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Power, Energy and Electrical engineering (CPEEE), International Conference on

作者： Yong Lyu Tiande Mo Ailiu Cheng Mingyou Ma Jiahao Dong Weng Zak Jason Lee Department of Data and Systems Engineering The University of Hong Kong Hong Kong SAR China Smart City Division Hong Kong Productivity Council Hong Kong SAR China School of Software and Microelectronics Peking University Beijing China

ISBN: (数字)9798331520090

ISBN: (纸本)9798331520106

This study adopts a game-theoretical approach to examine the impact of introducing retired electrical battery-based (REVB-based) energy storage systems on the energy storage market and the electrical grid. A bi-level Nash-Stackelberg game is formulated with three players: power stations (PSs), energy storage aggregators (ESAs), and energy storage service operators (ESSOs). In this framework, power stations participate in the day-ahead market and develop bidding strategies for electricity supply. Energy storage service operators, equipped with either standard storage systems or REVB-based systems, determine optimal bidding strategies for charging and discharging power. These strategies aim to provide load-shaving during peak-load periods and enhance the integration of solar power during base-load periods. The standard and REVB-based energy storage systems differ in their maximum charging-discharging cycles, investment and maintenance costs. The ESA acts as an intermediary between the day-ahead market and the energy storage market, aggregating the charging and discharging power from ESSOs. Specifically, during peak-load periods, the ESA arbitrages between the two markets by selling stored discharging power purchased from ESSOs to the day-ahead market. Conversely, during base-load periods, the ESA purchases charging power from the day-ahead market to charge energy storage systems and purchases charging capacity from ESSOs to facilitate the accommodation of renewable energy. The ESA is regarded as the leader in the Stackelberg-Nash game, with PSs and ESSOs acting as the followers. Additionally, incentive compensation is considered to address potential profit losses incurred by the ESA during baseload periods. Numerical simulations are conducted to explore how the introduction of REVB-based energy storage systems influences profits and system performance within the energy storage and day-ahead markets.

关键词： Renewable energy sources Costs System performance Games Numerical simulation Batteries Standards Power generation Energy storage Investment

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Efficient Batch Verification and Privacy-Preserving data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： data aggregation

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Imploding between the facts and concerns: analysing human–AI musical interaction

Humanities and Social Sciences Communications

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Humanities and Social Sciences Communications 2025年第1期12卷 1-20页

作者： Kelsey Cotton Kıvanç Tatar Anna-Kaisa Kaila Petra Jääskeläinen André Holzapfel Data Science and AI Division Computer Science and Software Engineering Chalmers Institute of Technology and University of Gothenburg Gothenburg Sweden Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden

The advancement of AI-tools for musical performance has inspired exciting opportunities for interaction with musical-AI-agents. Interactions between humans and AI-agents in musical settings entail dynamic exchanges of control and power, and framings of AI-agents’ roles by human performers. We probe these framings and power-control exchanges through qualitative thematic lenses, drawing from post-phenomenology,matters of fact and concernand feminist science and technology studies. We contribute with a novel interdisciplinary analytical method as a tool for developers and designers of AI systems to help visibilise and examine theimplicit, the wider connections and entangled filaments in Human–AI musical interactions.

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Improving the efficiency of application development based on the Macchinetta framework

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NTT Technical Review 2017年第2期15卷 1-7页

作者： Suzuki, Gengo Kanamaru, Akira Iwatsuka, Takuya Katada, Junya Okada, Satoshi Mochida, Seiichiro Natsukawa, Katsuyuki Motohashi, Kenji Hishiki, Takanori Kaneko, Takehiko Tanabe, Kenji Izumoto, Hiroshi Sakai, Miyotaka Yamashita, Katsuyuki Iwaki, Yosuke Software Engineering Project NTT Software Innovation Center Japan Center for Applied Software Engineering Production Engineering Department System Engineering Headquarters NTT DATA Corporation Japan SE Department in Core Technology Quality Management and Engineering Division NTT Comware Corporation Japan

At the NTT software Innovation Center, we are working to improve the stability and productivity of software development throughout the entire NTT Group by conducting research and development on new software development techniques and making these techniques accessible to developers. This article introduces our work on the Macchinetta framework that we developed in order to produce web-based enterprise applications more efficiently on the Macchinetta tool suite that commonizes development tools, and on the Macchinetta talent pool that provides talented developers to support efficient development.

关键词： software design

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Parallel processing for Fingerprint feature extraction

Parallel processing for Fingerprint feature extraction

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2011 International Conference on Electrical engineering and Informatics, ICEEI 2011

作者： Indrawan, G. Sitohang, B. Akbar, S. Data and Software Engineering Research Division School of Electrical Engineering and Informatics ITB Jl. Ganesha No. 10 Bandung West Java Indonesia

ISBN: (纸本)9781457707520

Real time fingerprint identification based on original non-alphanumeric content matching shouldn't compromise with its speed factor. Real production-quality implementation usually equipped with specific machine architecture to optimize this factor. On this research, we focus on achieving higher speed on common machine with respect to fingerprint accuracy factor. © 2011 IEEE.

关键词： Extraction

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Batch Mode Deep Active Learning for Regression on Graph data

Batch Mode Deep Active Learning for Regression on Graph Data

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Samoaa, Peter Aronsson, Linus Leitner, Philipp Chehreghani, Morteza Haghir Chalmers University of Technology Data Science and AI Division Gothenburg Sweden Chalmers University of Technology Interaction Design and Software Engineering Gothenburg Sweden

ISBN: (纸本)9798350324457

Acquiring labelled data for machine learning tasks, for example, for software performance prediction, remains a resource-intensive task. This study extends our previous work by introducing a batch-mode deep active learning approach tailored for regression in graph-structured data. Our framework leverages the source code conversion into Flow Augmented-AST graphs (FA-AST), subsequently utilizing both supervised and unsupervised graph embeddings. In contrast to single-instance querying, the batch-mode paradigm adaptively selects clusters of unlabeled data for labelling. We deploy an array of base kernels, kernel transformations, and selection methods, informed by both Bayesian and non-Bayesian strategies, to enhance the sample efficiency of neural network regression. Our experimental evaluation, conducted on multiple real-world software performance datasets, demonstrates the efficacy of the batch mode deep active learning approach in achieving robust performance with a reduced labelling budget. The methodology scales effectively to larger datasets and requires minimal alterations to existing neural network architectures. © 2023 IEEE.

关键词： Active Learning Deep Learning Graph Neural Network Kernels

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

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Journal of Computer Science & Technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive （SSD） flash cache endurance

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Deep Learning Methods Used in Remote Sensing Images: A Review

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Journal of Environmental & Earth Sciences 2023年第1期5卷 33-64页

作者： Ekram M.Rewhel Jianqiang Li Amal A.Hamed Hatem M.Keshk Amira S.Mahmoud Sayed A.Sayed Ehab Samir Hind H.Zeyada Sayed A.Mohamed Marwa S.Moustafa Ayman H.Nasr Ashraf K.Helmy School of Software Engineering Beijing University of Science and TechnologyBeijing100083China Data Reception Analysis and Receiving Station DivisionNational Authority for Remote Sensing and Space ScienceCairo1564Egypt

Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,oil spill identification,among many *** machine learning algorithms are given less and less attention in the era of big ***,a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer *** number of relevant articles has nearly doubled every year since *** in remote sensing technology,as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale,have opened up the possibilities for a wide range of modern ***,there are some challenges related to the availability of annotated data,the complex nature of data,and model parameterization,which strongly impact *** this article,a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures(vintage Convolutional Neural Network,CNN;Fully Convolutional Networks,FCN;encoder-decoder,recurrent networks;attention models,and generative adversarial models).The characteristics,capabilities,and limitations of current DL models were examined,and potential research directions were discussed.

关键词： Deep Learning(DL) Satellite imaging Image classification Segmentation and object detection

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Vacancy-Type Defects in HfO2 Layers and Their Role in Amorphous-to-Crystalline Transition Studied by Monoenergetic Positron Beams

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IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Tsukuba Ibaraki305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Ikoma Nara630-0192 Japan Tokyo Electron Technology Solutions Ltd S-Technology Development Center Nirasaki Yamanashi650 Japan Tokyo Electron Technology Solutions Ltd Development Department1 Nirasaki Yamanashi407-0192 Japan Tokyo Electron Ltd Digital Design Center Sapporo Hokkaido060-0003 Japan Tokyo Electron Technology Solutions Ltd Software and Data Engineering Division Nirasaki Yamanashi407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Tsukuba Ibaraki305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Tsukuba Ibaraki305-8568 Japan

The annealing properties of vacancies in HfO2 layers deposited on Si substrates and their role in amorphous-to-crystalline transition were studied with monoenergetic positron beams. HfO2 layers with a thickness of 4.30 nm were fabricated by the atomic layer deposition technique. The major vacancytype defects in these layers were identified as a Hf vacancy (VHf) coupled with multiple oxygen vacancies (VOs) and larger vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters started to increase, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to monoclinic crystalline phases. The amorphous-crystalline transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2025 IEEE.

关键词： Hafnium oxides

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