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2023 international conference on Design science, ICDS 2023

作者： Jiang, Guanghong Wang, Tiesen Xiao, Sen Cui, Jieran Wu, Chunlong Wang, Lelin School of Mechanical Engineering Hebei University of technology Beichen District Tianjin China School of Economics and Management Hebei University of technology Hongqiao District Tianjin China

ISBN: (纸本)9798350343786

In order to solve the problems of the existing grass grid paving machine, which destroys the existing turf and has low paving efficiency when working reciprocally, an automatic bidirectional grass grid paving vehicle is designed in this paper. In this paper, the functions of grass grid paving are studied and analyzed, and the machinery which can lay both horizontal and vertical turf at the same time is designed. The design mainly includes the mechanical execution part and the electric control part. In the mechanical part, the slotting functions are merged, and the design and layout of the executive mechanisms are streamlined. The electronic control system can realize the functions of obstacle detection, attitude detection, alarm, stress acquisition and so on. It uses the main control panel and the display screen to facilitate man-machine interaction. The model is built for verification, which shows that the mechanism and the electronic control system are scientific and that their functions are effective. © 2023 ieee.

关键词： Control systems

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Long-Periodicity Detection from Massive Alarms in Power System 21

Long-Periodicity Detection from Massive Alarms in Power Syst...

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21st international conference on Networking, Sensing and Control, ICNSC 2024

作者： Yang, Yunfan Yang, Xinyu Huang, Hao Zhang, Shilong Wu, Qinqin Li, Yanan Gu, Zhenwei Liu, Yang Guangdong Power Grid Co. Ltd Guangdong Power Dispatching and Controlling Center Department of Cyber Security Guangzhou China Xi'an Jiaotong University Moe Klinns Lab Shaanxi China

ISBN: (纸本)9798350365221

The advent of big data and cloud computing has ushered in a new era of complexity in cybersecurity. Analysis of network security situations has emerged as a pivotal aspect of safeguarding networks. However, with the escalating volume of alarms and the enhancement of attack techniques, the task of network security analysis has grown increasingly daunting. The power system faces numerous challenges in defending its information security, including analyzing vast amounts of data, mining alarm correlations, and detecting high-risk alarms. In this paper, we propose a long-periodicity detection method for alarms over a long time span, aiming to reduce the number of false alarms. experimental results on real datasets have demonstrated that we can effectively detect long-period alarms over a long time span. This can enhance the understanding of the overall network security situation and help staff pinpoint high-risk alarms with real threats. © 2024 ieee.

关键词： Cloud security

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extending the distributed computing infrastructure of the CMS experiment with HPC resources 20

Extending the distributed computing infrastructure of the CM...

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20th international Workshop on Advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Adelman-McCarthy, J. Boccali, T. Caspart, R. Peris, A. Delgado Fischer, M. Molina, J. Flix Giffels, M. Hernandez, J. M. Hufnagel, D. Kuhn, e. Madlener, T. Mohapatra, A. K. Ozturk, H. Yzquierdo, A. Perez-Calero Spiga, D. Wissing, C. Fermilab Natl Accelerator Lab Batavia IL USA INFN Sez Pisa Largo B Pontecorvo 3 I-56127 Pisa Italy KIT Hermann von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany CIEMAT Ave Complutense 40 Madrid Spain Port Informacio Cient fica PIC Barcelona Spain DESY Notkestr 85 D-22603 Hamburg Germany Univ Wisconsin Madison Madison WI USA CERN CH-1211 Geneva 23 Switzerland INFN Sez Perugia Via A Pascoli 23c I-06123 Perugia Italy

Particle accelerators are an important tool to study the fundamental properties of elementary particles. Currently the highest energy accelerator is the LHC at CeRN, in Geneva, Switzerland. each of its four major detectors, such as the CMS detector, produces dozens of Petabytes of data per year to be analyzed by a large international collaboration. The processing is carried out on the Worldwide LHC computing grid, that spans over more than 170 compute centers around the world and is used by a number of particle physics experiments. Recently the LHC experiments were encouraged to make increasing use of HPC resources. While grid resources are homogeneous with respect to the used grid middleware, HPC installations can be very different in their setup. In order to integrate HPC resources into the highly automatized processing setups of the CMS experiment a number of challenges need to be addressed. For processing, access to primary data and metadata as well as access to the software is required. At grid sites all this is achieved via a number of services that are provided by each center. However at HPC sites many of these capabilities cannot be easily provided and have to be enabled in the user space or enabled by other means. At HPC centers there are often restrictions regarding network access to remote services, which is again a severe limitation. The paper discusses a number of solutions and recent experiences by the CMS experiment to include HPC resources in processing campaigns.

关键词： international cooperation

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grid File index in Big Data Crowdsourcing 2

Grid File index in Big Data Crowdsourcing

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2nd international conference on computing and Information Technology, ICCIT 2022

作者： Qari, Shahad Mosa Alharthi, Manal Abdulrahman Houmaidan, Leena Solayman Alqurashi, Raghad Mohammed Almalki, Ashwaq Ahmed Alarabi, Louai Umm Al-Qura University Dept. of Computer Science Mecca Saudi Arabia

ISBN: (纸本)9781665436052

The area of our research in computer science, particularly spatial computing. Spatial computing provides a very important computing technology for any geographically related applications and services. It helps in the construction of geographical websites' profiles and services. Spatial computing can be used in location identification such as google maps and have other uses and services in spatial planning, traffic congestion control, evacuation systems, crowd management, and spatial crowdsourcing. In this research, we have investigated the main component of spatial crowdsourcing. In particular, we introduced a spatial index (grid File) to a crowdsourcing platform. Next, we constructed the basic spatial operation of crowdsourcing applications. We conducted an experimental study to recommend a suitable big data framework and suitable spatial indexing for crowdsourcing applications. © 2022 ieee.

关键词： Crowdsourcing

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FPPT and Fixed-time Sliding Mode Approaches for a Single-stage grid-integrated PV System

FPPT and Fixed-time Sliding Mode Approaches for a Single-sta...

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2023 ieee international conference on energy Technologies for Future grids, eTFG 2023

作者： Hosseinabadi, Pooyan Alinaghi Ahmed, Sajib Pota, Hemanshu Mekhilef, Saad Konstantinou, Georgios Unsw Canberra School of Engineering and Information Technology Australia Universiti Malaya Power Electronics and Renewable Energy Research Laboratory Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Australia Unsw Sydney School of Electrical Engineering and Telecommunications Australia

ISBN: (纸本)9781665471640

This article introduces a flexible power point tracking (FPPT) algorithm for a single-stage grid-integrated photovoltaic (PV) system. The proposed FPPT algorithm utilizes the scanning approach, which can operate under partial shading conditions. This algorithm cooperates with the voltage controller, current controller, and phase-locked loop (PLL) to ensure extracting a predefined power (within the maximum available power) from the solar PV array and delivering the extracted power into the grid. To make this possible, the use of new forms of robust fixed-time sliding mode approaches is proposed. A chattering-free fixed-time sliding mode voltage controller is defined to track the DC-link voltage reference provided by FPPT. A simple fixed-time sliding mode current controller is designed to maintain the unity power factor and to track the current reference provided by the voltage controller. A PLL algorithm based on a novel fixed-time sliding mode approach is defined to detect the grid voltage information and provide it to the inverter controllers. Stability analysis of the closed-loop system is given using the Lyapunov theorem. The effectiveness of the suggested scheme is demonstrated using simulation results in comparison with the conventional schemes. © 2023 ieee.

关键词： Fixed-time stability FPPT grid-integrated PV Inverters PLL Sliding mode

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A Demand Side Adjustable Load Cluster Regulation Method Based on Alternating Direction Multiplier Method 8

A Demand Side Adjustable Load Cluster Regulation Method Base...

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8th international conference on Power and Renewable energy, ICPRe 2023

作者： Shao, Chong Liu, Zhiwen Li, Yan Hong, Yuejia Cheng, Ran Duan, Yao China Southern Power Grid Energy Development Research Institute GuangZhou China Guangdong Power Grid Co. Ltd. Grid Planning & Research Center GuangZhou China

ISBN: (纸本)9798350328813

On the demand side, a large number of adjustable load clusters are connected to the distribution network as diversified stakeholders. In order to reduce its electricity cost and improve the local consumption rate of new energy, it is necessary to study the fine control strategy of adjustable load cluster to guide its optimal operation when connected to the distribution network. This paper proposes a demand-side adjustable load cluster control method. Firstly, a cooperative game model of multi-adjustable load cluster is established based on Nash negotiation theory. Then, the revenue is distributed in an asymmetric bargaining manner, and the objective function is decomposed into two subproblems: alliance cost minimization and intra-cluster revenue distribution. Finally, the alternating direction method of multipliers (ADMM) is used to solve the problem iteratively. The example verification shows that the proposed method has good convergence performance and computational efficiency, gives full play to the regulation potential of the adjustable load cluster, and effectively reduces the operating cost. © 2023 ieee.

关键词： Adjustable load Alternating direction multiplier method Cluster method Distributed computing

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SMTD Co-Simulation Framework With HeLICS for Future-grid Analysis and Synthetic Measurement-Data Generation

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ieee TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第1期58卷 131-141页

作者： Bharati, Alok Kumar Ajjarapu, Venkataramana Iowa State Univ Dept Elect & Comp Engn Ames IA 50010 USA

The power grid is transforming with large amounts of distributed energy resource (DeR) integration that is impacting the bulk power system planning and operations. grid regulators have identified that traditional power system analysis techniques are challenged under the changing grid conditions. Transmission and distribution (T&D) co-simulation is an effective methodology to accurately account for the changing power-grid. A scalable multitimescale T&D (SMTD) co-simulation framework is developed with commercial transmission system solver that can handle large-scale system models, and distribution system solver that can model the distribution systems in detail with dynamic models of load and DeR. In this article, PSS/e and gridLAB-D are combined using a Python interface with Hierarchical engine for Large-scale Infrastructure Co-Simulation software to enable T&D co-simulation. This framework is scalable and parallel computing compatible that enables large-scale system simulation and adoption of T&D co-simulation by the industry and utilities. The SMTD cosimulator is designed to integrate with existing system models and new ones. The article also highlights functional specifications for the T&D co-simulation solvers and models along with providing the algorithms for the steady state, quasi-steady state, and dynamic T&D co-simulation. In context of changing power grid, future power grid planning, validation of controls under critical contingencies and validation of various wide area monitoring and controls can effectively utilize the proposed framework that can capture the T&D interactions accurately. The SMTD cosimulator can also be used to generate realistic synthetic measurement-data with noise and packet drop for uncommon grid events enabling development and validation of power system data analytics and controls.

关键词： Load modeling Power system dynamics Tools Industries Python Mathematical models Computational modeling gridlab-d hierarchical engine for large-scale infrastructure co-simulation (HeLICS) power system dynamics power system simulations PSS e python transmission and distribution (T&D) co-simulation T&D

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e-Talk: Accelerating Active Speaker Detection with Audio-Visual Fusion and edge-Cloud computing 20

E-Talk: Accelerating Active Speaker Detection with Audio-Vis...

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20th Annual ieee international conference on Sensing, Communication, and Networking (SeCON)

作者： Yu, Xiaojing Zhang, Lan Li, Xiang-yang Univ Sci & Technol China Dept Comp Sci Hefei Anhui Peoples R China Deqing Alpha Innovat Inst Huzhou Zhejiang Peoples R China

ISBN: (纸本)9798350300529

Active Speaker Detection (ASD) aims to enhance communication and interaction in various scenarios, including meetings, group discussions, and security surveillance systems. The primary objective of ASD is to identify and label the position of the main active speaker. In large-scale surveillance systems, real-time ASD can pose network congestion issues due to the extensive video data uploaded from numerous cameras. To address this challenge, we propose a collaborative edge-cloud solution called e-TALK for ASD. e-TALK leverages the simplicity of voiceprint comparison and processing, as opposed to analyzing video sequences. It utilizes voiceprint consistency as the criterion for determining if there has been a change in the active speaker. Our research focuses on evaluating the performance and computational costs of different voiceprint features and recognition models in speaker identification tasks. Additionally, e-TALK introduces a potential speaker tracking scheme for fixed-angle cameras, in conjunction with foreground extraction algorithms. Moreover, e-TALK incorporates a cloud-based high-precision facial ASD model, which utilizes historical information to determine the active speaker in real-time. We conducted experiments to evaluate the performance of our proposed solution in various scenarios and settings. The results demonstrate the effectiveness of the e-TALK approach in improving active speaker detection, highlighting its potential for practical application in surveillance systems.

关键词： active speaker detection filtering temporality-level stream

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A Short-Term electric Load Forecasting Method Based on the Fusion of Global and Local Features 9

A Short-Term Electric Load Forecasting Method Based on the F...

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9th ieee international conference on Data science in Cyberspace, DSC 2024

作者： Gu, Haozhan Tian, Hangwei Wang, Jingchun Li, Yumei Shandong University Of Arts Academy of Fine Arts China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Accurate short-term electric load forecasting is crucial for ensuring the stability and efficiency of the power grid. This study proposes an innovative dual-branch electric load forecasting model that enhances the accuracy of predictions by processing global and local features in parallel. Global features are extracted through the Savitzky-Golay filter and a linear model to capture the overall trends of the load data. Local features are identified using the Convolutional Self-Attention Module, which integrates the local perception capabilities of Convolutional neural network with the self-attention mechanism of Transformers. By employing a Weighted Feature Fusion Network, we effectively integrate global and local information to enhance the performance of load forecasting. experimental results demonstrate that the proposed model significantly outperforms existing models in predictive performance. The model presented in this study offers a new perspective on electric load forecasting and has the potential to become an effective tool for power system planning and scheduling. © 2024 ieee.

关键词： Deep learning Feature fusion Load prediction Time series data analysis

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MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive Learning Framework for Drug-Target Binding Affinity Prediction

MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive L...

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2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Huiting Zhang, Weiyu Shang, Yong Lu, Wenpeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

ISBN: (纸本)9798350386226

Drug-target binding affinity prediction is critical to drug design. Some existing computational methods rely on single-scale data and cannot fully integrate the rich information of multi-scale data, such as molecular structures and network information. In this paper, we propose the MBC-DTA model, which effectively integrates multi-scale data, and models the complex interactions between atoms and amino acids. We introduce GCN and GAT, which effectively extract the feature representation of molecular structure scale and network scale. Afterwards, the features obtained from the molecular structure scale are fed into a bilinear attention network, which captures the intricate interaction information between drug-target pairs. Additionally, a cross-scale graph contrastive learning strategy is applied, optimizing the feature representations across different scales. extensive experimental results on two benchmark datasets demonstrate that the MBC-DTA model outperforms existing state-of-the-art methods. © 2024 ieee.

关键词： Contrastive Learning

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