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IEEE International Conference on Big Data

作者： Tran Anh Khoa Do-Van Nguyen Minh-Son Dao Koji Zettsu Big Data Integration Research Center National Institute of Information and Communications Technology Tokyo Japan

ISBN: (纸本)9781665480468

Split learning (SL) is a popular distributed machine learning (ML) method used to enable ML. It divides a neural network based model into subnetworks. Then, it separately trains the subnetworks on distributed parties (e.g., client and server). In distributed ML, data are generated and collected on the client-side. In contrast, the collected data are processed using an application deployed on the server side. However, when applied in practice using Internet of things systems and clients, numerous obstacles occur because of limited configuration and resources. Dividing neural networks in the SL is the biggest problem and an open question in numerous studies. This study introduces splitDyn, which is a new dynamic SL solution to solve the aforementioned problems. This method provides a solution for eliminating their inherent drawbacks. The main idea is to apply a Round-Robin schedule to select the client for the training process. Then, the next idea is to use the Hungarian optimization algorithm to assign a layer to a client and enhance the accuracy. The proposed method reasonably achieved better accuracy and reduced processing time than the other learning models. Furthermore, it applies the incident datasets to predict the incident event and in edge computing for edge artificial intelligence (AI) applications.

关键词： Training Temperature distribution Schedules neural networks Big Data Prediction algorithms Servers

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A single-stream adaptive scene layout modeling method for scene recognition

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neural Computing and Applications 2024年第22期36卷 13703-13714页

作者： Wang, Qun Zhu, Feng Lin, Zhiyuan Wang, Jianyu Li, Xiang Zhao, Pengfei Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Robot Science and Engineering Northeastern University Shenyang110016 China Faculty of Computer Science and Engineering Northeastern University Shenyang110016 China

Scene recognition has been the foundation of research in computer vision fields. Because scene images typically are composed of specific regions distributed in some layout, so modeling layouts of various scenes is a key clue for scene recognition. Existing methods usually require an additional stream to detect regions for subsequent modeling, which accumulate errors and may miss important information. Meanwhile, they use manual features to model relations between regions, which weakens the representation ability of layouts. In this paper, we propose a single-stream adaptive scene layout modeling approach based on a layout modeling module (LMM), which constructs layouts without additional detection streams and adaptively captures the relations to take advantage of graph attention network. LMM is directly concatenated to a convolutional neural network, where each pixel of the activation maps of the last convolutional layer is defined as a region that is the initial input node of the LMM. LMM first models the layout of each region, and then uses all regions with layout information to model the entire scene. Layout relations are encoded as edges, which are automatically analyzed according to region co-occurrence and relative position. Our work can be understood as optimizing features of the activation maps from a scene layout modeling perspective for scene recognition. Experimental results on MIT67, SUN397, and Places365 show that our single-stream model achieves competitive performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Architecture and Algorithm Design for Civil Aviation Data Real-Time Analysis System

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IEEE ACCESS 2024年 12卷 66382-66397页

作者： Zhang, Yifeng Xi, Qi Wang, Jing Gu, Shuhuai South China Normal Univ Sch Data Sci & Engn Shanwei 516600 Peoples R China

Currently, the analysis of civil aviation flight data relies primarily on post-flight data. This involves transmitting onboard data to the servers of the airline and aircraft manufacturer after the aircraft has landed, and the engines have been shut down, using ground-based cellular stations or wireless hotspots. Real-time data analysis during flight remains a challenge. With the advent of integrated communication networks in the aerospace sector, the real-time transmission of onboard data is poised to become a future trend. But there is a gap in real-time civil aviation data analysis system. This study is based on Kappa distributed computing architecture, established a real-time aviation data analysis system, which inputs Quick Access Recorder (QAR) data stream and achieves fast data decoding and multiple real-time analysis algorithms, including flight trajectory outlier repair, flight phase identification, steady cruising state determination, and engine lubricating oil quantity monitoring. The results of simulation experiments indicate that the maximum average latency for individual algorithms is consistently below 250 milliseconds, meeting real-time performance requirements. And all real-time algorithms have similar performances with or better than existing excellent post-flight algorithms. Specifically, the flight trajectory outlier repair algorithm can repair all outliers and cost 0.2ms to process an outlier. The flight phase identification achieved 97.8% accuracy referring to commercial software's results. The steady cruising state determination exhibits computational efficiency approximately five times faster than the naive method. The proposed engine lubricating oil quantity monitoring method based on the Clustering Optimized Transformer (CO-Transformer) neural network yields an average mean square error of approximately 0.057 L2 between predicted and actual values, respectively 7.36%, 66.01% and 66.05% better than common Transformer, LSTM and GRU neural net

关键词： distributed data processing machine learning aviation data analysis

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Comparison of response surface methodology with artificial neural network for prediction of the tensile properties of friction stir-processed surface composites

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2022年第1期236卷 126-137页

作者： Butola, Ravi Singari, Ranganath M. Murtaza, Qasim Tyagi, Lakshay Delhi Technol Univ Dept Mech Engn Delhi 11042 India

In the present work, nanoboron carbide is integrated in the aluminum matrix using friction stir processing: by varying process parameters, that is, tool pin profile, tool rotational speed and tool traverse speed, based on Taguchi L-16 design of experiment. A self-assembled monolayer is successfully developed on the substrate to homogeneously and uniformly distribute the reinforcement particles. Response surface methodology and artificial neural network models are developed using ultimate tensile strength and total elongation as responses. Percentage absolute error between the experimental and predicted values of ultimate tensile strength and total elongation for the response surface methodology model is 3.537 and 2.865, respectively, and for artificial neural network is 2.788 and 2.578, respectively. For both the developed models experimental and forecasted values are in close approximation. The artificial neural network model showed slightly better predictive capacity compared to the response surface methodology model. From the scanning electron microscopy micrograph, it is evident that throughout the matrix B4C reinforcement particles are well distributed also;with increasing tool rotational speed grain size decreases up to 1200 r/min;on further increasing the tool rotational speed particles starts clustering.

关键词： Friction stir processing artificial neural network response surface methodology self-assembled monolayer

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Multiobjective based strategy for neural Architecture Search for segmentation Task

Multiobjective based strategy for Neural Architecture Search...

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1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Sana, Abass Senhaji, Kaoutar Nakib, Amir Univ Paris Est Creteil UPEC Soc Etud Syst Informat Lab LISSI Vitry Sur Seine France Univ Paris Est Creteil UPEC Lab LISSI Vitry Sur Seine France

ISBN: (纸本)9798350364613;9798350364606

This research aims to determine an optimal neural network model for image segmentation, addressing a crucial aspect of computer vision and deep learning. The study aims to identify high-performance neural network architectures characterized by superior accuracy and minimized complexity through a combination of deep learning principles, multi-objective optimization and genetic algorithms. The proposed approach adapts NSGA-III to generate new neural network architectures encoded via binary representation. The chromosomes are then decoded to undergo the training. The focus of this work is to explore and identify better models based on performance metrics: intersection over union, accuracy, and frequency weighted intersection over union (fwloU), while simultaneously minimizing model complexity by optimizing the number of parameters. The primary results are very encouraging, and in future work, we aim to provide more tests and analysis on other benchmarks.

关键词： Terms Multi-objective optimization deep learning food segmentation genetic algorithms NSGA III

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Data augmentation and its application in distributed acoustic sensing data denoising

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GEOPHYSICAL JOURNAL INTERNATIONAL 2022年第1期228卷 119-133页

作者： Zhao, Y. X. Li, Y. Wu, N. Jilin Univ Dept Informat Engn Changchun 130026 Peoples R China

As a data-driven approach, the performance of deep learning models depends largely on the quantity and quality of the training data sets, which greatly limits the application of deep learning to tasks with small data sets. Unfortunately, sometimes we need to use limited small data sets to complete our tasks, such as distributed acoustic sensing (DAS) data denoising. However, using a small data set to train the network may cause overfitting, resulting in poor network generalization. To solve this problem, we propose an approach based on the combination of a generative adversarial network and a deep convolutional neural network. First, we used a small noise data set to train a generative adversarial network to generate synthetic noise samples, and then used these synthetic noise samples to augment the noise data set. Next, we used the augmented noise data set and the signal data set obtained through forward modelling to construct a synthetic training set. Finally, a denoising network based on a convolutional neural network was trained on the constructed synthetic training set. Experimental results show that the augmented data set can effectively improve the denoising performance and generalization ability of the network, and the denoising network trained on the augmented data set can more effectively reduce various kinds of noise in the DAS data.

关键词： Asia Downhole methods Image processing neural networks fuzzy logic Seismic noise

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Optimization method of machining parameters based on intelligent algorithm

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distributed AND PARALLEL DATABASES 2022年第4期40卷 737-752页

作者： Cai, Jie Zhang, Wei Deng, Jinlian Zhao, Weisheng Zhejiang Inst Mech & Elect Engn Sch Intelligent Mfg Hangzhou 310053 Zhejiang Peoples R China Zhejiang Inst Mech & Elect Engn Sch Creat Arts & Design Hangzhou 310053 Zhejiang Peoples R China Zhejiang Inst Mech & Elect Engn Sch Automat Hangzhou 310053 Zhejiang Peoples R China

The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements.

关键词： Intelligent algorithms processing parameters Heavy-duty CNC machine tools neural network algorithms

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Primal-Dual Fixed Point Algorithms Based on Adapted Metric for distributed Optimization

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IEEE TRANSACTIONS ON neural networkS AND LEARNING SYSTEMS 2023年第6期34卷 2923-2937页

作者： Li, Huaqing Zheng, Zuqing Lu, Qingguo Wang, Zheng Gao, Lan Wu, Guo-Cheng Ji, Lianghao Wang, Huiwei Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Beihang Univ Hangzhou Innovat Inst Hangzhou 310023 Peoples R China Neijiang Normal Univ Coll Math & Informat Sci Data Recovery Key Lab Sichuan Prov Neijiang 641100 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

This article considers distributed optimization by a group of agents over an undirected network. The objective is to minimize the sum of a twice differentiable convex function and two possibly nonsmooth convex functions, one of which is composed of a bounded linear operator. A novel distributed primal-dual fixed point algorithm is proposed based on an adapted metric method, which exploits the second-order information of the differentiable convex function. Furthermore, by incorporating a randomized coordinate activation mechanism, we propose a randomized asynchronous iterative distributed algorithm that allows each agent to randomly and independently decide whether to perform an update or remain unchanged at each iteration, and thus alleviates the communication cost. Moreover, the proposed algorithms adopt nonidentical stepsizes to endow each agent with more independence. Numerical simulation results substantiate the feasibility of the proposed algorithms and the correctness of the theoretical results.

关键词： Optimization Measurement Convex functions Approximation algorithms Signal processing algorithms Convergence Prediction algorithms Adapted metric method completely distributed nonsmooth convex function primal-dual fixed point algorithm randomized asynchronous iterative

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A PARALLEL ONLINE PERFORMANCE OPTIMIZATION METHOD FOR ATR ENGINES BASED ON ENSEMBLE neural network 69

A PARALLEL ONLINE PERFORMANCE OPTIMIZATION METHOD FOR ATR EN...

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69th ASME Turbomachinery Technical Conference and Exposition (ASME Turbo Expo) (GT)

作者： Cai, Weidong Zhao, Wei Liu, Binbin Yang, Xuesen Luo, Weiwei Zhao, Qingjun Chinese Acad Sci Inst Engn Thermophys Beijing Peoples R China Natl Key Lab Sci & Technol Adv Light Duty Gas Tur Beijing Peoples R China Univ Chinese Acad Sci Sch Aeronaut & Astronaut Beijing Peoples R China Beijing Key Lab Distributed Combined Cooling Heat Beijing Peoples R China

ISBN: (纸本)9780791887967

To address the issue of lengthy optimization processes for ATR engines using swarm intelligence optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), a novel Ensemble neural networks based Parallel Performance Optimization (ENNPPO) methodology is proposed. This strategy amalgamates several small neural networks into a comprehensive larger model for the construction of an advanced onboard ATR engine model, moreover, the independence of each small neural networks allows for the reduction of computational time through parallel processing on GPUs. The method then harnesses the PSO algorithm to refine performance. Numerical simulations carried out with embedded GPU computing chips demonstrate that the average optimization time per test case is 0.28 seconds, with an energy consumption of 1.22 Joules. The proposed method achieves an optimization precision that is 78.40% and 84.30% higher than that of traditional neural network approaches and Linear Parameter-Varying (LPV) model methods, respectively. Furthermore, the computation speed is enhanced by a factor of at least 7.8 times than CPU, directly attributable to the expedited parallel processing enabled by GPUs.

关键词： ATR engine performance optimization ensemble neural network (ENN) parallel performance optimization

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Enhancing emotion prediction using deep learning and distributed federated systems with SMOTE oversampling technique

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ALEXANDRIA ENGINEERING JOURNAL 2024年 108卷 498-508页

作者： Raju, V. V. Narasimha Saravanakumar, R. Yusuf, Nadia Pradhan, Rahul Hamdi, Hedi Saravanan, K. Aanandha Rao, Vuda Sreenivasa Askar, Majid A. Marri Laxman Reddy Inst Technol & Management Dept IT Hyderabad 500043 India Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept ECE Chennai 602105 India King Abdulaziz Univ Fac Econ Dept Econ & Adm Jeddah Saudi Arabia GLA Univ Dept Comp Engn & Applicat Mathura India Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakakah Saudi Arabia VelTech Rangarajan Dr Sagunthala R&D Inst Sci & Te Dept ECE Morai India Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram 522302 Andhra Pradesh India Assiut Univ Fac Comp & Informat Dept Comp Sci Assiut Egypt

Facial Expression Recognition (FER) categorizes various human emotions by analyzing the features of the face, so it plays a vital role in recognizing emotions. Prior studies have focused on the issue of recognizing emotions through voices or speech. Addressing the existing method issues, this approach aims to detect voices and threedimensional images using appropriate datasets and novel deep-learning techniques. In this research, the valid Audio-Visual datasets Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Acted Facial Expression in the Wild (AFEW), and eNTERFACE'05 datasets are chosen for analysis. RAVDESS dataset contains audio, AFEW, and eNTERFACE and has three-dimensional images of humans, i.e., 3D images. SMOTE technique is presented for solving overfitting problems to balance the dataset by oversampling and under-sampling process. The research employs the Federated 3D-CNN technique to predict the accurate emotions of humans. The 3D Convolutional neural network (3DCNN) predicts accurate information of a person at any angle in image processing. Mel Frequency Cepstrum Coefficient (MFCC) is used to extract and fine-tune the voices. A significant contribution of Federated Learning with 3D-Convolutional neural network is executed for multiple clients at a time through global and local updates of weights. The proposed framework achieves a prediction accuracy of 95.72 % when compared with existing methods. This approach helps in many applications, such as analyzing emotions, healthcare, etc.

关键词： Audio-visual Convolutional neural network Deep learning Recognising emotions Federated system SMOTE

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