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Design of Deep Learning Algorithm in the Control System of Intelligent Inspection Robot of Substation 2

Design of Deep Learning Algorithm in the Control System of I...

引用

2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Wei, Shuangshuang Gan, Zhenzhong Fan, Chunfeng Huang, Zhuang Zhu, Zhiyong Wei, Xueshan Qin, Kai Laibin Power Supply Bureau of Guangxi Power Grid Co. Ltd. Guangxi Laibin China

ISBN: (纸本)9798350347456

With the development of the scale of the power system and the maturity of robotics, the use of inspection robots instead of manual inspection can effectively improve the efficiency of inspection and realize the intelligent management of the power grid. In this paper, a control system for substation inspection robot is designed and implemented for the substation inspection environment. First, a convolutional neural network algorithm under deep learning (DL) is proposed to extract the characteristics of the inspection robot, and then related technologies for inspection robot control are proposed, including navigation and positioning, motion control, power supply management, safety and anti-collision, and finally, it can be obtained through the test of the control system. The inspection robot has good tracking capabilities. In addition, the real trajectory is basically consistent with the set trajectory, which also shows the robustness of the system. Therefore, the use of DL to design the control system of the intelligent inspection robot of the substation is of great research value. In this paper, it is hoped that the deep learning-based convolutional neural network algorithm can be used to extract the features of inspection robots, which can effectively prove that the robots have good tracking ability and promote the power grid inspection to a certain extent. This paper provides reference value for realizing the intelligentization of power grid selection. © 2023 IEEE.

关键词： Inspection

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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Forework: A Real-Time, distributed Digital Forensic Analysis Framework 32

Forework: A Real-Time, Distributed Digital Forensic Analysis...

引用

32nd National conference with international Participation, TELECOM 2024

作者： Penmetsa, Sahil Varma University of Central Missouri Computer Information Systems and Information Technology United States

ISBN: (纸本)9798350355000

The increasing volume of data in digital forensic investigations has outpaced the capabilities of existing forensic systems. Traditional systems such as The Sleuth Kit Hadoop, OCFA, and Hansken, though capable of handling large-scale data, fail to offer real-Time analysis, which is crucial for prioritizing critical evidence in investigations. This paper addresses this gap by proposing a modern, real-Time analysis approach and introducing Forework, a proof-of-concept implementation. Forework aims to deliver real-Time, prioritized analysis of forensic artifacts, providing an interactive and scalable solution. It leverages parallel and distributed computing to manage large datasets efficiently, ensuring investigators can quickly focus on the most relevant evidence. This system represents a significant step forward in digital forensics, aligning with contemporary needs for swift and effective data analysis. © 2024 IEEE.

关键词： Computer forensics

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Improving Efficiency of Catastrophic Cascade Attacks by Combining Multi-attribute Node Influence 26

Improving Efficiency of Catastrophic Cascade Attacks by Comb...

引用

26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Winter 2023

作者： Yang, Zhirou Bai, Linting Che, Kai Wen, Pengcheng Xi'an Aeronautics Computing Technique Research Institute Avic Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350345865

Infrastructures usually suffer environmental perturbations, and most of them are low probability events with catastrophic consequences, which poses a challenge to targeted preparedness for the possibility of cascading failures. The cascading failures on networks, in which malfunctioning nodes propagate failures through the reallocation process is studied in this paper. Traditional reallocation methods do not make use of global and local information reasonably, causing uneven distribution results. To conduct cascading attacks more efficiently, we propose a new strategy by considering node influence capability during the reallocation process, termed as RP-IC. The experimental results with respect to the node influence, network robustness as well as node destructiveness have demonstrated that our reallocation strategy RP-IC yields good performance on both synthetic and real networks, which can more efficiently expedite the cascade process than other traditional approaches. © 2023 IEEE.

关键词： Complex networks

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Design and Implementation of distributed Video AI Super-Resolution Reconstruction System 26

Design and Implementation of Distributed Video AI Super-Reso...

引用

26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Winter 2023

作者： Shang, Jingtao Guo, Yangliu Zhou, Taolong Li, Wei China North Vehicle Research Institute Beijing China Communication University of China School of Computer and Cyber Sciences Beijing China China Industrial Control Systems Cyber EmergencyResponse China

ISBN: (纸本)9798350345865

This article proposes a distributed video AI super-resolution reconstruction solution based on MapReduce to address the issue of slow video super-resolution reconstruction in a single-machine environment. The solution utilizes scene detection algorithms and precise video segmentation algorithms to divide long videos into multiple video segments based on scenes. These segments are then assigned to different processing nodes for parallel processing, and finally merged to reduce the time required for video AI super-resolution reconstruction. Experimental results demonstrate that this solution effectively improves the speed of video AI super-resolution reconstruction. In addition, this article addresses the issues of low efficiency and low storage space utilization in the distributed storage system HDFS (Hadoop distributed File System) when storing small files, specifically video segments. It proposes an optimized storage solution based on HDFS and Redis. This solution merges small files and constructs a small file merging and storage model on Redis, effectively alleviating the memory pressure on the NameNode in HDFS when storing a massive number of small files. Experimental results demonstrate that this solution sacrifices a certain degree of read/write performance but mitigates the rapid growth of NameNode memory. This solution holds significant theoretical and practical significance. © 2023 IEEE.

关键词： Image segmentation

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A Short-Term Regional Net Load Prediction Method Based on parallel Fragment Attention-BI-LSTM 6

A Short-Term Regional Net Load Prediction Method Based on Pa...

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6th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Shi, Puxin Geng, Lin Zhang, Mengfan Xu, Danlei Li, Haikun North China Branch of State Grid Corporation of China Beijing China Beijing Tsingsoft Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350305623

With the increasing proportion of new energy sources on the user side such as distributed photovoltaics being connected to the grid, the difficulty in predicting the net load of the distribution network continues to intensify, making it difficult to ensure the source load balance and safe and stable operation of the grid. Therefore, a regional short-term net load prediction neural network model based on dual periodic attention mechanism and bidirectional short-term and short-term memory network is proposed to address the dual periodic fluctuations in photovoltaic power and user load implied in the net load. Firstly, a periodic attention neural network model with temporal segment similarity as attention weight was established. Then, the two types of information are independently encoded through a parallel attention mechanism to extract the periodic characteristics of photovoltaic power and user load fluctuations. Finally, the residual connection mechanism and Bi-LSTM are used for temporal decoding to obtain the net load prediction results. Case analysis shows that the proposed method has an average improvement of 1.84% in prediction accuracy compared to traditional methods. © 2023 IEEE.

关键词： deep learning distributed photovoltaics net load

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Graph Based Quantitative Analysis of distributed Generation Impact on Regional Carbon Emissions 7

Graph Based Quantitative Analysis of Distributed Generation ...

引用

7th international conference on Smart grid and Smart Cities, ICSGSC 2023

作者： Huang, Wenqi Tang, Yachen Dai, Zhen Liu, Guangyi Hou, Jiaxuan Li, Xuanang Guangdong China Envision Digital Redwood CityCA United States

ISBN: (纸本)9798350305913

Accurately accounting for carbon emissions is a fundamental requirement for understanding the trends in carbon emissions in the power system, effectively implementing carbon reduction measures, and promoting the green transformation of the economy. This paper proposed a method for the locally balanced calculation of carbon intensity at regional power grid nodes, based on the concept of regional carbon flow balance, considering distributed generation in the computation, and following the principle of regional generation prioritizing local consumption. The paper defined the net generation, load, and intermediate nodes in the regional power grid. Using graph databases and graph computing, the method achieved local aggregation and local prioritization to calculate the carbon intensity of end nodes (net generation and net load nodes) and intermediate nodes. The proposed method was validated using actual data from a provincial power grid and compared with the results obtained from existing methods, allowing for analysis of the reasons for changes in regional carbon intensity. © 2023 IEEE.

关键词： Electric loads

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Combining distributed and Central Control for Self-Adaptive Systems of Systems 42

Combining Distributed and Central Control for Self-Adaptive ...

引用

42nd IEEE international conference on distributed computing Systems (ICDCS)

作者： Kroeher, Christian Gerling, Lea Schmid, Klaus Univ Hildesheim Inst Comp Sci Hildesheim Germany

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

distributed and central control are two complementary paradigms to establish self-adaptation in software systems. Both approaches have their individual benefits and drawbacks, which lead to significant trade-offs regarding certain software qualities when designing such systems. The significance of these trade-offs even increases the more complex the target system becomes. In this paper, we present our work-in-progress towards an integrated control approach, which aims at providing the best of both control paradigms. We present the basic concepts of this multi-paradigm approach and outline its inherent support for complex system hierarchies. Further, we illustrate the vision of our approach using application scenarios from the smart energy grid as an example for self-adaptive systems of systems.

关键词： Control design self-adaptation self-adaptive system system of systems emergent systems

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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Single Matrix Block Shift (SMBS) Dense Matrix Multiplication Algorithm 45th

Single Matrix Block Shift (SMBS) Dense Matrix Multiplication...

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45th Annual conference of the South-African-Institute-of-Computer-Scientists-and-Information-Technologists (SAICSIT) on Human- Machine-Digital-Convergence

作者： Ohene-Kwofie, Daniel Hazelhurst, Scott Univ Witwatersrand Sch Elect & Informat Engn Johannesburg South Africa Univ Witwatersrand Sch Publ Hlth Johannesburg South Africa Univ Witwatersrand Sydney Brenner Inst Mol Biosci Johannesburg South Africa

ISBN: (纸本)9783031648809;9783031648816

Many scientific and numeric computations rely on matrix-matrix multiplication as a fundamental component of their algorithms. It constitutes the building block in many matrix operations used in numeric solvers and graph theory problems. Several algorithms have been proposed and implemented for matrix-matrix multiplication, especially, for distributed-memory systems, and these have been greatly studied. In particular, the Cannon's algorithm has been implemented for distributed-memory systems, mostly since the memory needs remain constant and are not influenced by the number of processors employed. The algorithm, however, involves block shifting of both matrices being multiplied. This paper presents a similar block-oriented parallel algorithm for matrix-matrix multiplication on a 2-dimensional processor grid, but with block shifting restricted to only one of the matrices. We refer to this as the Single Matrix Block Shift (SMBS) algorithm. The algorithm, we propose, is a variant of the Cannon's algorithm on distributed architectures and improves upon the performance complexity of the Cannon and SRUMMA algorithms. We present analytic as well as experimental comparative results of our algorithm with the standard Cannon's algorithm on 2-dimensional processor grids, showing over 4X performance improvement.

关键词： parallel algorithms Matrix multiplication Computational efficiency Scalability Speedup

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