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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Dash, Yajnaseni Kumar, Naween Abraham, Ajith Bennett University School of Computer Science Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350383522

The digital landscape has changed in recent years due to the rise in popularity of edge computing and Content Delivery Networks (CDNs), which offer faster and more effective internet access. Edge computing, a solution to cloud computing constraints in the Internet of Things (IoT), offers scalability and smarter service delivery, but also faces challenges like power distribution and privacy protection. Conversely, CDNs enhance network performance by distributing content among cache servers near users, resulting in faster, more reliable apps and services, acting as reliable overlay networks. Edge computing and CDNs are becoming mainstream tech solutions that may improve many applications. This paper examines the advancements in both edge computing and CDNs across various disciplines, highlighting their revolutionary potential on the internet. It also investigates the intricate developments in these technologies, emphasizing their role in creating a networked world. © 2024 IEEE.

关键词： Content Delivery Networks Edge Computing Internet of Things (IoT) IoT networks

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Prediction of Time Series Data based on PCA-HMM-BN 1

Prediction of Time Series Data based on PCA-HMM-BN

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Liu, Yutong Heilongiang University of Science and Technology School of Computer and Information Engineering Harbin China

ISBN: (纸本)9798350352931

This present work describes a framework for the analysis of score dynamics and energy fluctuations in competitive environments, aimed at forecasting match outcomes and providing strategic decisions useful to coaching and players' performance. Potential energy was defined, seven key measures were identified related to Kendall's tau, shown as heat maps to outline the underlying patterns. Using the CV method and the AHP composite weighting, overall scores can determine the overall possible energy level of players. The line graph should be drawn based on the latter in order to assess volatility throughout the course of the match. Then, it carries out a derivation method for potential energy series, followed by extracting the most critical turning points in energy dynamics through the PCA-HMM-BN algorithm. Feature engineering with lag features and effect coding improves predictability. This graph presents a comparison among different machine learning models, namely BP Neural Network, XGBoost, and SVM, by showing the high performance obtained by an SVM model tuned with the ASAA algorithm, with an R-squared score of 0.99967. © 2024 IEEE.

关键词： Graph neural networks

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HACS: An Enhancement Framework for Deep Code Search Benefiting from Hard Negative Samples 36

HACS: An Enhancement Framework for Deep Code Search Benefiti...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Song, Qihong Liu, Jianxun Hu, Haize School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)1891706594

Code search aims to retrieve relevant code snippets from large code repositories based on query, promoting code reuse and enhancing software development efficiency. Deep Learning is a powerful approach for code search, in which the hard negative samples within training batches critically impact model performance. However, most existing deep code search models only randomly sample negative samples, resulting in a paucity or complete lack of hard negative samples. To address this limitation, we introduce a novel enhancement framework named HACS to optimize the composition of negative samples within batches, thus enhancing the training effectiveness of deep code search models. The core idea is to increase the count of hard negative samples within the negative samples corresponding to each query in the training batch. Specifically, HACS utilizes deep reinforcement learning techniques for sampling hard negative samples and implements vector-level mixed data augmentation strategy to generate hard negative samples. We evaluated our framework on a public dataset covering six programming languages. Experimental results reveal that HACS significantly improves the code search performance of existing models. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Deep reinforcement learning

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A Voltage-supportive Controller for Ultra-fast Electric Vehicle Chargers in Islanded DC Microgrids

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 896-906页

作者： Noushin Poursafar Seyedfoad Taghizadeh M.Jahangir Hossain Masoud Karimi-Ghartemani School of Engineering Macquarie UniversitySydneyNSW 2109Australia School of Electrical and Data Engineering University of Technology SydneySydneyNSW 2007Australia Department of Electrical and Computer Engineering Mississippi State UniversityMississippiMS 39762USA IEEE

This paper presents a controller for fast and ultrafast electric vehicle(EV)charging *** affecting the charging efficiency,the proposed controller enables the charger to provide support to the interconnection voltage to counter and damp its *** solutions are either hardware-based such as using supercapacitors and flywheels which increase the cost and bulkiness of the charging station,or software-based such as P/V droop methods which are still unable to provide a robust and strong voltage *** paper proposes an emulated supercapacitor concept in the control system of the ultra-fast EV charger in an islanded DC ***,it converts the EV from a static load to a bus voltage supportive load,leading to reduced bus voltage oscillations during single and multiple ultra-fast EV charging operations,and rides through and provides supports during extreme external *** analysis and design guidelines of the proposed controller are presented,and its effectiveness and improved performance compared with conventional techniques are shown for different case studies.

关键词： Electric vehicle(EV) charging station ultrafast charging DC microgrid voltage support capacitor emulation

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Banking credit qualification based on blockchain and zero-knowledge proofs 9

Banking credit qualification based on blockchain and zero-kn...

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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Xue, QingShui He, YiXun Shanghai Institute of Technology School of computer science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

In the context of the new era, the financing needs of enterprises grow significantly with the continuous expansion of their scale. However, in the financing process, small and micro enterprises often encounter challenges such as insufficient credit ratings, lack of working capital, difficulty in accessing information, and lack of guarantee mechanisms, while medium and large enterprises are more concerned about the security of confidential data. To address these challenges, this paper innovatively proposes a credit model based on zero-knowledge proof, ZKPrivateLoan, which combines blockchain technology with distributed zk-SNARK technology to not only efficiently generate proof of credentials of confidential data to ensure the security of data privacy, but also enhance the trust of commercial banks in the credit credentials of micro- and small enterprises. In addition, this paper also realizes the batch verification mechanism based on the ZKPrivateLoan model, which effectively reduces the verification time and blockchain uploading cost and provides a more efficient and secure solution for enterprise financing. © 2024 IEEE.

关键词： Blockchain Distributed Zero Knowledge Proof Financing Certification

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A novel method for predicting the interaction between Chinese herbal compounds and targets 3

A novel method for predicting the interaction between Chines...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Wang, Hongmei Liu, Geanqi Xu, Ming School of Computer Science & Engineering Changchun University of Technology Changchun130102 China

ISBN: (纸本)9781510681279

The active components and target points of traditional Chinese medicine are highly complex and difficult to ascertain. In recent years, computational methods have become an effective approach for predicting compound-target interactions. However, effective utilization of the topological structural features of the network remains an urgent challenge. Traditional prediction models tend to focus excessively on the relationships between nodes while overlooking the features of the edges. To address these issues, we propose a TCMCPI model to predict the interactions between herbal compounds and target points. First, we transform the CPI network into a line graph by establishing a topological structure of the edges. We then used GCN and CNN to extract features from the compounds and targets. These features are fused together to represent the nodes of the line graph, and GAT is utilized to learn the node features. Finally, we map the node features of the line graph back to the edge features in the CPI network by merging the adjacent edge features into node features. The experimental results demonstrate that TCMCPI outperforms other baseline methods in predicting interactions between herbal compounds and target proteins, and high AUROC and AUPR evaluation metrics attest to its superior predictive performance. © 2024 SPIE.

关键词： Forecasting

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Smart Classroom Power Automation: An Adaptive And Predictive Approach

Smart Classroom Power Automation: An Adaptive And Predictive...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Madhurima, Gangireddy Reddy, Panduga Venkata Jaya Srikanth Srinivasa Perumal, R. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331529833

The growing demand for energy efficiency in educational institutions has led to the exploration of smart technologies for managing power consumption. This project introduces a "Power Automation for Classrooms"system that leverages long-range RFID (Radio Frequency Identification) technology to automate the activation of classroom lights and fans based on student attendance. The system operates by detecting student entries through RFID tags without the need for manual scanning, thus ensuring a seamless and instant process. The project aims to reduce energy wastage by activating the power in stages depending on the number of students present, with specific power allocation thresholds set according to the class strength. The data on students entered, as well as the power consumption for each classroom, is stored in the cloud, allowing for real-time monitoring, analysis and supports data-driven decision-making for future energy planning. © 2025 IEEE.

关键词： Decision making

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Deep Learning Approaches for Air Quality Forecasting: An LSTM and CNN Perspective 2

Deep Learning Approaches for Air Quality Forecasting: An LST...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Lakshmi, S. Krishnamoorthy, A. Vellore Institute of Technology School of Computer Science Engineering Vellore632014 India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368109

Nowadays, air pollution has become a significant and urgent environmental concern, because of human activities, growing industries, and urbanization. Prolonged exposure to elevated levels of air pollution might result in health issues. Hence, the ability to forecast pollution levels is essential for prompt action and efficient control of air quality. PM2.5 is a notable air pollutant comprised of minuscule particles, each measuring less than 2.5 micrometers in diameter. Rising PM2.5 concentrations exacerbate the risk to human wellness. This research examines the precise forecasting of long-term results of PM2.5 pollutants. The levels of PM2.5 help decrease air pollution and prevent its adverse effects. This study proposes a hybrid CNN-LSTM approach that uses the CNN algorithm to acquire spatial characteristics and the LSTM framework to derive temporal details. Also, this model analyzes data from five highly polluted stations in Delhi, and the data was collected from 01/01/2019 to 10/12/2020 from the Central Pollution Control Board in India. Correlation coefficients between PM2.5 and various substances aid in identifying comparable pollutants and evaluating their collective health effects. To assess the efficacy of these models, it is important to measure such as Mean Square Error and Mean Absolute Error (MAE). The hybrid model exceeds the baseline models, leading to the lowest error rate. © 2024 IEEE.

关键词： Mean square error

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MUSE: Multi-Knowledge Passing on the Edges, Boosting Knowledge Graph Completion 23

MUSE: Multi-Knowledge Passing on the Edges, Boosting Knowled...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Liu, Pengjie School of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798331528041

Knowledge Graph Completion (KGC) aims to predict the missing information in the (head entity)-[relation]-(tail entity) triplet. Deep Neural Networks have achieved significant progress in the relation prediction task. However, most existing KGC methods focus on single features (e.g., entity IDs) and sub-graph aggregation, which cannot fully explore all the features in the Knowledge Graph (KG), and neglect the external semantic knowledge injection. To address these problems, we propose MUSE, a knowledgeaware reasoning model to learn a tailored embedding space in three dimensions for missing relation prediction through a multiknowledge representation learning mechanism. Our MUSE consists of three parallel components: 1) Prior Knowledge Learning for enhancing the triplets' semantic representation by finetuning BERT;2) Context Message Passing for enhancing the context messages of KG;3) Relational Path Aggregation for enhancing the path representation from the head entity to the tail entity. Our experimental results show that MUSE significantly outperforms other baselines on four public datasets, such as over 5.50% improvement in H@1 and 4.20% improvement in MRR on the NELL995 dataset. The code and all datasets will be released via https://***/NxxTGT/MUSE. © 2024 IEEE.

关键词： Knowledge graph

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