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

共 10372 条记录，以下是461-470 订阅

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Wind Speed Forecasting of high Plateau Airport Based on Informer 6

Wind Speed Forecasting of High Plateau Airport Based on Info...

引用

6th International conference on Software Engineering and computer Science, CSECS 2023

作者： Zhang, Tianhang Hu, ShanShan Xiang, Zheng Zhang, Lijuan Kuang, Yinli He, Yucong College of Air Traffic Management Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9798350306378

The wind is a critical meterological factor that affects flight operations. Accurate medium-to-short-term wind speed forecasting will contribute to an efficient flight departure system, which is significant to civil aviation industry. In this paper, a novel model based on Informer algorithm is proposed to improve the capacity of wind speed forecasting in high plateau airport. The proposed method can achieve more remarkable performance than traditional algorithms due to the multidimensional time-scale relationship between meteorological factors can be captured more precisely by Informer algorithm. To estimate the performance of the proposed model, several state-of-the-art algorithms for time-series forecasting are conducted as benchmarks. Experiment result demonstrate that the proposed model shows the optimal effect in terms of the mean square error, mean absolute error, root mean square error and mean absolute percentage error for each forecasting horizon. © 2023 IEEE.

关键词： Airports

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A Deep Learning-Based Method for Drivers’ Shoe-Wearing Recognition 13th

A Deep Learning-Based Method for Drivers’ Shoe-Wearing Reco...

引用

13th International conference on Sensor Systems and Software, S-Cube 2022

作者： Hu, Baoyue Hu, Xing University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9783031348983

What types of shoes that the driver should be wear on driving is under the clear regulation. Non-standard shoe-wearing such as wearing high-heeled shoes, platform shoes, slippers or with bare feet will bring great safety risks and may lead to traffic accidents. According to statistics by traffic department, many traffic accidents are caused by irregular shoe-wearing. Although computer vision has been applied for monitoring driver’s behavior in many aspects, such as driver’s face, eye, and hand, there is still no computer vision-based method or hardware device on the vehicle that to monitor the driver’s shoe-wearing. Therefore, if the driver’s illegal shoe-wearing behavior can be identified before driving, it can play an important role in reducing the incidence of traffic accident. The main difficulties in drivers’ shoe-wearing detection lie in the diversity of shoe types, the variety of foot postures, and the complexity of the detection environment. Therefore, the traditional computer vision method will be high error detection rate in such scenes. In this paper, a deep learning-based method for detecting abnormal shoe-wearing of drivers before driving is proposed. Different models such as SVM, Fast-RCNN, Faster-RCNN, YOLO v2, YOLO v3, YOLO v4 are used to identified drivers’ shoe-wearing. To the best of our knowledge this is the first work that using the computer vision technology for automatic monitoring on drivers’ shoe-wearing. The experimental results show the proposed method can identify the drivers’ shoe wearing effectively and efficiently. It has high application value for improving traffic safety. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： computer vision

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Data Generation for Photovoltaic Systems: Enhancing Accuracy with the Huld Model

Data Generation for Photovoltaic Systems: Enhancing Accuracy...

引用

2024 IEEE International conference on Big Data, BigData 2024

作者： Ardebili, Ali Aghazadeh Livera, Andreas Georghiou, George E. Longo, Antonella Ficarella, Antonio HSPI Dept. of Research and Development Rome Italy University of Salento CRISR Dept. of Engineering for Innovation Lecce Italy University of Cyprus PV Technology Laboratory FOSS Research Centre for Sustainable Energy Dept. of Electrical and Computer Engineering Nicosia1678 Cyprus

ISBN: (纸本)9798350362480

Gaps in current numerical models highlight challenges in accurately simulating photovoltaic (PV) systems, particularly their sensitivity to weather forecast outputs and the impact of environmental factors such as wind speed and humidity. These factors introduce discrepancies between simulated and real-life PV system performance. This study investigates the potential of open-source PV physical models, leveraging pvlib-python, to address these challenges. The research focuses on enhancing the accuracy of these models to generate synthetic data that closely replicate real-world conditions. Using the single diode equation implemented in pvlib-python, DC power production data from a real-life PV module is simulated, with initial discrepancies identified and mitigated through the integration of Huld model. This integration aims to improve the accuracy of synthetic data, thereby bridging the gap between simulated and actual performance metrics. The efficacy of the enhanced predictive models is evaluated using historical field measurements from a PV system testbed, demonstrating advancements in simulating PV system behaviors with enhanced accuracy and reliability. The results unveil a significant enhancement in synthetic data generation using PVLib and Huld model. © 2024 IEEE.

关键词： Accuracy Enhancement Digital Twin Energy Simulation Huld Model performance Modeling Photovoltaic Systems Pvlib Python Renewable Energy Synthetic Data Generation

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GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information 17th

GeoRefineNet: A Multistage Framework for Enhanced Cephalome...

引用

17th Asian conference on computer Vision, ACCV 2024

作者： Viriyasaranon, Thanaporn Ma, Serie Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9789819609000

The precise detection of cephalometric landmarks on two-dimensional (2D) radiographs or three-dimensional (3D) computed tomography (CT) images is a fundamental step in various medical fields, especially in research on orthodontics and maxillofacial surgery. Deep learning-based detectors have demonstrated remarkable accuracy in 2D cephalometric analysis, whereas conventional single-view approaches are limited by their reliance on information from a single perspective. This study proposes GeoRefineNet, a novel multistage framework that leverages information from multiple CT scans acquired at various angles. By incorporating geometric knowledge through a 3D heatmap reconstruction process, GeoRefineNet improves robustness, accuracy, and adaptability to various cephalometric configurations. The proposed framework predicts 3D landmark positions on CT images, effectively addressing challenges associated with high-dimensional input data and limited training examples. GeoRefineNet surpasses the existing state-of-the-art models in the 2D and 3D domains, as demonstrated by its superior performance on numerical and clinical datasets. These findings indicate that GeoRefineNet offers a promising avenue for improving the accuracy and reliability of cephalometric landmark detection fostering further advances in clinical diagnosis and treatment planning. Our code is available at https://***/Thanaporn09/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computerized tomography

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HCCL: Hierarchical Channels and Contrastive Learning for Drug-Gene Multi-Relation Prediction

HCCL: Hierarchical Channels and Contrastive Learning for Dru...

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2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shang, Yizhe Chen, Jianrui Lei, Xiujuan Wu, Fang-Xiang Shaanxi Normal University School of Computer Science Xi'an China University of Saskatchewan Department of Mechanical Engineering Division of Biomedical Engineering Department of Computer Science SaskatoonS7N 5A9 Canada

ISBN: (纸本)9798350386226

Drug-gene interaction plays a crucial role in drug discovery and personalized medicine. Although existing methods have improved the accuracy of exploring multiple relationships between drugs and genes, there are still some limitations, such as susceptibility to data sparsity and poor generalization, which pose some challenges for practical applications. To address these challenges, we propose a novel Hierarchical Channels and Contrastive Learning (HCCL) framework in which drug feature extractor captures structural information of drug molecules from atom and bond channels. After obtaining the initial features of drugs and genes, we employ high-low-order channels to update them, where the low-order channel adopts graph convolutional networks while the high-order channel leverages hypergraph structures for message propagation. Finally, we adopt contrastive learning and inter-channel attention to fuse high-low-order features, which improves the robustness of the model and prevents feature information loss. Experimental results demonstrate the superior performance of HCCL. © 2024 IEEE.

关键词： Contrastive Learning

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Fast multilevel B-spline approximation for scattered data on GPU architecture using CUDA 3

Fast multilevel B-spline approximation for scattered data on...

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3rd International conference on computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Xu, Luyan Huang, Qi College of Computer Science and Technology Jilin University Changchun130015 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781510671720

This paper presents a novel algorithm for fast approximating scattered data, based on leveraging the parallelism of Graphics Processing Unit (GPU) to achieve high performance. The algorithm is designed to transform all key steps of computation into the corresponding CUDA kernel functions, which allows for the simultaneous execution of thousands of threads. We also employ powerful optimization techniques in our implementation to avoid atomic operations and to enhance program efficiency. Experimental results show that our algorithm achieves significant speedup comparing with conventional CPU-based solutions. Furthermore, using adaptive spatial subdivision, we extend our algorithm to handle large scale datasets, and it has good adaptability even for arbitrary datasets with highly varying data densities. © 2023 SPIE.

关键词： Graphics processing unit

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A Computational Model for Interactive Visualization of high-performance Computations 9th

A Computational Model for Interactive Visualization of High-...

引用

9th Russian Supercomputing Days International conference, RuSCDays 2023

作者： Vasev, Pavel N.N. Krasovskii Institute of Mathematics and Mechanics of the Ural Branch of the Russian Academy of Sciences Ekaterinburg Russia

ISBN: (纸本)9783031494345

Interactive visualization of high-performance computations is important area in supercomputing. Interactivity assumes that visualization of results of computation is generated during computation process. However there is a problem: due to overwhelming size of data to visualize, a visualization program should be itself parallel and executed on supercomputer. Beside that, such program should allow to be changed dynamically, because visualization pipeline may change due to user steering of interactive visualization. Current mainstream frameworks for interaction with supercomputer programs assume usage of external parallel programming methods. In current paper, an original parallel programming model is suggested that have built-in capabilities for online interactive visualization. At basic level, it is based on messages and reactions. At higher level it uses promises for inter-operation of computation and visualization parts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Visualization

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Conversion of a MATLAB Code to Python: Increasing Accessibility to Surface Plasmon Resonance Biosensing Studies

Conversion of a MATLAB Code to Python: Increasing Accessibil...

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International conference on Advances in Signal Processing and Communication Engineering, ICASPACE 2023

作者： Phillips, Jonathan Gollapalli, Ravi Terwilliger, Mark Heinrich, Sidney Department of Engineering and Industrial Professions University of North Alabama 1 Harrison Plaza FlorenceAL35632 United States Department of Computer Science and Information Systems University of North Alabama 1 Harrison Plaza FlorenceAL35632 United States

ISBN: (纸本)9789819705610

In this paper, a Python coded simulation of a surface plasmon resonance (SPR) biosensor that considers the application of electrical bias across the sensor surface is compared to the same simulation written in MATLAB. MATLAB is a high-level, expensive, and hardware demanding programming language that is commonly used in the data science domain due to its efficiency and convenience in calculation-heavy programming. However, Python is a high-level, free-to-use, popular, and extremely accessible programming language that is comparatively less demanding of hardware. The goal of this comparison was aimed at investigating the process of converting the MATLAB program to Python and determining the advantages and disadvantages of the Python simulation to determine the feasibility of performing the simulation on a more accessible computing device such as a Raspberry Pi. The conversion process included manual translation and translation performed by artificial intelligence. Upon completion of the conversion, the MATLAB simulation dramatically outperformed the Python counterpart by completing the calculations in times between 30 and 50 min;compared to Python’s completion times between 90 and 120 min. Ultimately, it was determined that while MATLAB has many disadvantages involving costs, with the use of dynamic translation it is able to execute the simulation program much more efficiently than the Python program. Therefore, economic and performance factors must be balanced for the subject utilizing the program. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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LCLB: Low Cross-Chain Transaction Ratio and Load Balancing for Blockchain Sharding 6

LCLB: Low Cross-Chain Transaction Ratio and Load Balancing f...

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6th International conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wang, Yu Wu, Xiaoming Yuan, Qile Dong, Yunfeng Liu, Xiangzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798331541699

As blockchain technology becomes widely applied, handling high volumes of transactions has become a critical challenge. Sharding has emerged as an effective solution to enhance the throughput of blockchain systems. Yet, this approach introduces challenges related to the frequency of cross-shard transactions and workload balancing. high rates of cross-shard transactions can slow overall processing speed and reduce throughput, while uneven load distribution can overburden some shards and underutilize others, impacting overall efficiency. To address these issues, this paper proposes a sharding method named LCLB. This method employs the concept of label propagation to pre-shard accounts with frequent transactions into a designated shard, ensuring a lower ratio of cross-shard transactions. It also uses an elastic load balancing algorithm to dynamically adjust the distribution of accounts, ensuring a relatively balanced processing load across all shards. Our results indicate that LCLB significantly enhances blockchain sharding performance,not only enhancing transaction throughput but also significantly reducing transaction confirmation latency, while ensuring a balanced load across various shards. © 2024 IEEE.

关键词： Blockchain

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SW-TRRM: Parallel Optimization Research of the Random Ray Method Based on Sunway Bluelight II Supercomputer 23rd

SW-TRRM: Parallel Optimization Research of the Random Ray Me...

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23rd International conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Ren, Zenghui Liu, Tao Liu, Zhaoyuan Guo, Ying Pan, Jingshan Zhao, Dawei Wu, Xiaoming Yang, Meihong Qilu Univ Technol Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Shandong Acad Sci Jinan Peoples R China

ISBN: (纸本)9789819708079;9789819708086

The Random Ray Method (TRRM) is a new approach to solving partial differential equations (PDEs) based on the method of characteristics (MOC). It employs stochastic rather than deterministic discretization of characteristic tracks and can be used for the numerical simulation of nuclear reactors. In this paper, we propose SW-TRRM, a parallel optimization program for TRRM based on the Sunway Bluelight II Supercomputer for the first time. We present a two-level parallelization scheme that consists of thread-level and process-level optimization. At the thread-level, we introduce three schemes for speeding up within a single core group, including direct parallelization, parallelization by energy groups, and loop structure optimization. At the process-level, we implement task parallelization among multiple processes using domain replication. Moreover, we devise an algorithm to optimize the MPI collective communication across super-nodes. Experimental results show that SW-TRRM achieves a 17.40x speedup within a single core group compared to the original TRRM program. When scaled up to 2,048 processes and 133,120 cores, SW-TRRM maintains good strong and weak scalability.

关键词： high performance computing Parallel optimization Sunway supercomputer The random ray method

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