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International Conference on High Voltage engineering and Power systems (ICHVEPS)

作者： Arif Majiid Nusa Pratama Muhammad Irfan Hashfi Ido Fandy Dermawan Kevin Marojahan Banjar-Nahor Nanang Hariyanto School of Electrical Engineering and Informatics Institut Teknologi Bandung PT PLN (Persero) Bandung Indonesia Faculty of Computer Science Universitas Indonesia PT PLN (Persero) Jakarta Indonesia UP2B Minahasa system PT PLN (Persero) Tomohon Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

The penetration of renewable energy with high intermittencies, such as solar power, presents a challenge for the North Sulawesi and Gorontalo power grid. Currently, the largest photovoltaic plant in the region is Likupang PV with a capacity of 15 MWp, followed by Isimu PV with a capacity of 10 MWp. The daytime peak load of the electrical system is 330 MW, with the initial penetration of photovoltaics into the system at 7.58%. Due to the low stiffness of the system, which was measured at 22.32 Mw/Hz in 2022, the intermittence has a direct impact on the system’s operation. The study method used for PV curtailment first determines the magnitude by analyzing the correlation between changes in multiple PV and changes in demand affecting frequency. The Pearson correlation coefficient is used to determine the magnitude of the correlation among these variables. If Likupang PV has a significant influence on changes in frequency, then it can become a curtailment target to limit the amount of power change due to intermittence. Secondly, through data mining, we can understand the function of the influence of changes in PV power on frequency changes. From this function, we can determine the operating limits of the PV to achieve the desired frequency operating range. Thirdly, in this study, this function is tested through simulations and experiments in the field to verify the accuracy of the required PV curtailment execution. By applying this approach, business owners can optimize the planning, regulation, and reliability of the electricity system, thereby mitigating power interruptions in solar power plants.

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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Distributed Optimization of Clique-Wise Coupled Problems via Three-Operator Splitting

arXiv

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arXiv 2023年

作者： Watanabe, Yuto Sakurama, Kazunori Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States Department of System Innovation Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan

This study explores distributed optimization problems with clique-wise coupling via operator splitting and how we can utilize this framework for performance analysis and enhancement. This framework extends beyond conventional pairwise coupled problems (e.g., consensus optimization) and is applicable to broader examples. To this end, we first introduce a new distributed optimization algorithm by leveraging a clique-based matrix and the Davis-Yin splitting (DYS), a versatile three-operator splitting method. We then demonstrate that this approach sheds new light on conventional algorithms in the following way: (i) Existing algorithms (NIDS, Exact diffusion, diffusion, and our previous work) can be derived from our proposed method;(ii) We present a new mixing matrix based on clique-wise coupling, which surfaces when deriving the NIDS. We prove its preferable distribution of eigenvalues, enabling fast consensus;(iii) These observations yield a new linear convergence rate for the NIDS with non-smooth objective functions. Remarkably our linear rate is first established for the general DYS with a projection for a subspace. This case is not covered by any prior results, to our knowledge. Finally, numerical examples showcase the efficacy of our proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization algorithms

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Leveraging Adaptive Algorithms for Real-Time Data Analysis

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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International Conference on Computing and Networking Technology (ICCNT)

作者： Girija Shankar Sahoo R Hannah Jessie Rani Manish Kumar Goyal Vazeer Ali Mohammed D Little Femilin Jana Namrata N. Wasatkar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer & Information System Lewis University Illinois USA Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area.

关键词： Adaptation models Data analysis Real-time systems Optimization

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Biofunctionalization-Optimized MoS2-Based Fet Biosensors for the Detection of Tau Protein

SSRN

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SSRN 2024年

作者： Wen, Xiaohong Zhao, Xuefeng Shan, Xinzhi Lu, Hongliang Gao, Xiumin Zhuang, Songlin Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of High-Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai200050 China State Key Laboratory of ASIC System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai200433 China

Field effect transistor (FET)-based biosensors can accurately quantify changes in weak signals and show excellent performance advantages in the detection of trace biological disease markers. Biological functionalization (biofunctionalization) is an important factor in the fabrication of FET-based biosensors, which directly affects the performance of the biosensor. However, little attention from researchers has been paid to biofunctionalization, which results in uneven performance of biosensors. Herein, MoS2-based FET biosensors are designed and fabricated using four different biofunctionalization methods. The most suitable biofunctionalization method is obtained by comparing the sensing performances of different functionalized MoS2based FET biosensors. Moreover, the working mechanism of the optimized biofunctionalization method is explored. Besides, the biofunctionalization-optimized MoS2-based FET biosensor exhibits excellent sensing performance, achieving sensitive detection of Tau protein in the concentration range of 10-2~103 pg/mL. This study provides a universal and effective strategy for the biofunctionalization optimization of two-dimensional materials-based FET biosensors. © 2024, The Authors. All rights reserved.

关键词： Molybdenum compounds

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Biofunctionalization-Optimized Mos2-Based Fet Biosensors for the Detection of Tau Protein

SSRN

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SSRN 2024年

Field effect transistor (FET)-based biosensors can accurately quantify changes in weak signals and show excellent performance advantages in the detection of trace biological disease markers. Biological functionalization (biofunctionalization) is an important factor in the fabrication of FET-based biosensors, which directly affects the performance of the biosensor. However, little attention from researchers has been paid to biofunctionalization, which results in uneven performance of biosensors. Herein, MoS2-based FET biosensors are designed and fabricated using four different biofunctionalization methods. The most suitable biofunctionalization method is obtained by comparing the sensing performances of different functionalized MoS2-based FET biosensors. Moreover, the working mechanism of the optimized biofunctionalization method is explored. Besides, the biofunctionalization-optimized MoS2-based FET biosensor exhibits excellent sensing performance, achieving sensitive detection of Tau protein in the concentration range of 10-2~103 pg/mL. This study provides a universal and effective strategy for the biofunctionalization optimization of two-dimensional materials-based FET biosensors. © 2024, The Authors. All rights reserved.

关键词： Molybdenum compounds

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Fea Study on the Tsv Copper Filling Influenced by the Additives and Electroplating Process

SSRN

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SSRN 2023年

作者： Zhao, Zifeng Liu, Ziyu Chen, Lin Sun, Qingqing Liu, Hongye Sun, Yabin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China Jiashan Fudan Institute Zhejiang Province Jiaxing314100 China Department of Electrical Engineering East China Normal University Shanghai200241 China

Abstract Through-Silicon Via (TSV) is a key technology of three-dimensional (3D) integration. However, it is easy to have voids in the Cu filling process of TSV. Additives and optimized filling process are used to solve this problem. However, the comprehensive analysis of these affecting parameter needs many experiments and cost much time. Thus, the finite element analysis (FEA) for the mass transferring of electroplating is carried out by the COMSOL software in the work. The filling processes with different inhibitor concentrations are first studied and the most suitable density of 64mol/m2 is obtained. However, high inhibitor concentration will lead to much lower plating speed. Thus, the effect of filling process such as current density and plating bath flow velocity on the voids are investigated under the optimized concentrations above. The best filling quality is obtained at the current density of 1.4 ASD and the bath stirring velocity of 0.4 m/s. The underlying growth mechanism of these influencing factors are also analyzed and revealed. © 2023, The Authors. All rights reserved.

关键词： Copper

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A Comprehensive Study on Drones and Big Data for Supply Chain Optimization Using a Novel Approach

A Comprehensive Study on Drones and Big Data for Supply Chai...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Dankan Gowda V D Palanikkumar Suganthi. N A.S. Malleswari Sanjog Thapa Rama Chaithanya Tanguturi Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bangalore Karnataka India Department of Computer Science & Engineering Aditya College of Engineering and Technology Surampalem Andhra Pradesh India Department of Information System and Analysis Lamar University Beaumont Texas USA School of Business Woxsen University Hyderabad India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

This research paper aims to provide qualitative and quantitative insights regarding the application of drones and big data techniques in the SCM field through a new scientific method. The proposed method exploits the opportunities of drones for real-time data capturing and big data technologies to enrich decision making in supply chain. The study is pertinent to modern tendencies in supply chain systems and proves the efficiency of the new method through the precise calculations and contrastive analysis. The following are some of the findings that suggest efficiency, cost decrease, and outstanding performance of the supply chain. The studies being offered in this research will prove beneficial for practitioners and researchers who endeavor to advance the concepts and practice of logistics and supply chain management.

关键词： Costs Supply chain management Supply chains Demand forecasting Big Data Real-time systems Security Reliability Drones Resilience

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Space-Air-Ground Integrated Network with Native Intelligence(NI-SAGIN):Concept,Architecture,Access and Networking

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China Communications 2022年第1期19卷 I0002-I0007页

作者： Zhenyu Xiao Qihui Wu Jiajia Liu Ning Zhang Tao Sun the School of Electronic and Information Engineering Beihang University IET China Institute of Communications and of Electronics the Nanjing University of Aeronautics and Astronautics(NUAA) key lab of Ministry of Industry and Information Technology of China the School of Cybersecurity Northwestern Polytechnical University Shaanxi Provincial Engineering Laboratory of Cyber Security Xi’an Unmanned System Security and Intelligent Communications ISTC Center IEEE IOT-AHSN TC IEEE Communications Society1 IEEE Vehicular Technology Society1 the Department of Electrical and Computer Engineering at University of Windsor1 China Mobile Research Institute1 GPP SA21 China Mobile’s 3GPP SA and CT groups

Ubiquitous coverage is one of the most important goals for mobile communication *** achieve this,integration of space,air,and ground networks is highly demanded,which expects to become the one of the enabling technolo... 详细信息

关键词： Access networks Networking

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Weakly Supervised and Semi-Supervised Semantic Segmentation Makes Full Use of a Small Amount of Labeled Data

SSRN

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SSRN 2023年

作者： Zhang, Wenming Li, Yaqian Li, Haibin Wang, Zhe Engineering Research Center Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Semi-supervised and weakly supervised semantic segmentation are very challenging tasks and have been studied in recent years. The purpose is to reduce the high cost of semantic segmentation label production. In this paper, we propose a weakly supervised assistant supervision strategy (ASS), which introduces more semantic segmentation for the shallow network by introducing additional classification tasks to the classification network, and enhances the fusion of shallow features and deep features. In addition, we propose a multi-scale global correlation module (MSG). This module uses the correlation degree of different scale regions to improve the weight of the nondiscriminative part, which solves the problem of insufficient CAMs activation. In most scenarios, weak supervision and semi-supervision are inseparable. We creatively propose a training strategy that integrates semi-supervision and weak supervision, adding pixel-level supervision in the training process of weak supervision to give full play to the role of pixel-level annotation. Extensive experiments on PASCAL VOC 2012 dataset show that our weakly supervised training strategy achieves the state of the art, and the combination of weakly supervised and semi-supervised training strategy can make more effective use of pixel-level labels. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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