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5th international symposium on Big Data and Applied Statistics, ISBDAS 2022

5th International Symposium on Big Data and Applied Statisti...

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2022 5th international symposium on Big Data and Applied Statistics, ISBDAS 2022

the proceedings contain 37 papers. the topics discussed include: research on graph classification based on improved metric learning;technology strategy for meticulous governance of smart cities: memory bus width aware pruning for efficient image super-resolution implementation in FPGA;research on univariate anomaly diagnosis of gas pipeline measurement data based on random forest algorithm;a new black widow algorithm for discontinuous optimization in cloud task environment;development and application of data mining method for synthetic aperture radar image ship inspection based on big data application technology;key technologies of media big data in-depth analysis system based on 5G platform;research on monitoring data security sharing method for hydropower station operation and maintenance;and research on user granularity-level personalized social text generation technology.

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KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue generation

KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for K...

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5th international Workshop on Learning with Imbalanced Domains - theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Bai, Jiaqi Yan, Zhao Yang, Ze Yang, Jian Liang, Xinnian Guo, Hongcheng Li, Zhoujun Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Tencent Cloud AI Shenzhen Peoples R China

ISBN: (纸本)9783031434143;9783031434150

Existing knowledge-grounded conversation systems generate responses typically in a retrieve-then-generate manner. they require a large knowledge base and a strong knowledge retrieval component, which is time- and resource-consuming. In this paper, we address the challenge by leveraging the inherent knowledge encoded in the pre-trained language models (PLMs). We propose Knowledgeable Prefix Tuning (KnowPrefix-Tuning), a two-stage tuning framework, bypassing the retrieval process in a knowledge-grounded conversation system by injecting prior knowledge into the lightweight knowledge prefix. the knowledge prefix is a sequence of continuous knowledge-specific vectors that can be learned during training. In addition, we propose a novel interactive re-parameterization mechanism that allows the prefix to interact fully with the PLM during the optimization of response generation. Experimental results demonstrate that KnowPrefix-Tuning outperforms fine-tuning and other lightweight tuning approaches, and performs comparably with strong retrieval-based baselines while being 3x faster during inference (the code is available at https://***/fantast4ever/KnowPrefix-Tuning.)

关键词： Dialogue generation Parameter-efficient fine-tuning Knowledge-grounded dialogue Pre-trained language models

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Operator Partitioning and Parallel Scheduling optimization for Deep Learning Compiler 5

Operator Partitioning and Parallel Scheduling Optimization f...

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5th IEEE international Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2022

作者： Li, Zhiyu Zhou, Xiang Weng, Wenbin The 2nd Institute of China Aerospace Science & Industry Corporation Aerospace Science Intelligence Industry Development Co. Ltd Beijing China Industry Corporation Aerospace Science & Intelligence Industry Development Co. Ltd. Beijing China Minhang Branch of Shanghai Public Security Bureau Shanghai China

ISBN: (纸本)9781665471978

TVM(tensor virtual machine) as a deep learning compiler which supports the conversion of machine learning models into TVM IR(intermediate representation) and to optimise the generation of high-performance machine code for various hardware platforms. While the traditional approach is to parallelise the cyclic transformations of operators, in this paper we partition the implementation of the operators in the deep learning compiler TVM with parallel scheduling to derive a faster running time solution for the operators. An optimisation algorithm for partitioning and parallel scheduling is designed for the deep learning compiler TVM, where operators such as two-dimensional convolutions are partitioned into multiple smaller implementations and several partitioned operators are run in parallel scheduling to derive the best operator partitioning and parallel scheduling decisions by means of performance estimation. To evaluate the effectiveness of the algorithm, multiple examples of the two-dimensional convolution operator, the average pooling operator, the maximum pooling operator, and the ReLU activation operator with different input sizes were tested on the CPU platform, and the performance of these operators was experimentally shown to be improved and the operators were run speedily. © 2022 IEEE.

关键词： Deep learning

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Coupling site wide CPT profiles and genetic algorithms for whole-site offshore windfarm layout optimization 5th

Coupling site wide CPT profiles and genetic algorithms for w...

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5th international symposium on Cone Penetration Testing, CPT 2022

作者： Charles, J.A. Gourvenec, S.M. Vardy, M.E. University of Southampton Southampton United Kingdom SAND Geophysics Southampton United Kingdom

ISBN: (纸本)9781032312590

Offshore windfarm layout is driven by optimizing wind potential at the selected site subject to minimum spacing between turbines. Within these constraints, this paper explores the optimization of turbine layout within a selected site based on geotechnical site conditions. Utilizing an irregular but site-wide grid of CPT profiles, a simple pile design method and a cubic interpolation of pile dimensions, heatmaps of pile length and diameter at every possible coordinate within a site can be generated, providing the opportunity for optimization of layout. this paper demonstrates the ability to optimize the location of a prescribed number of wind turbine foundations, in conjunction with other constraints such as minimum spacing, to minimize total steel usage (a simple proxy for foundation cost and embodied carbon) in the foundation system using a genetic algorithm (GA)-based approach. Existing work on windfarm layout optimization uses such techniques to maximize wind energy generation but negligible work exists on extending the methodologies to minimize foundation costs. this paper demonstrates the viability of using a set of CPT profiles with a GA-based approach for a geotechnically-informed windfarm layout and explores the effects of various meta parameters using publicly available datasets. the work demonstrated in this paper is directly relevant to ongoing advances in geophysics and machine learning that would allow for the generation of a synthetic CPT profile at any point on the site, eliminating the drawbacks of interpolation between actual CPT profiles for design parameters. © 2022 the Author(s).

关键词： Genetic algorithms

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optimization and Development Methods for Data Asset Management in Power Systems Based on Knowledge Graphs

Optimization and Development Methods for Data Asset Manageme...

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New Energy and Electrical Technology (ISNEET), international symposium on

作者： Shuai Huang Qian Hu Yubao Li Hangzhou Electric Power Design Institute Co. Ltd Hangzhou China

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

With the increasing complexity and intelligence of modern power systems, the volume of data has grown exponentially, covering various stages from generation, transmission, and distribution to consumption. Traditional data management methods struggle to handle the vast data volume and complex data relationships, resulting in data silos and information asymmetry, which limit the efficiency and decision-making capabilities of power systems. Knowledge graphs, as an emerging data management technology, integrate data and knowledge to provide a global view of data relationships and intelligent analysis capabilities. this paper proposes an optimization method for data asset management in power systems based on knowledge graphs, discussing the construction of knowledge graphs, data processing techniques, and their application in power systems to enhance data management and intelligence levels.

关键词： Power system dynamics Decision making optimization methods Knowledge graphs Big Data Data processing Asset management Complexity theory Artificial intelligence

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Design of OMNET++ for Input-queued switching Networks

Design of OMNET++ for Input-queued switching Networks

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Computer Engineering and Intelligent Communications (ISCEIC), international symposium on

作者： Bowen Cheng Weibin Zhou School of Electronic Information and Automation Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

To evaluate the performance of iterative scheduling algorithms in input queuing and switching networks, this study constructs a specialized simulation platform using OMNET++ software suit. Adopted through a modular and object-oriented approach, the platform supports various scheduling algorithms and can accurately simulate the complete process from data generation to output, including the processing of different types of service flows. By implementing the iSLIP, DRRM, EiSLIP and EDRRM algorithms, the platform's network-level performance simulation capability is verified under different load conditions, demonstrating its potential in promoting the research of network architecture optimization and scheduling strategies, which is of great significance in promoting the theoretical and practical advancement of network technology. this platform fills the gap of existing simulation software in specific areas and provides an effective tool for the research of iterative scheduling algorithms.

关键词： Scheduling algorithms Software algorithms Switches Network architecture Data collection Iterative algorithms Software Scheduling optimization

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Multi-Objective Optimal Scheduling Technology for Honeycomb Active Distribution Networks Based on Information Entropy

Multi-Objective Optimal Scheduling Technology for Honeycomb ...

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New Energy and Electrical Technology (ISNEET), international symposium on

作者： Chi Xiao Xiaojun Zhang Jincan Shan Jianpu Man Jie Hao Dehuai Kong State Grid Weihai Power Supply Company Weihai China

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

In the context of changes to the single-source radial network structure of active distribution networks, there exists the problem of inaccurate prediction of multi-user target loads, leading to significant power scheduling errors and high energy losses. To mitigate losses in distribution network systems and enhance the scheduling capabilities of distributed generation, a multi-objective optimal scheduling technology for honeycomb active distribution networks based on information entropy is proposed. By constructing a multi-objective optimal scheduling model for honeycomb active distribution networks, information entropy is utilized to quantify the uncertainty of different scheduling schemes. A multi-objective load scheduling model for distribution networks is designed, and a self-adaptive particle swarm optimization algorithm based on distribution entropy is adopted to propose the solution method and process for the model. Experimental results show that after applying this technology, the distribution network system loss is reduced, the operational stability of the distribution network is improved, and flexible scheduling and efficient utilization of distributed generation are achieved.

关键词： Uncertainty Optimal scheduling Distribution networks Entropy Stability analysis Distributed power generation Active distribution networks Information entropy Particle swarm optimization Load modeling

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Research on Hybrid Energy Storage Configuration Method with Independent Operating Entities Participating in Frequency Regulation Auxiliary. services

Research on Hybrid Energy Storage Configuration Method with ...

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New Energy and Electrical Technology (ISNEET), international symposium on

作者： Shiyao Wei FES TU Transport Institute of Dalian Jiaotong University Dalian Jiaotong University Dalian China

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

ABSTRA CT-this article focuses on the research of energy storage configuration methods for hybrid energy storage power stations that participate in frequency regulation auxiliary services as independent operating entities. Firstly, a full life cycle cost model for a hybrid energy storage system consisting of lithium batteries and flow batteries was established, as well as a revenue model for participating in frequency regulation services as an independent energy storage power station; A method was proposed to achieve AGC(Automatic generation Control) signal decomposition and allocation by optimizing the parameters of second-order Butterworth low-pass filters; On this basis, a hybrid energy storage capacity and power optimization configuration model was established, taking into account constraints such as battery charging and discharging depth, with the goal of maximizing frequency modulation benefits. Genetic algorithm was used to achieve nonlinear solution of the objective function; Finally, taking the actual AGC frequency modulation command as an example, the optimized configuration of hybrid energy storage was achieved.

关键词： Frequency modulation Costs Low-pass filters Automatic generation control Hybrid power systems Resource management Frequency control Signal resolution optimization Energy storage

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Optimized Scheduling of Multi-Energy Coupled Virtual Power Plants for Low-Carbon Economic Operation

Optimized Scheduling of Multi-Energy Coupled Virtual Power P...

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New Energy and Electrical Technology (ISNEET), international symposium on

作者： Yang Li Yujie Huang Xinming Zheng Junwei Yang Hongzheng Xu Southern Power Grid Co. Ltd. EHV Transmission Company Dali Bureau Dali China Faculty of Transportation Engineering Kunming University of Science and Technology Kunming China Long Shine Technology Group Co. Ltd. Wuxi China

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

With the global emphasis on reducing greenhouse gas emissions, traditional power generation methods are facing challenges, highlighting the importance of dispatching distributed energy. this paper addresses the issues of resource allocation and low-carbon operational model transformation in Multi-Energy Coupled Virtual Power Plants (MECVPPs) by establishing a low-carbon economic dispatch model for MECVPPs. Furthermore, an improved Common Coot optimization Technique (COOT) is designed to solve this model. Simulation experiments validate the effectiveness and practicability of the model in terms of resource dispatching and demand-side response, providing guidance for the low-carbon economic operation and dispatch of MECVPPs.

关键词： Economics Renewable energy sources Job shop scheduling Electricity Virtual power plants Power markets Dispatching Power generation Load modeling Energy storage

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Research on Optimal Scheduling of Cross-Region Consumption of Wind Power

Research on Optimal Scheduling of Cross-Region Consumption o...

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New Energy and Electrical Technology (ISNEET), international symposium on

作者： Zijing Lu Zishou Li Zhangzhe Li Jun Tang Hongkun Chen State Grid Hubei Electric Power Co. Ltd State Grid Corporation of China Hubei China School of Electrical Engineering and Automation Wuhan University Hubei China

ISBN: (数字)9798331505905

ISBN: (纸本)9798331505912

the rapid development of wind power has brought challenges in terms of consumption and scheduling. this article proposes a research method based on optimized scheduling to address the key issues of cross-region transmission and power consumption in the development process of wind power in China. Firstly, model the uncertainty of wind power output based on wind speed probability model and wind turbine output probability model; then, an optimization scheduling model is constructed with the goal of minimizing the total system cost, combined with the layout design of wind power transmission channels, to enhance the regulation capability of the power system; Finally, taking the IEEE 39 node system as an example, the configuration effects of different external channel positions are compared and analyzed through simulation. the results show that reasonable planning of the layout of external transmission channels can significantly improve the level of wind power consumption and alleviate transmission pressure, providing theoretical support and empirical basis for cross-region transmission of wind power and optimization scheduling of large power grids.

关键词： Wind energy generation Analytical models Uncertainty Wind speed Simulation Layout Optimal scheduling Wind power generation Regulation Wind turbines

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