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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Generalizing Vehicle Manoeuvre Prediction Across Diverse Datasets 2

Generalizing Vehicle Manoeuvre Prediction Across Diverse Dat...

引用

2nd IEEE International conference on Emerging Research in Computational Science, ICERCS 2024

作者： Venkatarao, Marella Reddy, Dodda Venkata Rao, S. Siva Nageswara Ul Hassan Khan, Ameen Avinash, Golla Koushik, Pendela Chenchu Narasaraopeta Engineering College Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331534950

Due to the complex nature of automotive components and sensor data, predictive maintenance is essential to ensure the reliability and safety of the vehicle. This work introduces a new predictive algorithm for automotive engine health, designed as it will provide higher accuracy and faster decisions in detecting potential engine failures, linear Through discriminant analysis, Gaussian naive edges, support vector machines, decision trees, random forests, gradient enhancement, and AdaBoost, the program displays patterns and abnormalities that may indicate impending engine *** data set undergoes extensive preprocessing steps such as standardization, handling missing values, and feature engineering to improve model performance. The evaluation criteria used include accuracy, precision, and confusion matrix, with special attention to prevent overfitting through regularization and the early stop method. In the developed model, the group method, especially stacked model 1, obtains impressive results with a model accuracy of 0.99. This high accuracy highlights the effectiveness of the ensemble approach in managing forecasts. The model's ability to deliver real-time analysis and early warning can help significantly reduce maintenance costs, prevent failures, and enhance vehicle safety, resulting in improved vehicle engine health during the maintenance process. © 2024 IEEE.

关键词： Support vector machines

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A Quantum Temporal Difference Learning Method Based on Quantum World Model

A Quantum Temporal Difference Learning Method Based on Quant...

引用

2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Zeng, Peigen He, Ying Yu, F. Richard Du, Jianbo Shenzhen University College of Computer Science and Software Engineering Shenzhen China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Carleton University School of Information Technology Ottawa Canada

ISBN: (纸本)9798350351255

Based on quantum parallelism theory and quantum phenomena such as superposition and entanglement, quantum reinforcement learning (QRL) has the potential to surpass classical reinforcement learning (RL). Although some excellent works have been done on QRL, existing RL methods encounter chanllenges when performing in environments with sparse rewards. In this paper, we provide a new perspective on conducting temporal difference (TD) learning in quantum computing, which can eliminate redundant exploration steps compared to classical methods. Specifically, we first use environment information to construct a world model with quantum circuits, enabling it to interact in a quantum way. Then, we perform the learning process by using the quantum world model and Grover's algorithm to query backwards how to reach the recorded states with high TD-errors. Simulation results show that our proposed method has superior performance compared to classical RL algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient ILT via Multigrid-Schwartz Method 24

Efficient ILT via Multigrid-Schwartz Method

引用

61st ACM/IEEE Design Automation conference, DAC 2024

作者： Sun, Shuyuan Yang, Fan Yu, Bei Shang, Li Zhou, Dian Zeng, Xuan State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China School of Microelectronics Fudan University Shanghai China China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Chinese University of Hong Kong Hong Kong University of Texas DallasTX United States

ISBN: (纸本)9798400706011

Inverse Lithography Technology (ILT) is an important Resolution Enhancement Technology (RET) in chip manufacturing. Due to the high computational demands of ILT, large-scale layouts are typically partitioned into smaller tiles for independent processing. In this paper, we propose a multigrid-Schwarz method to overcome challenges in tile assembly. Experimental results show that our approach achieves comparable performance to the full-chip ILT, offering increased parallelizability and speedup in parallel mode. Unlike the traditional divide-and-conquer algorithm, it effectively alleviates discontinuities of tile stitching, preventing manufacturing failures. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Inverse problems

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for high-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

引用

CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of conference on Lasers and Electro-Optics

作者： Qin, Jun Yang, Qipeng Han, Changhao Zhou, Yan Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Liu, Siming Li, Yueqin Sun, Jian Min, Miao Hu, Weiwei He, Zhixue Wang, Lei Yu, Shaohua Shu, Haowen Wang, Xingjun Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing100876 China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

关键词： Optical interconnects

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Multi-focus image fusion based on edge feature interaction and global reconstruction 4

Multi-focus image fusion based on edge feature interaction a...

引用

4th International conference on Signal Image Processing and Communication, ICSIPC 2024

作者： Li, Qing Huang, Man College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Transsion Communication Technology Co. Ltd. Chongqing401120 China

ISBN: (纸本)9781510682467

Multi-focus image fusion aims to synthesize key features of different images to enhance visual performance and produce high-quality images with higher resolution and finer details to enrich overall visual perception. However, existing methods mainly focus on feature extraction without paying much attention to the feature reconstruction process. In this paper, we propose a fusion method based on edge feature interaction and global reconstruction. By combining spatial attention and channel attention mechanisms, image features are effectively extracted at the encoder stage. And the edge information is detected using the canny operator, and subsequently an interactive edge feature fusion block is designed to facilitate the edge information fusion of two images detected by the canny operator. In the decoder stage, a self-attention mechanism is used for image reconstruction to further improve the quality of image fusion. Extensive experiments show that our model achieves state-of-the-art level and performs well in both subjective perception and objective evaluation. © 2024 SPIE.

关键词： Image fusion

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EP-Net: Automatic Artery/Vein Classification With Evidential Probability Map

EP-Net: Automatic Artery/Vein Classification With Evidential...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Rongchang Xu, Bo Jia, Xiaoliang Liu, Jin Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Abnormal retinal vascular morphology is commonly associated with cardiac, cerebrovascular, and systemic diseases. Hence, automated artery/vein(A/V) classification is crucial for the diagnosis of ophthalmic and systemic diseases. However, existing methods still face limitations in A/V classification and are prone to errors especially in microvessels and in noisy backgrounds. To alleviate these problems, this paper proposes an Evidence Probability Network (EP-Net) to achieve accurate A/V classification. Concretely, the multi-scale feature module in the EP-Net learns various vessel features, and the evidence probability module measures uncertainty and evidence for each pixel to overcome misclassification because of over-/under-confidence. Experiments on two public fundus image datasets demonstrate the superiority of the proposed EP-Net over state-of-the-art A/V classification methods. © 2024 IEEE.

关键词： Lung cancer

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Unmanned Aerial Vehicle Assisted Timely Data Collection in IoT Networks with Faulty Sensors 16

Unmanned Aerial Vehicle Assisted Timely Data Collection in I...

引用

16th International conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Zhang, Dongliang Gong, Jie School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9798350390643

Thanks to their low deployment costs and high flexibility, unmanned aerial vehicles (UAVs) have been widely applied for data collection in Internet of Things (IoT) networks. However, the unpredictable status updates from deployed sensor nodes (SN) and potential failures of SN can affect the freshness of information. In this paper, we investigate the UAV data collection in monitored areas with redundant SNs to alleviate the impact of potential SN failures. The SNs generate status updates at random time slots, which will be collected by UAVs and transmitted to a data center. In order to minimize the average age of synchronization (AoS) of the network and the UAV energy consumption, we formulate a Markov decision process and adopt dueling double deep Q network (D3QN) method to optimize UAV flight trajectories. Simulation results demonstrate that our method achieves better performance compared with baseline policies in environments where SN failures occur. © 2024 IEEE.

关键词： Markov processes

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YPMNet: A lightweight drowning detection algorithm 24

YPMNet: A lightweight drowning detection algorithm

引用

4th International conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Lei, Xigui Jiao, Ge Liu, Hai Hu, Yuxin College of Computer Science and Technology Hengyang Normal University Hengyang Hunan China

ISBN: (纸本)9798400710247

In recent years, there has been a rise in drowning accidents in swimming pools. As a result, there is a growing interest in using deep learning methods to detect drowning incidents. However, current research has identified several issues with existing drowning detection methods, including poor real-time performance, a high number of parameters, and extensive calculations. To address these problems, a lightweight and real-time drowning detection algorithm, YPMNet, was developed. This algorithm utilizes the MobileNetV3 lightweight network to reconstruct the YOLOPose backbone network, resulting in faster feature extraction. Additionally, the SE attention mechanism is incorporated to balance the trade-off between model accuracy and lightweight design. To further optimize the algorithm for mobile devices, the h-swish activation function is used to enhance the model's nonlinear mapping power and robustness. The experimental results demonstrate that YPMNet achieves a detection accuracy of 90% and a detection speed of 32fps, meeting the real-time requirements for drowning detection. © 2024 Copyright held by the owner/author(s).

关键词： Feature extraction

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Remodelled and Reduced complexity-OMP-based Channel Estimation Schemes for Intelligent Reflecting Surface-Aided Millimeter Wave Systems 16

Remodelled and Reduced complexity-OMP-based Channel Estimati...

引用

16th International conference on Signal Processing and Communication System, ICSPCS 2023

作者： Oyerinde, Olutayo Oyeyemi Flizikowski, Adam Marciniak, Tomasz School of Electrical and Information Engineering University of the Witwatersrand Johannesburg2050 South Africa Bydgoszcz University of Science and Technology Faculty of Telecommunications Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350333510

Intelligent reflecting surface (IRS) has been put forward as one of the key candidate technologies to achieve high spectrum efficiency. However, to reach its full potential there is a need for the availability of accurate channel state information, which is quite challenging to estimate. This paper, therefore, focuses on proposing two compressive sensing algorithm-based channel estimation schemes to be used in the IRS-aided millimeter wave (mmWave) wireless communication systems. The first channel estimation named reduced complexity-orthogonal matching pursuit (OMP) (RedCOMP)-based channel estimation scheme seeks to achieve optimum performance while reducing the computational complexity associated with the traditional OMP algorithm. The second scheme named remodelled OMP (RemOMP)-based channel estimation method aims at obtaining enhanced channel estimate with the aid of side information known a priori at the receiver. Simulation results indicate that the proposed RemOMP-based channel estimator exhibits the best performance among the three channel estimation schemes with similar computational complexity cost to the traditional OMP-base channel estimator. However, the RedCOMP-based channel estimation scheme, while not showing good performance, incurred the least computational complexity cost among all the schemes. © 2023 IEEE.

关键词： Millimeter waves

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Multi-Task Learning Enhanced Physics-Informed Neural Network for Solving Fluid-Structure Interaction Equations 7

Multi-Task Learning Enhanced Physics-Informed Neural Network...

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7th IEEE Information Technology, Networking, Electronic and Automation Control conference, ITNEC 2024

作者： Zhao, Yihe Yao, Jun Wang, Jian Xie, Xuetao Ablameyko, Sergey V. Qingdao China Qingdao China Belarusian State University Faculty of Applied Mathematics and Computer Science Minsk220030 Belarus

ISBN: (纸本)9798350370782

Physics-informed neural networks (PINNs) incorporate physical constraints into their loss functions, allowing them to efficiently solve Partial Differential Equations (PDEs). In this work, we introduce an innovative network structure based on PINNs, specifically designed to address fluid-structure interaction (FSI) problems in porous media, referred to as M-PINN. Our approach is inspired by fully coupled traditional numerical methods, leveraging a multi-task learning (MTL) framework with soft parameter sharing to enhance the interactions across different physical fields. This alignment leads to an improved representation of multi-physics coupling phenomena. Experiments on the constant pressure production problem demonstrate that our method provides better performance than both traditional and sequential PINN approaches. © 2024 IEEE.

关键词： Liquefied gases

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