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Performance evaluation of GPU-enhanced Jacobi algorithm implementations for optimizing CFD Poisson partial differential equation solving

Performance evaluation of GPU-enhanced Jacobi algorithm impl...

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international conference on System theory, Control, and Computing (ICSTCC)

作者： Sebastian Sbîrnă Liana-Simona Sbîrnă DTU Compute Department Institute of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Department of Chemistry Faculty of Sciences University of Craiova Craiova Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

the current work presents an in-depth analysis of several optimizations using GPU parallel computing applied to the Jacobi method for solving Poisson partial differential equations in computational fluid dynamics (CFD). We expand on previous CPU-parallelized Jacobi algorithm research, exploring four GPU-optimized Jacobi method variants: single-threaded, multi-threaded, multi-GPU and a norm-based stopping criterion kernel. these implementations are benchmarked against a multi-threaded CPU baseline. Results indicate that, whereas the single-threaded GPU version is slower than the CPU baseline, multi-threaded GPU versions achieve significant speed gains, especially for larger grid sizes. the multi-GPU version doubles memory bandwidth, enhancing performance for extensive computations, despite overhead for smaller matrices. the norm-stopping criterion kernel offers early convergence for small matrices but at a high overhead cost. Profiling confirms a memory-bound bottleneck, suggesting single-precision and optimized memory access as improvements. Ultimately, multi-threaded GPU kernels substantially outperform the CPU baseline for large-scale CFD problems, establishing GPUs as efficient accelerators for the Jacobi algorithm.

关键词： Jacobian matrices Performance evaluation Runtime Computational fluid dynamics Graphics processing units Process control Bandwidth Switches throughput Kernel

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CareerBoost: A Hybrid RAG-NLP Job Recommendation Framework

CareerBoost: A Hybrid RAG-NLP Job Recommendation Framework

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Sanskruti Deshmukh Aaryaman Bajaj Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

the proposed system presents a novel approach to job recommendation and resume enhancement, leveraging advanced Natural Language processing (NLP) techniques and Retrieval-Augmented Generation (RAG). We introduce a comprehensive system that combines semantic search, FAISS-based similarity matching, and a fine-tuned GPT-2 model to provide personalized job recommendations and actionable resume improvement suggestions. the evaluation, conducted through offline metrics and online A/B testing with 5,000 users, demonstrates significant improvements over traditional keyword-matching approaches. the RAG-based system achieved a 25.8% increase in recommendation relevance (Precision@10), a 45.7% increase in user engagement, and a 41.2% increase in job application rates. Furthermore, users who applied the system’s resume enhancement suggestions saw a 9.0% improvement in subsequent job matches. these results highlight the potential of AI-driven systems to revolutionize online recruitment and career development, while also emphasizing the need for ongoing consideration of ethical implications and fairness in AI-assisted hiring processes.

关键词： Measurement Ethics Career development Accuracy Engineering profession Semantic search Resumes Natural language processing Recruitment Testing

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Extended field of vision by using two synchronized camera acquisition off-axis holograms

Extended field of vision by using two synchronized camera ac...

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international conference on Image and Signal processing and their Applications (ISPA)

作者： Omar Chaab Fatan Bougheziou Malika Mimi Serge Simoëns Department of Sciences and Technology Signals and Systems Laboratory University Abdelhamid Ibn Badis Mostaganem Algeria Applied Optics Laboratory Institute of Optics and Fine Mechanics University Ferhat Abbas Setif Algeria Department of Electrical Engineering Signals and Systems Laboratory University Abdelhamid Ibn Badis Mostaganem Algeria Acoustic and Fluid Mechanics Laboratory UMR CNRS 5509 UCB Lyon I INSA Central School of Lyon Ecully Lyon France

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Digital holography (DH) is an imaging technique that allows the recording and the reconstruction of the complex optical field (complete information) in both amplitude and phase. Its main advantage is the possibility to modify the recorded wavefront to get free-aberration version. this technique represents the best contender for metrological applications especially in mechanical fluids where it gives an additional dimension for the determination of velocity field of different kind of fluid flow. In order to extend the fluid field of investigation, two synchronized cameras CCD and CMOS with different characteristic (pitch and size) are used for recording two views holograms; this task is implemented under Labview interface which also permits to record successive several holograms with a predefined time interval. the holograms are obtained with the help of holographic setup in off-axis configuration of two views (direct and orthogonal). In order to obtain the total field of view, the two reconstructed holograms must have the same output resolution.

关键词： Image resolution Heuristic algorithms Signal processing algorithms Transforms Holography Cameras Synchronization

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Improved Hamiltonian Control Law with Load Current Sensorless of Multiphase parallel Converter for Electric Vehicle Applications 49

Improved Hamiltonian Control Law with Load Current Sensorles...

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49th Annual conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Kamnarn, Uthen Yodwong, Burin Mungporn, Pongsiri thounthong, Phatiphat Khomfoi, Surin Kumam, Poom Pierfederici, Serge Nahid-Mobarakeh, Babak Takorabet, Noureddine Rajamangala University of Technology Lanna Department of Electrical Engineering Chiang Mai Thailand King Mongkut's University of Technology North Bangkok Thai French Innovation Institute Bangkok Thailand King Mongkut's University of Technology North Bangkok Renewable Energy Research Centre Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Technical Education Department of Teacher Training in Electrical Engineering Bangkok Thailand School of Engineering King Mongkut's Institue of Technology Ladkrabang Department of Electrical Engineering Bangkok Thailand Faculty of Science Department of Mathematics Bangkok Thailand University of Lorraine Laboratoire d'Energetique et de Mecanique Theorique et Appliquee Umr 7563 Cnrs Vandœuvre-lès-Nancy54500 France McMaster University Electrical and Computer Engineering HamiltonONL8S 4L8 Canada Université de Lorraine ENSEM-GREEN Nancy54052 France

ISBN: (纸本)9798350331820

this article presents an improved Hamiltonian control law (HCL) with load current sensorless ability for multiphase parallel converters in electric vehicle applications. On one hand, the proposed control approach has a significant advantage in handling constant power load (CPL) that occurs in the situation of the vehicle, which is a dangerous situation that can cause oscillations and uncertainty. On the other hand, the load current observer is applied with HCL to improve the efficiency and reliability of the converter system. In addition, by eliminating the need for current sensors, the cost and size of the converter system are reduced, while maintaining accurate current control. Moreover, the difference flatness approach has been applied to generate the current reference resulting in regulating the DC bus voltage. Finally, the multiphase two-quadrant converter driving by the proposed control law has been evaluated through experimental tests. the obtained experimental results demonstrate the excellent performance of the multiphase two-quadrant converter with CPL thereby confirming the efficacy of the proposed approach. © 2023 IEEE.

关键词： Electric vehicles

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Ensemble of Deep Neural Network Models for MOS Prediction

Ensemble of Deep Neural Network Models for MOS Prediction

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Marie Kunešová Jindřich Matoušek Jan Lehečka Jan Švec Josef Michálek Daniel Tihelka Martin Bulín Zdeněk Hanzlíček Markéta Řezáčková New Technologies for the Information Society and Department of Cybernetics Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

Automatic evaluation of the quality of synthetic speech has the potential to serve as a cheaper and less time-consuming alternative to standard listening tests. In this paper, we present our contribution to the ongoing research: a system for automatic prediction of the mean opinion score (MOS) given by human listeners. the system was specifically developed for the recent VoiceMOS Challenge. Following the success of fusion systems in similar challenges, our contribution is an ensemble that interpolates the outputs of seven different models: four different wav2vec models, a CNN-RNN model, QuartzNet, and the LDNet baseline. During the VoiceMOS challenge, our system achieved the second-best utterance-level MSE of 0.171 and ranged from 2nd to 8th place among all 22 participating teams in terms of other evaluation metrics.

关键词： Measurement Deep learning Neural networks Signal processing Predictive models Data models Speech synthesis

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Application of the LSA Technique to Determine the Priority of Alerts from a Command and Control Center 8

Application of the LSA Technique to Determine the Priority o...

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8th international conference on eDemocracy and eGovernment (ICEDEG)

作者： Orellana, Marcos Lima, Juan-Fernando Camacho Game, Daniel Martinez Leon, Pablo Cedillo, Priscila Prado, Daniela Univ Azuay Comp Sci Res & Dev Lab LIDI Cuenca Ecuador Univ Azuay Sch Comp Sci Engn Cuenca Ecuador Univ Cuenca Dept Comp Sci Cuenca Ecuador

ISBN: (纸本)9781665425131

It is essential to determine the alert level in a command-and-control center when someone calls an operator for an emergency since life may be in danger. the alert level is determined based on the evaluation that the operator can perceive during the call. Sometimes, cases can be similar to those previously attended. therefore, it is helpful that this knowledge can be rescued and applied to new cases. In this context, the Latent Semantic Analysis (LSA) technique can determine the alert level and find the most representative words in each case. thus, when a new alarm is triggered, the system can recommend the alert level with which it is rated. Consequently, a solution based on previous knowledge has been stated. this solution leads to the following methodological process: i) data preprocessing, ii) topic analysis and iii) classification. When this proposal was applied, the results revealed an accuracy greater than 60% in predicting the type of alert based on the text.

关键词： Pre-processing Classification Bag of Words TF-IDF

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Rectified Flow-Based Generative Transfer Learning for System Fault Diagnosis

Rectified Flow-Based Generative Transfer Learning for System...

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international conference on Vision, Image and Signal processing (ICVISP)

作者： Ruonan Liu Shuai Wu Qiming Liu Dongsheng Guo Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China Tianjin University College of Intelligence and Computing Tianjin China Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Mechanical Engineering Tianjin China Hainan University School of Information and Communication Engineering Hainan China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

With the rapid development of new energy sources under the “dual carbon” background, the safety and stability of energy storage and power generation systems have attracted widespread attention. this paper investigates the challenges of insufficient training data and the limitations of existing generative transfer learning methods in the field of system fault detection. To address these issues, we propose a graph neural network model based on a generative transfer learning method utilizing the Rectified Flow algorithm for system fault detection. the Rectified Flow algorithm is applied to transfer learning to enhance the efficiency and effectiveness of system fault diagnosis, especially when training data is limited. the model leverages source domain data to train the target domain data, overcoming the data scarcity issue in the target domain. Our experimental results demonstrate that the proposed model outperforms traditional methods in terms of computational efficiency and model conciseness, showing remarkable performance in system fault detection. this study not only provides an effective solution to the data insufficiency problem in system fault detection but also offers valuable insights for further enhancing the performance of system fault diagnosis.

关键词： Fault diagnosis Fault detection Computational modeling Transfer learning Signal processing algorithms Training data Signal processing Stability analysis Safety Power generation

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An Efficient parallel CNN Inference Framework for Multi-zone Processor

An Efficient Parallel CNN Inference Framework for Multi-zone...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Jie Chen Zhong Liu College of Computer National University of Defense Technology Changsha China

Deep convolutional neural networks have been widely used in various fields, and how to accelerate and optimize the performance of neural network models according to the architectural characteristics of accelerator hardware has become the research hot spot. We propose and design an efficient parallel convolutional neural network inference framework named PCIF for multi-zone processor. Its core is a multi-core parallel and vectorized C++ operator library. PCIF has designed optimized data layout and transmission strategies, layer fusion and multi-image fusion methods, which can automatically parse and efficiently map existing neural network models to multi-core vector accelerators to accelerate inference computation. Users can also build new neural network models through the C++ and Python APIs provided by PCIF to achieve high performance inference computing. the results of the experiment show that the AlexNet, VGG-16, GoogleNet, and ResNet-50 network models achieve efficient computing performance using PCIF.

关键词： Quantization (signal) Multicore processing Computational modeling Neural networks C++ languages parallel processing Libraries

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On Defragmentation Algorithms for GPU-Native Octree-Based AMR Grids 16th

On Defragmentation Algorithms for GPU-Native Octree-Based AM...

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16th international conference on parallel Computing Technologies, PaCT 2021

作者： Pavlukhin, Pavel Menshov, Igor Keldysh Institute of Applied Mathematics Moscow125047 Russia Research and Development Institute "Kvant" Moscow125438 Russia

ISBN: (纸本)9783030863586

the GPU-native CFD framework with dynamical adaptive mesh refinement (AMR) requires periodical execution of memory compaction operations to relieve memory expenses. the present paper addresses several different parallel GPU memory defragmentation algorithms for octree-based AMR grids. these algorithms are tested on benchmark CFD problems typical for AMR transformation. the results show that the memory defragmentation algorithm based on the prefix scan procedure is not only 1–2 order faster compared to algorithm based on space filling curve (z-curve) but also surprisingly and dramatically impacts on CFD solver performance by reducing total GPU runtime for some problems up to 37%. © 2021, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Wind Power Forecasting Model Based on Feature processing and Deep Hybrid Kernel Extreme Learning Machine 8th

Wind Power Forecasting Model Based on Feature Processing and...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wei, Xiongfei Wang, Jing Ruan, Yi Fang, Yuanjie College of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China School of Electronic Engineering Chaohu University Anhui Chaohu238024 China

ISBN: (纸本)9789819602315

To improve the forecasting accuracy of wind power, a wind power forecasting (WPF) model based on feature cleaning combined with deep hybrid kernel extreme learning machine (DHKELM) is proposed. the Pearson correlation coefficient (PCC) method is used to optimize the features, eliminate irrelevant features, and the isolated forest (IF) algorithm is used to clean the anomalous data in the retained features, which enhances the correlation between the selected features and wind power, and then combines the DHKELM to predict the wind power. Finally, the proposed model is applied to predict the wind power of a 1MW wind turbine, and the results of the progressive forecasting of the proposed method and the forecasting results of different models are compared. the results show that the PCC-IF-DHKELM forecasting model has a root-mean-square error of 0.01928 MW, an average absolute error of 0.015254 MW, and a correlation coefficient of 0.99435, which is significantly better than the other forecasting models, proving the effectiveness of the PCC-IF-DHKELM forecasting method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind forecasting

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