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Design of Cloud-Edge-End Collaborative Framework for Power Supply Intelligent Inspection Operation Scenario Based on Unmanned Aerial Vehicle

Design of Cloud-Edge-End Collaborative Framework for Power S...

引用

IEEE Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Wei Wang Ping Qiu State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang International Studies University Hangzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

As a new type of tool with powerful perception, monitoring, and emergency response capabilities, unmanned aerial vehicles (UAVs) have the characteristics of flexibility, maneuverability, and high-altitude inspection. UAVs can greatly improve the efficiency of power facility maintenance and repairing, reduce personnel risks, and have gradually become an important auxiliary means for power supply operation and management. Currently, research on cloud-edge collaborative computing using UAVs has been applied to power systems, especially in their daily maintenance. However, there is not much discussion on building a systematic UAV-based cloud-edge collaboration system, especially to meet the requirements of power supply companies. Aiming at the problems such as the limited distance of image transmission, the lack of timeliness and visualization of image data processing in the application process of UAVs, in this paper, a "cloud-edge-end" collaborative framework has been proposed to achieve fast data return over the distance under signal coverage, and use the characteristics of local edge computing to quickly solve image preprocessing.

关键词： Power supplies Clouds Collaboration Inspection Autonomous aerial vehicles Cameras Electricity supply industry Real-time systems Maintenance Edge computing

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Deep Learning-based Channel Estimation in 5G MIMO-OFDM Systems

Deep Learning-based Channel Estimation in 5G MIMO-OFDM Syste...

引用

international conference on Signal processing and Communication (ICSC)

作者： M. Meenalakshmi Saurabh Chaturvedi Vivek K. Dwivedi Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9781665454315

the primary air interface of fifth-generation (5G) wireless communication is multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM). In addition, channel estimation holds a major role in achieving effective system performance in 5G wireless networks. Importantly, deep learning (DL) techniques have the ability to improve system performance and reliability and to reduce the computational complexity of 5G communication systems. Hence, DL techniques have been applied in MIMO-OFDM systems to improve the channel estimation quality and reduce the bit error rate compared to traditional channel estimation methods. this paper offers a comprehensive survey of DL-based channel estimation in MIMO-OFDM receivers in terms of DL algorithms, 5G channel models, and error performance. In conclusion, the application of DL techniques in MIMO-OFDM shows a significant potential for performance enhancement in 5G networks.

关键词： 5G mobile communication System performance Wireless networks Bit error rate Channel estimation Signal processing algorithms Receivers

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GSpTC: High-Performance Sparse Tensor Contraction on CPU-GPU Heterogeneous Systems

GSpTC: High-Performance Sparse Tensor Contraction on CPU-GPU...

引用

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

作者： Guoqing Xiao Chuanghui Yin Yuedan Chen Mingxing Duan Kenli Li College of Computer Science and Electronic Engineering Hunan University Changsha China National Supercomputing Center in Changsha Changsha China Shenzhen Research Institute Hunan University Shenrhen China

Many fields of scientific simulation, such as chem-istry and condensed matter physics, are increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this work, we centre around binary sparse tensor contraction (SpTC) which has the challenges of index matching and accumulation. To address and alleviate the above difficulties, we present GSpTC, an efficient element-wise SpTC framework on CPU-GPU heterogeneous systems. GSpTC first introduces a fine-grained index partition based on element-wise tensor con-traction, which facilitates efficient use of the parallel computing power of GPUs. In particular, GSpTC leverages multi-threading parallelism on GPUs for the contraction phase and merge phase, which greatly accelerates the computation phase in sparse tensor contraction. through evaluation of five datasets, we find that the proposed GSpTC achieves an average performance improvement of 74.32% compared to the previous state-of-the-art Sparta framework.

关键词： Tensors Instruction sets Graphics processing units parallel processing Indexes Chemical elements Physics

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Profile face recognition based on elements by normalizing global and local features

Profile face recognition based on elements by normalizing gl...

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international conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Erfan Khoshnevisan Hamid Hassanpour Mohammad. M. AlyanNezhadi Faculty of Computer Engineering Shahrood University of Technology Department of Mathematics Unicerversity of Science and Technology of Mazandaran

ISBN: (纸本)9781665476249

Despite recent advances in face recognition using deep learning, pose changes are still one of the challenging problems. In this paper, we presented a method to normalize the image in the feature space by capturing local consistency during training. we used facial elements such as eyes, lips, nose, and hairstyle in addition to its general state for recognition. For this purpose, all the elements were detected and localized by semantic segmentation and then normalized. Specifically, an Attention-based convolutional neural network is introduced to integrate local features with their long-range dependencies yielding better feature representations and hence generate representation that preserves identities better, especially for larger pose angles. We used the face/head segmentation dataset for training and the FERET dataset for testing, and the results were good for images with the most difficult angular conditions. the performance of this model on the +90 ° images of the FERET dataset for 980 samples was 49.6%compared to VGGFace which was only 2.6% accurate.

关键词： Training Face recognition Semantic segmentation Lips Pipelines Nose Signal processing

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An Enhanced Approach to Content-based Image Retrieval using Harris Hawks Optimization and Automated Image Captioning

An Enhanced Approach to Content-based Image Retrieval using ...

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

作者： Roshni Padate Ashutosh Gupta Prasun Chakrabarti Arvind Sharma Computer Engineering Fr. Conceicao Rodrigues College of Engineering Mumbai India Computer Science and Engineering Sir Padampat Singhania University Udaipur India Department of Mathematics Sir Padampat Singhania University) Udaipur India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

this paper introduces an innovative approach to Content-Based Image Retrieval (CBIR) that leverages Harris Hawks Optimization (HHO) to improve feature selection and retrieval accuracy. CBIR systems are increasingly important in fields such as medical imaging and remote sensing, where efficient and accurate image retrieval is essential. Inspired by the cooperative hunting strategies of Harris hawks, the HHO algorithm efficiently balances exploration and exploitation, optimizing the feature extraction process to enhance retrieval outcomes. the proposed system is evaluated on various datasets and compared to other optimization techniques, such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). the findings show that the HHO-optimized system outperforms these methods in terms of precision, recall, and F1-scores, while also reducing retrieval ***, the incorporation of image captioning into the CBIR framework further improves the user experience. By integrating Computer Vision (CV) and Natural Language processing (NLP), the system generates descriptive captions along- side retrieved images, offering valuable textual context. this capability allows users to quickly evaluate the relevance of retrieved images, particularly in complex fields like medical imaging. Experimental results demonstrate that the combination of HHO-based optimization and image captioning significantly enhances both retrieval accuracy and user satisfaction, making it a powerful solution for large-scale image retrieval tasks.

关键词： Accuracy Image retrieval Feature extraction Natural language processing User experience Particle swarm optimization Optimization Remote sensing Genetic algorithms Biomedical imaging

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Parameter Tuning of Discontinues Lyapunov Based Controller Based on the Gray Wolf Optimization Algorithm applied to a Robotic Manipulator

Parameter Tuning of Discontinues Lyapunov Based Controller B...

引用

international conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Sam Ziamanesh Hadi S. Salimi Ahmad Tavana Amir A. Ghavifekr Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (纸本)9781665476249

this study investigates designing a controller that can be implemented into the nonlinear 2 Degree of Freedom (2-DOF) robotic arm. In order to reveal the effectiveness and robustness of a controller, Proportional Integral Derivative with gravity compensation controller, and discontinuous Lyapunovbased controller have been utilized, and compared. the gain tunning of the controllers has been carried out using two metaheuristic methods: Grey Wolf optimization (GWO) and Grasshopper optimization Algorithm (GOA). Results of the simulations demonstrate that the Discontinuous Lyapunov-Based controller tuned by GWO, provides more accurate results. Evaluations of Integral Absolute Error (IAE) and Integral Absolute Change in Control Output (IACCO) have been used to study the manipulator’s performance during the simulations.

关键词： Simulation Metaheuristics Signal processing algorithms Signal processing Manipulators Robustness Intelligent systems

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Fabric Image Layering Based on parallel K-means

Fabric Image Layering Based on Parallel K-means

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

作者： Lei Wang Wei Zhao Tong Sang Zeng Zeng Yangfan Li School of Microelectronics Shanghai University Shanghai China School of Computer Science and Engineering Central South University Changsha China

K-means is a commonly used image layering algorithm. Since the K-means algorithm needs to calculate the Euclidean distance between data points, the computational complexity is high and the time consumption is large, which is difficult to meet the needs of industrial production. therefore, it is very important to accelerate K-means. Many calculations in the K-means algorithm are independent, andCUDA can solve this problem better. this paper proposes a stream-based K-means parallel algorithm, which can not only realize the parallel calculation inside K-means, but also realize the search of multiple $K$ at the same time. Besides, according to the image data and the Euclidean distance calculation mechanism, a simple size penalty coefficient with extremely low computational complexity is proposed to select the $K$ value. Experiments show that the CUDA K-means method proposed in this paper is 27 times higher than that of CPU K-means, and the proposed penalty coefficient can find the inflection point in the loss of multiple $K$ values.

关键词： Industries Correlation coefficient Graphics processing units Euclidean distance Production Streaming media Inspection

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DV Emotion Net: A Sub-Study on Emotion Detection Model Performance Across P100, T4, and TPU VM V3-8

DV Emotion Net: A Sub-Study on Emotion Detection Model Perfo...

引用

Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Dhiren Dommeti Siva Ramakrishna Nallapati Rand Alfaris School of Professional Studies (SPS) Clark University Worcester Massachusetts USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

this study evaluates the performance of different hardware configurations-GPU P100, GPU T4, and TPU VM v3-8-in the context of emotion detection using the DV EmotionNet framework. Building on prior research that integrates audio and video modalities for emotion recognition, the analysis explores how each hardware setup influences model efficiency and accuracy. Audio features were extracted using techniques such as energy, zero crossing rate, and Mel-Frequency Cepstral Coefficients (MFCC), while video features were obtained through spatial-temporal Gaussian kernels and Gaussian-weighted functions applied to the second momentum matrix. the Multimodal Feature Aggregation (MFA) method was employed to fuse the audio and video features, creating a comprehensive dataset. the evaluation utilized the Fusion of Emotion Recognition Convolutional Neural Network (FERCNN) model, focusing on the impact of accelerators on performance metrics. Recent advancements often face challenges like high computational costs, scalability issues, and sensitivity to noisy data. this study addresses these challenges by systematically evaluating the computational efficiency and accuracy trade-offs across different hardware accelerators. Results from the RAVDESS and CREMAD datasets revealed notable differences in accuracy, with the P100 demonstrating superior performance on simpler tasks, while TPU VM v3-8 excelled in more complex scenarios. these findings highlight the significance of hardware choice in optimizing multimodal emotion recognition systems, reinforcing the critical role of effective computational resources in enhancing applications across various domains, including human-computer interaction, healthcare, and entertainment.

关键词： Training Emotion recognition Accuracy Fluctuations Computational modeling Graphics processing units parallel processing Hardware Computational efficiency Testing

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processing and visualization of the results of parametric numerical calculations 16

Processing and visualization of the results of parametric nu...

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16th international conference Optical Methods of Flow Investigation, OMFI 2021

作者： Bondarev, A.E. Kuvshinnikov, A.E. Keldysh Institute of Applied Mathematics RAS Miusskaya sq. 4 Moscow125047 Russia

In modern problems of mathematical modeling in computational gas dynamics, it is increasingly necessary to implement parametric studies. In these cases, the key factors of the problem under consideration vary with the chosen step within the given ranges. Calculations of this kind can be effectively carried out by constructing a generalized computational experiment. A generalized computational experiment is a computational technology that combines the solution of mathematical modeling problems, parallel technologies, and visual analytics technologies. the results of a generalized computational experiment are multidimensional arrays, where the dimension of the arrays corresponds to the number of key factors. processing and visual presentation of such arrays requires solving a number of separate tasks. the processing and visual presentation of the results are carried out for target functionals represented as a function of many variables. the report presents an examples of solving specific processing and visualization problems based on the implemented generalized computational experiment for 3D cone in supersonic flow. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Visualization

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ReSLB: Load Balanced Workflow for Distributed Deep Learning Mass Spectrometry Database

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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

作者： Li Mocheng Liu Yang Ou Yang Chen Zhiguang Xiao Nong Chen Tao Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences(Beijing) Beijing Institute of Lifeomics Beijing China

the proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. the parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. the heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. this paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. this work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that.

关键词： Deep learning Scalability Neural networks Data preprocessing Distributed databases Graphics processing units Clustering algorithms

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