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International Journal of High Speed Electronics and systems 2024年第4期33卷

作者： Shur, Michael Department Of Electrical Computer And Systems Engineering Rensselaer Polytechnic Institute 110 8th Street TroyNY United States

Terahertz (THz) sensing technology enables 6G communication, detection of biological and chemical hazardous agents, cancer detection, monitoring of industrial processes and products, and detection of mines and explosives. THz sensors support security in buildings, airports, and other public spaces. They found important applications in radioastronomy and space research and, more recently, in Artificial Intelligence-driven THz sensing of MMICs and VLSI. Exploding demand for data transfers will require using the 300 GHz band after 2028 or even before and will make the deployment of THz sensing electronics inevitable. This paper discusses the new physics of THz sensing and THz sensing devices. It also reviews the THz sensing market, and key THz sensor companies. © 2024 World Scientific Publishing Company.

关键词： Data transfer

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Assessment of Energy Management Strategies in Buildings Equipped with Modulating Heat Pumps 2

Assessment of Energy Management Strategies in Buildings Equi...

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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Efkarpidis, Nikolaos A. Christoforidis, Georgios C. Papagiannis, Grigoris K. University of Applied Sciences and Arts Northwestern Switzerland Institute of Electric Power Systems Windisch Switzerland University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece Aristotle University of Thessaloniki Power Systems Laboratory School of Electrical & Computer Engineering Thessaloniki Greece

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

Buildings equipped with battery energy storages (BES), thermal energy storages (TES), Photovoltaics (PV), solar thermal collectors (STC) and modulating heat pumps (HP) can significantly enhance the energy management potential. The energy management systems are mainly based on self-consumption maximization (SCM) and time-of-use arbitrage (ToUA). In terms of electricity pricing, dynamic pricing policies with two, three tariff zones or real-time pricing (RTP) are mostly applied. In this study, different control algorithms based on the concepts of SCM and ToUA are evaluated for a complex energy system consisted of BES, TES, PV, STC and HPs with variable-speed compressors. The performance of the proposed energy management strategies is assessed under ToU and RTP pricing regimes. © 2022 IEEE.

关键词： Costs

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Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第1期2卷 40-51页

作者： Ferrando, Robert Pagnier, Laurent Mieth, Robert Liang, Zhirui Dvorkin, Yury Bienstock, Daniel Chertkov, Michael University of Arizona Program in Applied Mathematics and Department of Mathematics TucsonAZ85721 United States Department of Industrial and Systems Engineering at Rutgers University PiscatawayNJ08854 United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218-2625 United States Elec. and Comp. Eng. and Ralph o'Connor Sustainable Energy Institute Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218-2625 United States Columbia University IEOR New YorkNY10027 United States

This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels. © 2023 IEEE.

关键词： Costs

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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Left and Right Lung-Specific Method of Segmentation in Chest Radiographs

Left and Right Lung-Specific Method of Segmentation in Chest...

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Applications of Machine Learning 2024

作者： Marsh, John J. Narayanan, Barath Narayanan Hardie, Russell C. Electrical and Computer Engineering Department University of Dayton 300 College Park DaytonOH45469 United States Sensor and Software Systems Division University of Dayton Research Institute 300 College Park DaytonOH45469 United States

ISBN: (纸本)9781510679368

Lung segmentation supports essential functionality within the realm of computer-aided detection and diagnosis using chest radiographs. In this research, we present a novel bifurcation approach of segmenting the lungs by separating them down the spinal column and having separate networks for the right and the left lungs respectively. Results from the right lung and left lung networks are then merged to form the overall lung. We utilize DeepLabV3+ network with ResNet50 as the backbone for both left and right lung networks. Results are presented for publicly available datasets such as Shenzhen Dataset and Japanese Society of Radiological Technology (JSRT) dataset. Our proposed bifurcation approach achieved an overall accuracy of 98.8% and an IoU (Intersection over Union) of 0.977 for a set of 100 cases in Shenzhen dataset. We conducted an additional robustness study of this method by training and testing on an independent dataset utilizing a hold-out methodology. We utilize a private dataset for the training and testing occurs on an independent JSRT dataset comprising 140 cases and our algorithm achieved an overall IoU of 0.945 thereby demonstrating its efficacy against other whole lung models and setting a new benchmark for future research works. © 2024 SPIE.

关键词： Image segmentation

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A Manta Ray-Inspired Biosyncretic Robot with Stable Controllability by Dynamic Electric Stimulation

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Cyborg and Bionic systems 2022年第1期2022卷 314-326页

作者： Chuang Zhang Yiwei Zhang Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Inst itute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang 110159China Emerging Technologies Institute Department of Industrial Manufacturing Systems Engineering University of Hong KongPokfulamChina

Biosyncretic robots,which are new nature-based robots in addition to bionic robots,that utilize biological materials to realize their core function,have been supposed to further promote the progress in *** as the main operation mechanism relates to the robotic overall ***,biosyncretic robots actuated by living biological actuators have attracted increasing ***,innovative propelling modes and control methods are still necessary for the further development of controllable motion performance of biosyncretic *** this work,a muscle tissue-based biosyncretic swimmer with a manta *** propelling mode has been *** is more,to improve the stable controllability of the biosyncretic swimmer,a dynamic control method based on circularly distributed multiple electrodes(CDME)has been *** this method,the direction of the electric field generated by the CDME could be real-time controlled to be parallel with the adtuation tissue of the dynamic ***,the instability of the tissue actuation induced by the dynamic included angle between the tissue axis and electric field direction could be ***,the biosyncretic robot has demonstrated stable,controllable,and efective swimming,by adjusting the electric stimulation pulse direction,amplitude,and *** work may be beneficial for not only the development of biosyncretic robots but also other related studies including bionic design of soft robots and muscle tissue engineering.

关键词： Robot robot Dynamic

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A 74.8-88.8 GHz Wideband CMOS LNA Achieving +4.73 dBm OP1dB and 6.39 dB Minimum NF

A 74.8-88.8 GHz Wideband CMOS LNA Achieving +4.73 dBm OP1dB ...

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2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Zou, Linfeng Zhao, Kangjie Fang, Zonghua Huang, Leilei Liu, Boxiao Shi, Chunqi Chen, Guangsheng Chen, Jinghong Zhang, Runxi East China Normal University Institute of Microelectronic Circuits and Systems China Shanghai Eastsoft Microelectronics Co. Ltd. China University of Houston Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350347647

This paper presents a 74.8-88.8 GHz low-noise amplifier (LNA) in a 55-nm CMOS technology. The LNA employs one common-gate (CG) stage, one common-source (CS) stage, and two cascode stages. A hybrid broadband interstage network (HBIN) is developed to extend the amplifier bandwidth. An inductor-feedback common-gate-shorting (IFCGS) technique is proposed to improve the gain and output 1dB compression point (OP1dB). An out-of-phase-dual-coupling (OPDC) transformer structure is also developed to achieve gm-boost and reduce the noise of the CG stage. Benefiting from the proposed techniques, the LNA achieves a measured -3dB bandwidth of 14 GHz, a 17.1 dB peak gain, a minimum noise figure of 6.39 dB, and 4.73 dBm OP1dB at 80 GHz while consuming 72.4 mW of power. © 2023 IEEE.

关键词： Millimeter waves

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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

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7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of engineering & Technology 2023.

关键词： Smart city

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Customizable Perturbation Synthesis for Robust SLAM Benchmarking

arXiv

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arXiv 2024年

作者： Xu, Xiaohao Zhang, Tianyi Wang, Sibo Li, Xiang Chen, Yongqi Li, Ye Raj, Bhiksha Johnson-Roberson, Matthew Huang, Xiaonan Department of Robotics University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States Department of Electrical and Computer Engineering Carnegie Mellon University United States

Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Simulation-based benchmarks have emerged as a highly scalable approach for robustness evaluation compared to real-world data collection. However, crafting a challenging and controllable noisy world with diverse perturbations remains relatively under-explored. To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations. This pipeline incorporates customizable hardware setups, software components, and perturbed environments. In particular, we introduce comprehensive perturbation taxonomy along with a perturbation composition toolbox, allowing the transformation of clean simulations into challenging noisy environments. Utilizing the pipeline, we instantiate the Robust-SLAM benchmark, which includes diverse perturbation types, to evaluate the risk tolerance of existing advanced multi-modal SLAM models. Our extensive analysis uncovers the susceptibilities of existing SLAM models to real-world disturbance, despite their demonstrated accuracy in standard benchmarks. Our perturbation synthesis toolbox, SLAM robustness evaluation pipeline, and Robust-SLAM benchmark will be made publicly available at https://***/Xiaohao-Xu/SLAM-under-Perturbation/. © 2024, CC BY-NC-SA.

关键词： Pipelines

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SOCIAL NETWORK INFLUENCE ON RESIDENTIAL SOLAR PHOTOVOLTAIC ADOPTION IN AN AGENT-BASED ELECTRICITY SYSTEM MODEL

SOCIAL NETWORK INFLUENCE ON RESIDENTIAL SOLAR PHOTOVOLTAIC A...

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ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： Russo, Gina Dello Wu, Lei Lytle, Ashley Odonkor, Philip School of Systems and Enterprises Stevens Institute of Technology HobokenNJ07030 United States Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States School of Humanities Arts and Social Sciences Stevens Institute of Technology HobokenNJ07030 United States School of Systems and Enterprises Stevens Institute of Technology HobokenNJ07030 United States

ISBN: (纸本)9780791888377

As the United States phases out traditional fossil fuels in favor of renewable energy sources, it is important to capitalize on all available avenues to increase renewable penetration. In the last decade, the costs associated with residential solar photovoltaic (PV) installations have decreased significantly, providing more homeowners with the opportunity to generate their own clean electricity. Research has found that the decision to invest in a residential solar PV system is guided by economic, social, and personal factors. Accounting for such complexities, the joint power of agent-based modeling and social network analysis is leveraged in this study to evaluate the effect of social influence on solar PV adoption. Featuring residential consumer agents with data-driven attributes, a logistic regression function to predict solar adoption, and random and small-world social network implementations, this work simulates residential solar PV adoption in New Jersey. Results indicate that including social influence in an agent-based electricity system model leads to increased installed residential solar capacity, but not necessarily higher adoption rates. These findings suggest that, with an understanding of the intricacies of consumer social networks, there are potential opportunities to bolster residential solar installations through low-cost social campaigns that motivate individuals to adopt home solar through their social ties. Copyright © 2024 by ASME.

关键词： Clean energy

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