9th Asia Conference on Power and electrical engineering, ACPEE 2024

作者： Gu, Yichao Zhuang, Kai Zhang, Jimiao Xie, Jianfeng Chongqing University of Science and Technology School of Electrical Engineering Chongqing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350309638

This paper proposes a novel adaptive synthetic inertia (SI) control scheme for the battery energy storage system (BESS) in the wind farm. The proposed approach dynamically adjusts the amplitude of the direct current (DC) of the voltage source converter (VSC) in response to grid frequency disturbances. The unique feature of the proposed control is its ability to emulate droop and inertia in real time by adjusting parameter values according to the rate of change of frequency (RoCoF) and frequency deviation. Within the SI control, a first-order low-pass element is incorporated into the virtual inertia control to augment the system inertia. Its advantages and effectiveness are rigorously demonstrated on a modified IEEE benchmark system under load changes and wind speed fluctuations. © 2024 IEEE.

关键词： Battery storage

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Design and Development of a Peltier Cooling System for Photovoltaic Thermal Management Using Simulation Tools 4

Design and Development of a Peltier Cooling System for Photo...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Oeishee, Moslema Hoque Muyeed, Munyem Ahammad Mehedi, Md. Rejoan Nidhi, Ramisa Sharar Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh Brac University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331530938

Mitigating the adverse effect of high temperature on photovoltaic (PV) module's efficiency in hot environment by using a thermoelectric cooling (TEC) method with PV through a detailed analysis, is the pivot objective of this paper. This research evaluates the Peltier module's effect on the power output and temperature of the PV modules in different ambient situations for the active cooling mode of the Peltier module. We utilized PVsyst and MATLAB (for initial circuit designs), as the simulation tools to analyze the performance ratio (PR) and thermal loss factor of the PV system for both with and without cooling conditions. Moreover, under an ambient temperature of 25.2°C, our developed TEC system enhances the PV module's performance ratio ranging from 83.42% to 85.8%, while The thermal loss factor is decreased ranging from -10.36% to -6.7%. These findings indicate that controlled cooling is capable of considerably improving PV performance, resulting in significant energy output rise. In addition to creating new paths to achieve more effective and sustainable energy production, this study demonstrates the way TEC systems might enhance solar energy systems, specifically in hot environments. © 2025 IEEE.

关键词： MATLAB

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Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

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computer Systems science & engineering 2023年第4期45卷 497-512页

作者： S.Nithya Roopa P.Anandababu Sibi Amaran Rajesh Verma Department of Computer Science and Engineering Kumaraguru College of TechnologyCoimbatore641049India Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of Computing Technologies SRM Institute of Science and TechnologyChennai603203India Department of Electrical Engineering King Khalid UniversityAbha61411Kingdom of Saudi Arabia

Recently,energy harvesting wireless sensor networks(EHWSN)have increased significant attention among research *** harvesting energy from the neighboring environment,the sensors in EHWSN resolve the energy constraint problem and offers lengthened network *** is one of the proficient ways for accomplishing even improved lifetime in *** clustering process intends to appropriately elect the cluster heads(CHs)and construct *** several models are available in the literature,it is still needed to accomplish energy efficiency and security in *** this view,this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy Harvesting Wireless Sensor Networks(CROC-SEHWSN)*** presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in *** CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization(RO)***,the CROC-SEHWSN model derives a fitness function(FF)involving seven distinct parameters connected to *** accomplish security,trust factor and link quality metrics are considered in the *** design of RO algorithm for secure clustering process shows the novelty of the *** order to demonstrate the enhanced performance of the CROC-SEHWSN approach,a wide range of simulations are carried out and the outcomes are inspected in distinct *** experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.

关键词： Clustering wireless sensor networks network lifetime energy efficiency metaheuristics energy harvesting rider optimization

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Design of electric powertrains to achieve NVH performance using autoencoders and a physical meaningful latent space

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Neural Computing and Applications 2025年第16期37卷 10287-10308页

作者： Ricardo Souza, Marcos Offner, Guenter Soltoggio, Andrea Mohammadpour, Mahdi Theodossiades, Stephanos Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough United Kingdom AVL List GmbH Graz Austria Computer Science Department School of Science Loughborough University Loughborough United Kingdom

One of the fundamental differences in the perception of electric (e-) vehicles is how their radiated noise is perceived with respect to classic internal combustion engines. Even though e-vehicles are usually quieter, the tonal content of the radiated noise can be more annoying. This paper proposes a novel approach that starts from the assumed radiated noise spectrum profile as input to a neural network that can return powertrain design parameters that would lead to generation of that specific noise profile. The proposed network acts as an autoencoder where the latent space is forced to have a physical meaning. As diverse combinations of powertrain parameters can result in similar noise profiles, a variational autoencoder is used to learn a structured latent representation, ensuring continuity and smooth transitions between possible solutions. The network predictions are validated against results of a three-dimensional CAE e-powertrain model. Overall, the mean absolute error is around 5 dBA for this feasibility study, which aims to demonstrate the concept. This work takes an inverse approach to the optimisation problem by starting from the user-perceived noise to predict the parameters required to achieve that. Although this study focuses solely on gear teeth microgeometry changes and bearing preloads, additional powertrain parameters could be incorporated as needed. © The Author(s) 2025.

关键词： Internal combustion engines

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Through the Looking Glass - The 2022 Edition: Trends in solid-state circuits from ISSCC

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IEEE Solid-State Circuits Magazine 2022年第1期14卷 54-72页

作者： Mirabbasi, Shahriar Fujino, Laura C. Smith, Kenneth C. Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada Division of Engineering Science University of Toronto Toronto Canada

The International Solid-State Circuits Conference (ISSCC) is the flagship conference of the IEEE Solid-State Circuits Society. The theme for ISSCC 2022 is 'Intelligent Silicon for a Sustainable World.' This theme further highlights the fact that to cope with strong environmental changes, the circuit design community is evolving more than ever to support a more sustainable world. Given their widespread use in our everyday lives, without change, ICs will become the dominant energy consumer and source of carbon emissions in the (near) future. Despite phenomenal advances in solid-state circuits and systems, low-power circuit design is becoming increasingly more sophisticated in all application areas, from digital computation and machine learning to analog design. In the context of data communications, advances in wireless and wireline circuits will help optimize local and global communications energy. Various sensors can be deployed to measure environmental data and optimize energy consumption. Power management techniques continue to be an essential aspect of a majority of circuits and systems to extend their lifetime and battery longevity. Furthermore, new technologies are providing opportunities to develop innovative circuits, reduce fabrication impacts, and improve recyclability. © 2009-2012 IEEE.

关键词： Timing circuits

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年第13期12卷 25059-25076页

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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Automatic Modulation Recognition Using Parallel Feature Extraction Architecture 18th

Automatic Modulation Recognition Using Parallel Feature Extr...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Tang, Haolin Zhao, Yanxiao Kuzlu, Murat Luo, Changqing Catak, Ferhat Ozgur Wang, Wei Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States Department of Engineering Technology Old Dominion University NorfolkVA United States Department of Computer Science Virginia Commonwealth University RichmondVA United States Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Computer Science San Diego State University San DiegoCA United States

ISBN: (纸本)9783031714665

Automatic Modulation Recognition (AMR) plays a critical role in wireless communication and can be applied in various applications such as spectrum monitoring and signal surveillance. Recently, different AMR approaches have been proposed based on Deep Learning (DL), which are inspired by the powerful abilities of feature extraction in neural networks, i.e., Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Among these solutions, hybrid methods extract spatial features and temporal features of signals by combining CNN and RNN into a single architecture to provide better performance. However, it is inefficient to extract two types of features using a single architecture since the temporal feature could be degraded after CNN layers, and the spatial feature may be compromised after RNN layers. To overcome this challenge, we propose a parallel neural network architecture for AMR, which extracts spatial features using CNN layers and temporal features using LSTM (Long Short-Term Memory) layers, respectively, in two parallel routes. Afterward, the extracted features will be combined to predict the modulation scheme of a signal. Extensive simulations are conducted on signals with 11 different modulation methods at a wide range of Signal-to-Noise Ratio (SNR) levels to evaluate our proposed parallel feature extraction architecture. In addition, we compare our solution against three other DL-based methods, and the results show that our method outperforms in terms of recognition accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Design of an Optical Wireless Communication Link Using Mid Wave Infrared Laser Source & Comparison with Short Wave Infrared Laser Sources in Different Atmospheric Anomalies: An Analytical Study

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Nonlinear Optics Quantum Optics 2024年第1-2期60卷 85-111页

作者： Mukherjee, Shibabrata Paul, Sujoy Mazumdar, Saswati Electrical Engineering Department Jadavpur University West Bengal Kolkata700032 India Centre for Distributed Computing Computer Science Engineering Department Jadavpur University West Bengal Kolkata700032 India

Optical Wireless Communication (OWC), sometimes referred to as Free Space Optics (FSO), is the backbone of leading-edge communication systems that offer high performance and fast speed. The main pitfall of this system is that the optical signal is hindered by the different atmospheric anomalies. As a result, the system performance has been distorted and degraded. Among the different atmospheric anomalies like fog, rain, snow, etc., fog is the main enemy of this system. Using the properly chosen optics at the transmitter and receiver sides and wavelength, the system performance may be increased in diverse weather conditions. In this article, four wavelengths like two Mid Wave Infrared (MWIR) (3400 nm, 4100 nm) and two Short Wave Infrared (SWIR) (1550 nm & 2000 nm), 27 dBm optical peak power, the OOK modulation technique, 10 MHz modulation bandwidth, 10-7 BER, 2 km optical wireless link distance have been considered. From the theoretical simulation results, it has been initiated that MWIR optical sources (like 4100 nm and 3400 nm) contribute 72.22 dB, and 71.27 dB link margin at adverse atmospheric anomalies respectively, whereas SWIR Optical sources like 1550 nm & 2000 nm furnish 67.70 dB, 68.50 dB link margin respectively for 2 km link distance. The 4100 nm wavelength Laser source performs better in different atmospheric anomalies and provides a better pointing error angle in clear, as well as adverse weather conditions, compared to other wavelengths. © 2024 Old City Publishing, Inc.

关键词： Space optics

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