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Data-driven Industrial enterprise Order Scheduling and Optimization 6

Data-driven Industrial Enterprise Order Scheduling and Optim...

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6th international conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Xu, Sen Lin, Rongheng Wang, Baigen Wang, Ou Zhao, Qian Liu, Huizhou School of Computer Science Beijing University of Posts and Telecommunications State Key l Lab of Networking and Switching Technology Beijing China Anqing Power Supply Company State Grid Anhui Electric Power Co. Ltd. Anqing China State Grid Anhui Electric Power Co. Ltd. Hefei China

ISBN: (纸本)9798331541750

As China's industrial enterprises continue to grow and competition intensifies, efficient order scheduling and management have become essential to operational success. Many enterprises face challenges such as dispersed and unstandardized order data, reliance on manual scheduling, and limited data analysis capabilities. This study leverages priority-based and genetic algorithms, supported by extensive original fixed data (e.g., reservation order data, raw material supply data, production process data) and system-generated production data (e.g., sub-order data, raw material demand data), to design an optimized order scheduling method. The proposed algorithm not only meets order acceptance and scheduling requirements but also improves the rationality of order scheduling and the effectiveness of resource allocation. By reducing the rate of incomplete orders caused by raw material supply constraints and tight delivery times, this algorithm offers industrial enterprises a standardized production process, thereby enhancing overall operational efficiency and competitiveness. © 2024 ieee.

关键词： genetic algorithm order scheduling priority

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A machine learning model develops the electrical energy consumption and costs for charging eVs through the grid 2

A machine learning model develops the electrical energy cons...

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2nd ieee international conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Neeraja, B. Singh, Ravinjit Panda, Somesubhra Kumar, Sumit Singh, Pavitar Parkash Government Polytechnic Hyderabad Department of Electrical and Electronics Engineering Hyderabad India Malaysia Global Institute of Science & Technology Department of Electrical Engineering West Bengal Haldia India Hi-Tech Institute of Engineerig & Technology Department of Electrical Engineering Ghaziabad India Lovely Professional University Department of Management India

ISBN: (纸本)9798350315905

Sustainable energy sources and efficient energy management methods are essential for carbon-neutral construction. Utilizing electric car capability is one option when sources cannot meet demand. Investment in charging facilities and electric cars is a necessary first step towards expanding vehicle-to-grid (V2G) technology, as is the acceptance that it is effective. The proprietor of an electronic car will profit financially from installing a recharge station, and the contractor will receive a reward. This report details a successful V2G approach designed and deployed on a fully functional college campus. In addition, a machine learning method has been developed to estimate future energy use and expenses for the studied structure. The created algorithm's precision in estimating energy usage ranged from 93% to 97%, with an average mistake in expense estimates of less than 7%. The findings indicate a 37% reduction in energy usage, with a possibility for savings of 68% if consistently the plan is implemented, demonstrating that the machine learning method helps lower emissions. © 2023 ieee.

关键词： Machine learning

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Alzheimer's Disease Detection Model Based on Multimodal Data early Fusion of Medical Neuroimaging 25

Alzheimer's Disease Detection Model Based on Multimodal Data...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data science and Systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Shangxiao Qin, Sheng Wang, Kaiyang Yang, Su Zhang, Shiqi School of Electronic and Information Engineering Guangxi Normal University Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guilin China International Joint Audit Institute Nanjing Audit University Nanjing China Swansea University Department of Computer Science Swansea United Kingdom

ISBN: (纸本)9798350330014

Alzheimer's disease (AD) is a long-term neurodegenerative disease. The research in the field of AD is rapidly devel-oping, but most research methods are based on unimodal without considering the complementarity between different modes. In this work, a multimodal-based deep learning model is proposed for AD detection. Specifically, two medical neural image modes (i.e. magnetic resonance imaging and Positron emission computed to-mography) are used to fuse into a feature map. The convolutional block attention modules and convolutional neural networks are utilized to extract the features. A multilayer perceptron is used as a model classifier for the diagnostic classification of the learned AD features. To validate the model, experiments are conducted using the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with existing AD diagnostic models, this work is better, and the accuracy of this model reaches 99.17%. It provides an optional solution for AD detection. © 2023 ieee.

关键词： Magnetic resonance imaging

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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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Research on Smart grid Information Security System Based on Computer GIS Wide Area Information Big Data

Research on Smart Grid Information Security System Based on ...

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2023 international conference on Internet of Things, Robotics and Distributed computing, ICIRDC 2023

作者： Lu, Xiaofen Wang, Zhiyong Liu, Fuyan Wang, Yuhang Economic Research Institute of State Grid Zhejiang Electric Power Company Hangzhou China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang Huayun Information Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9798350375275

This paper proposes a smart grid information security system based on GIS wide area information and domestic SM2 cryptographic system. The problems of SM2 algorithm in security platform are studied and a component-based cryptographic channel construction method is proposed. DS18B20 is used to measure the electric energy and extract the clock signal to collect the electric energy signal. In order to ensure the safety of the pulse power supply, the voltage and current of the power supply are protected. Through video connection to achieve monitoring interaction and collaboration, so as to achieve remote control. Use the SM2 password security element to determine user access to resources. The results show that the platform can realize seamless integration among the applications of power secondary system and form an organic whole among them. In this way, the centralized management and control of user identity and access rights are realized. © 2023 ieee.

关键词： Smart power grids

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Multi-Vision Services Acceleration Framework for IoT Devices

Multi-Vision Services Acceleration Framework for IoT Devices

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ieee international conference on Web Services (ieee ICWS)

作者： Xie, Jianhang Ding, Chuntao Ren, Shenyuan Li, Yidong Key Lab Big Data Artificial Intelligence Transpor Minist Educ Beijing Peoples R China Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China

ISBN: (纸本)9798350368567;9798350368550

Deploying a single deep neural network (DNN) model on ubiquitous Internet of Things (IoT) devices to provide multiple vision services (e.g., semantic segmentation service, facial attribute recognition service) has attracted significant interest from industry and academia. However, the most relevant studies have two limitations: (i) the multiple services DNN commonly has low performance due to the gradient interference between different vision services;(ii) the multiple services DNN has low inference speed on IoT devices. To this end, this paper introduces a multiple vision services acceleration framework for IoT devices. The proposed MCBNet contains a novel multi-controllable branching structure to control different services explicitly for improving multiple services performance and a service balance layer with the per-channel trainable masks in specific branches for mitigating gradient interference. In deployment, the proposed framework converts the multi-branch topology MCBNet model into the plain topology Rep-MCBNet model by re-parameterization technique for accelerating services inference speed. experimental results on the CelebA and Cityscapes datasets show that our proposed framework outperforms the state-of-the-art approaches in terms of performance and inference speed. In particular, we deploy the Rep-MCBNet on the laptop with Intel mobile processors demonstrating that the proposed framework achieves 1.9x faster processing frames per second than eTR-NLP, with 1.5x fewer parameters and 1.3x fewer floating-point operations.

关键词： Service computing multiple vision services DNN IoT devices re-parameterization

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Distributed Standardized Data Sharing Scheme Based on Blockchain for Power System 11

Distributed Standardized Data Sharing Scheme Based on Blockc...

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11th international conference on Information Systems and computing Technology, ISCTech 2023

作者： Liang, Jinzhao Chen, Haomin Ma, Yun Digital Grid Group Co. Ltd China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350342406

The security of the traditional data sharing scheme is generally based on the trust of both sides, less consideration of security, and generally need a trusted party for scheduling. However, in the standardization process of power information system, participating users are mutually distrusting, and it is difficult to balance decentralization, efficiency, cost and verifiability requirements. To address these challenges, we propose a novel data sharing architecture. The architecture of this paper can meet the key characteristics of distributed data sharing, namely decentralization, trust flow, supervision, and can realize the necessary security requirements of standardized data sharing. In addition, we deploy the scheme and implemented in two test networks of the well-known blockchain ethereum. The results show that our scheme has better operating efficiency and stability, and is suitable for practical application. © 2023 ieee.

关键词： Standardization

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Unsupervised Machine Learning Techniques to Categorize Genomic Islands

Unsupervised Machine Learning Techniques to Categorize Genom...

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ieee international conference on electro Information Technology (eIT)

作者： Ghaffari, Noushin Zhou, Lijie Nazara, Rabeya Mageeney, Catherine M. Williams, Kelly P. Prairie View A&M Univ Dept Comp Sci Roy G Perry Coll Engn Prairie View TX 77446 USA Sandia Natl Labs Livermore CA 94550 USA

ISBN: (纸本)9798350330656;9798350330649

unsupervised Machine Learning (ML) techniques are powerful tools in identifying similarity patterns and can be utilized to categorize the data into related groups. This study showcases the applications of unsupervised methods, namely hierarchical clustering to carefully determine related groups of newly identified genomic islands. Genomic islands (GIs) are mobile genetic elements integrated into bacterial chromosomes. GIs can impact evolution of bacteria for example by carrying virulence or metabolic genes [1]. Precisely identifying GIs calls for a sophisticated process which we have recently implemented as an already published tool called TIGeR which stands for Target / Integrative Genetic element Retriever (TIGeR). TIGeR identifies mobile DNAs in each genome and identifies genomic islands with high accuracy [2]. We have employed TIGeR to identify approximately 130,000 GIs in e. coli bacteria. To identify the similarities among the e. coli GIs, the hierarchical clustering algorithms and our heuristics have successfully categorized the data into relevant groups. We have been able to identify related groups of GIs as well as singleton GIs. Our results provide a promising method for categorizing large DNA segments that can be compared using a similarity measure and be categorized into more precise clusters for further analysis.

关键词： bioinformatics unsupervised learning phylogenic trees genomic islands high-performance computing

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Chinese Named entity Recognition Based on grid Tagging and Semantic Segmentation 9

Chinese Named Entity Recognition Based on Grid Tagging and S...

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9th ieee international conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Zhao, Xuedong Shi, Zhiliang Xiang, Yan Ren, Ying Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China Kunming Enersun Technology Co. Ltd. Kunming650217 China

ISBN: (纸本)9798350304428

Chinese Named entity Recognition (CNeR) is one of the fundamental tasks in Chinese Natural Language Processing and has been extensively researched. However, previous studies have primarily focused on specific types of CNeR subtasks, which hindered the overall progress and practical implementation of CNeR. Recently, grid-tagging-based methods have emerged as having significant advantages in information extraction tasks due to the flexible annotation framework and model architecture they provide. Building on this, we unify the CNeR task as a character pair relation classification problem and solve it by predicting the character pair relation matrix, which is similar to the semantic segmentation task in computer vision. To do this, we utilize the U-shaped segmentation module to capture low-level and high-level features in the feature map of image style, enabling us to extract more semantic information. We also effectively model the boundary information of the entity, which in turn enhances the overall accuracy of entity recognition. To test the effectiveness of our method, we conduct experiments on two NeR datasets containing flat and nested entities in the Chinese medical domain (CMeeeV2, MMC). Our results demonstrate that our approach improves the F1 score by 0.73% and 1.07% respectively compared to current state-of-the-art models, further validating its effectiveness. © 2023 ieee.

关键词： Semantic Segmentation

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Power grid Data Monitoring and Analysis System based on edge computing 7

Power Grid Data Monitoring and Analysis System based on Edge...

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7th ieee international conference on Smart Cloud, SmartCloud 2022

作者： Wang, Tianyou Qin, Yuanze Huang, Yu Lou, Yiwei Xu, Chongyou Chen, Lei Peking University National Engineering Research Center for Software Engineering Beijing China State Grid Ningbo Electric Power Supply Company Ningbo China State Grid Zhejiang Electric Power Co. LTD. Hangzhou China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

With the continuous accumulation of large-scale power grid data, the traditional centralized data analysis method is more and more expensive for data transmission. Based on this, we designed a grid big data monitoring and analysis system and transferred the computation process to the edge node close to the data source through an edge computing strategy. On the one hand, data processing and data analysis algorithms are encapsulated by container technology, and the algorithm is mirrored to the edge nodes of the power network through the system to complete the computation. On the other hand, the computing clusters are deployed at the edge nodes of the power network, which is responsible for the scheduling, execution, and status monitoring of computing tasks. computing tasks can be flexibly managed in a cluster by extending user-defined resources. Through the reserved parameters, users can intervene in task execution policies, and tasks can be configured. The edge node sends the calculation result or early warning information to the central monitoring service through the asynchronous message. Compared with the traditional centralized data analysis system, the proposed method relieves the problem of the overhead of massive data transmission in the network, reduces the application cost, helps to apply the data analysis to more edge side nodes, and fully excavates the potential value of grid data. © 2022 ieee.

关键词： edge computing

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