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检索条件"任意字段=3rd International Conference on High Performance Computing in Science and Engineering, HPCSE 2017"

共 1549 条记录，以下是41-50 订阅

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Research on Continuous Variable Reasoning and Completion Method for Digital Twin Based on Improved Dynamic Discrete Bayesian Network 3

Research on Continuous Variable Reasoning and Completion Met...

引用

3rd international conference on Cloud computing, Big Data Application and Software engineering, CBASE 2024

作者： Zhang, Aoqi Liu, Yong Deng, Wei Jia, Yafei Qi, Difan Li, Gege Henan University of Science and Technology Luoyang China Huanghe University of Science and Technology Zhengzhou China

ISBN: (纸本)9798331506582

In the new generation of industrial intelligent systems, digital twin technology is a core method for simulating, predicting, and optimizing physical systems, relying on high- quality, continuous data streams to enable real-time mapping of physical entities. However, due to limitations such as insufficient device resources and communication failures, systems frequently face issues such as erroneous, missing, or delayed data, which undermine data integrity. Given the complexity of digital twin data, predominantly composed of continuous variables, the Dynamic Discrete Bayesian Network (DD-BN) has been introduced to infer and complete missing data. Yet, as data dimensionality increases, DD-BN encounters challenges like the curse of dimensionality and data sparsity, making it difficult to maintain real-time performance and accuracy. To address these issues, this paper proposes a high-Precision Data Inference and Completion Method for Continuous Variables based on an Improved Dynamic Discrete Bayesian Network (HP-DAD-BN). This approach combines Independent Component Analysis (ICA) for dimensionality reduction, spline function fitting, and Monte Carlo sampling to optimize the inference process. Simulation results show that this method significantly enhances inference accuracy, data completion performance, and real-time capabilities across both low- and high-dimensional data, demonstrating superior overall performance. © 2024 IEEE.

关键词： Dimensionality reduction

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Novel Attack Vector to Abuse AWS for Cryptojacking 3

Novel Attack Vector to Abuse AWS for Cryptojacking

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Nissar, Nahfid Arjun, S. Siddartha, Kvk Raheman, Shaik Muzamil Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350375190

This study investigates cryptojacking attacks on cloud infrastructure, focusing on hacker groups and their methods from 2017 to 2024. We analyze how attackers deploy mining software and malware in the cloud, leading to performance degradation, resource consumption, and financial loss. Typically, these attacks target specific cloud resources like AWS EC2 or Lambda. Our novel approach involves using an EC2 instance as an internal command-and-control (C2) server to coordinate the provisioning of AWS ECS, Lightsail, and EKS services for mining purposes. System specifications are exfiltrated through DNS tunneling. We propose strategies and best practices for mitigating these attacks. Victims of these attacks often face significant costs, with each dollar gained by attackers resulting in nearly 53 in electricity and compute bills. © 2024 IEEE.

关键词： Losses

来源：评论

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Face based Attendance and Behaviour Tracking 3

Face based Attendance and Behaviour Tracking

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Vijayasekaran, G. Rajalakshmi, B. Anindhitha, M. Bhavani, B. Bhavya, S. New Horizon College of Engineering Department of Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9798350375190

A high-accuracy system for multi-face detection, recognition, one of the busiest uses of image processing is face recognition, which is essential to the industry. The ability to recognize a human face is a current concern for authentication, particularly when it comes to student attendance. A highaccuracy system for multi-face detection, recognition, and behaviour tracking is challenging task in the modern industry sector. The technology uses sophisticated face identification algorithms and recognition models by utilizing datasets and deep learning. The GUI application for the same improves user interaction, ensuring strong performance for real-world applications. This project's goal is to improve attendance accuracy, simultaneously recognizing several faces at once using MTCNN (Multi-task Cascaded Convolutional Neural Networks) algorithm and documenting the data. This study investigates how deep learning approaches can help with less time-consuming attendance tracking. © 2024 IEEE.

关键词： Python

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Structural Analysis of Slitted Rotor for 10,000 rpm Switched Reluctance Machine 3

Structural Analysis of Slitted Rotor for 10,000 rpm Switched...

引用

3rd international conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Patel, Ashutosh Prasad, Kamisetti N.V. Kumar, Pramod Narayanan, G. Indian Institute of Science Department of Mechanical Engineering Bengaluru560012 India Indian Institute of Science Department of Electrical Engineering Bengaluru560012 India

ISBN: (纸本)9798350349207

Switched reluctance machines (SRMs) are increasingly being investigated for high-speed and high-temperature applications, as their rotors do not have permanent magnets or windings. Solid rotor SRMs are a viable option for high-speed and high-temperature applications. However, rotor core losses are quite high in solid rotor SRM. Recently, slitted rotor SRM has been proposed to reduce rotor core loss significantly compared to the solid rotor SRM. The slits are made in the active portion of the rotor perpendicular to the shaft axis to reduce the core losses. While this improves the electrical performance, the shaft rigidity and integrity are evaluated in this paper. Stress analysis and deformation due to the forces acting on the rotor at different speeds and load conditions are presented. The natural frequencies of the slitted rotor and corresponding mode shapes are determined using model analysis. The first four natural frequencies are 885 Hz, 2363 Hz, 3592 Hz, and 4308 Hz. Further, a comparative study between slitted and solid rotors specifically designed for a 5 kW, 10000 rpm SRM is presented, offering insights into their respective performance characteristics. © 2024 IEEE.

关键词： Natural frequencies

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Research and Implementation Based on the eTPU 3

Research and Implementation Based on the eTPU

引用

3rd international conference on Artificial Intelligence, Automation, and high-performance computing, AIAHPC 2023

作者： Wang, Ying Gao, Bo Li, Jinze Hao, Yibo Nation’s Research Laboratory of Computer Application Engineering Xi’an Aeronautics Computing Technique Research Institute AVIC Xi’an China Xi’an XinHang Talent Service Co. Ltd. Xi’an China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

As a special I/O channel in the control system, the eTPU (Enhanced Time Processing Unit) cooperates with the host computer to complete the corresponding functions. As the most foundational and important function in the eTPU, which is completed through I/O channel. This paper makes a comprehensive analysis of the thread entry and the pre-defined channel mode in the process of implementing the I/O function. Through the design of thread entry and the optimal selection of the pre-defined channel mode, the I/O function can be efficiently completed. By adopting clever design ideas and optimization, eTPU can provide more accurate and efficient functions. Especially through the implementation part of this article, the usage method of eTPU is more convenient to master. © 2023 SPIE.

关键词： Computer control systems

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Economic Model Management and Application System Development Based on Genetic Algorithm 3

Economic Model Management and Application System Development...

引用

3rd international conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Shi, Zhengbin School of Management and Engineering China Nanjing University China Jiangsu Province Nanjing City210093 China

ISBN: (纸本)9798331519032

With the rapid advancement of computer technology, people have put forward higher-level and more comprehensive needs in fields such as computer science and network communications. In this context, an economic model algorithm needs to be established to solve complex, changeable and difficult-to-handle practical problems. This article aims to analyze the information asymmetry that exists in economic management and the contradiction between manual operation and machine working modes, and also designs a set of computer system models that are universal and of high practical value, thereby transforming abstract mathematics into practices that can be applied to the real world. Afterwards, this article simulates the performance of the system model. The simulation results show that the prediction accuracy of the system is between 90% and 98%, and the error rate is within 10%;the resource utilization range is between 2% and 6%, and the production efficiency is between 90% and 93%. This shows that the system has a low resource utilization ratio and high production efficiency. © 2024 IEEE.

关键词： Economic efficiency

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User Pairing in Full-Duplex Enabled Vehicular Networks Based on Deep Reinforcement Learning 3

User Pairing in Full-Duplex Enabled Vehicular Networks Based...

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Ren, Jie Guan, Xietian Shi, Xiaosen Yang, Wenrui Han, Liang College of Electronic and Communication Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9798331530280

This paper investigates the user pairing in vehicular networks where a full-duplex (FD) base station simultaneously communicates with multiple pairs of uplink (UL) and downlink (DL) half-duplex (HD) users. Specifically, we formulate the user pairing problem to maximize the total sum rate of the system. Traditional pairing methods, characterized by high computational complexity and limited adaptability to the dynamic nature of vehicular networks, are not suitable for practical vehicular scenarios. To address this challenge, we employ deep reinforcement learning to solve the user pairing problem. Our experiments show that the proposed algorithm outperforms random pairing methods approaching optimal performance with less time consumption. © 2024 IEEE.

关键词： Deep reinforcement learning

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Risk Assessment and Prediction Based on Generalized Adaptive Backpropagation Neural Networks 3

Risk Assessment and Prediction Based on Generalized Adaptive...

引用

3rd international conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Wang, Zipan School of Transportation Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798331519032

This paper introduces a novel risk evaluation and prediction model based on the GABP neural network. Initially, a K-means clustering model categorizes events into three risk levels: high, medium, and low. Subsequently, a TOPSIS entropy weight model is developed to select various risk events and establish corresponding early-warning evaluation models. The model's effectiveness is validated through sensitivity analysis using a confusion matrix, achieving a sensitivity value of 0.938, and F1 scores above 92% across training, testing, and cross-validation datasets, with the training set reaching 93.6%. Further, the paper explores regression models including Random Forest and XGBoost, concluding that the GABP neural network optimized with a genetic algorithm exhibits superior performance, with an optimal evaluation effect at K=5 and a testing accuracy of 95.87%. These findings underline the model's robustness and high predictive accuracy in risk assessment. © 2024 IEEE.

关键词： Prediction models

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computing Power Detection Based on Smart Contract 3

Computing Power Detection Based on Smart Contract

引用

3rd international conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Liu, Shaowei Liu, Xue Long, Chengnian School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Jiangsu Automation Research Institute Lianyungang China

ISBN: (纸本)9781510688179

As blockchain technology is widely applied across various fields, the performance requirements of blockchain systems, such as throughput and transaction latency, are increasing. In this context, blockchain systems have gradually evolved to adopt a two-phase consensus protocol that involves election and block generation. By electing a small number of nodes from a large-scale network to form a committee, a more efficient internal consensus protocol can be executed, thereby improving the overall performance of the blockchain system. This paper proposes a node performance detection method based on smart contracts, which sets performance thresholds for committee nodes without compromising the decentralization of public chains. This ensures high performance, low latency, and enhances the reliability of the consensus protocol. © The Authors.

关键词： Blockchain Consensus Election performance Detection Smart Contract

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Research on the Optimization of Local Lane Segmentation in BEV Based on Dynamic Serpentine Convolution 3

Research on the Optimization of Local Lane Segmentation in B...

引用

3rd international conference on high performance computing and Communication engineering, HPCCE 2023

作者： Dong, Yinzhou Li, Ning Wang, Chao Beijing Information Science and Technology University Beijing100192 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

BEV high-definition maps play a crucial role in autonomous driving and navigation systems, where their segmentation accuracy directly affects system performance and safety. Traditional feature extraction networks, when dealing with complex BEV maps, are often limited by their fixed kernel sizes and shapes, leading to insufficient accuracy in critical tasks such as lane segmentation. This paper improves and proposes an A-HDN framework for high-definition segmentation in BEV, observing the slender and continuous features of linear structures and introducing a Dynamic Serpentine Convolution network (DSConv). This network can flexibly conform to the lane structures in BEV and learn features, while also staying close to the target structure under constraints, thus better learning the features. Additionally, a Ghost module is introduced, which allows the learning network to better preserve features without affecting model performance. Finally, experiments show that this algorithm has increased the map segmentation precision by 1.6 IoU and improved directional detection by 0.8 mAP. © 2024 SPIE.

关键词： Semantic Segmentation

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