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检索条件"任意字段=Proceedings of the 2023 5th International Conference on Big Data Engineering and Technology"

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5th international conference on Mechanical engineering, ICOME 2021

5th International Conference on Mechanical Engineering, ICOM...

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5th international conference on Mechanical engineering, ICOME 2021

ISBN: (纸本)9789811908668

the proceedings contain 46 papers presendted at a virtual meeting. the special focus in this conference is on Mechanical engineering. the topics include: Facial Recognition-Based Automatic Door Security System Integrated with Internet of things for Smart Home Actualization;data-Driven LoRa Precision Agriculture as a Farm Monitoring Solution and Increasing Efficiency of Water Use and Consumption of NPK Fertilizer;human Gait Parameters Calculation Based on Partial Motion data;robust H∞ Controller with Parametric Uncertainties for Anti-Rolling Gyro System;analysis of Linkage Length Effect on the Dynamics of Series Active Variable Geometry Suspension;design Optimization of Braces for three-Legged Minimum Jacket Offshore Structure;plant Maintenance Budgeting Prioritization Based on Reliability Prediction of Repairable System;Analysis of the Reduction of Vibration and Chatter Effect in Boring Process Due to the Addition of Spring Radial Vibration Damper (SRVD) on the Workpiece;Analysis of Vibration Characteristics Due to the Effect of Adding Rubber Radial Vibration Damper (RRVD) to the Boring Process;Cavitation Detection of Centrifugal Pumps Using SVM and Statistical Features;ball Screw Regenerative Shock Absorber Design and Road Test for Small Class Multi Purposes Vehicle Suspension;numerical Study on Dynamic Stabilities Prediction in a Small Unmanned Aerial Vehicle;stent Geometry Optimization for In-Stent Restenosis Prevention through Fluid–Structure Interaction Analysis;work Exchange Network Synthesis Based on Superstructure Approach;Study of the Microwave Heating Energy Effect on the Electrochemical Performance of GDCSr Electrospun Nanofiber Composite Cathode for Intermediate SOFC;tricycle with an Electric Drive Mechanism for Post-stroke Rehabilitation;the Bulb Concept in Drag Reduction on Pentamaran.

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Neural network parameter optimization for model predictive direct speed control of PMSM 5

Neural network parameter optimization for model predictive d...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Gao, Lixiao Chai, Feng School of Electrical Engineering and Automation Harbin Institute of Technology Heilongjiang Harbin150001 China

ISBN: (纸本)9781510668621

In this paper, we propose a neural network-based approach to optimize the parameters involved in the model predictive direct speed control (MPDSC) of a permanent magnet synchronous motor (PMSM). Model predictive control is a widely used technique in motor control to enhance system performance by predicting future behavior and determining control actions accordingly. However, the effectiveness of MPDSC is highly dependent on the weighting parameters of the cost function. Optimizing these parameters in a complex PMSM MPDSC control system under diverse working conditions presents a challenging task, as conventional methods may struggle to efficiently find the optimal parameters. To address this issue, we design a neural network optimization framework. Initially, an original optimization algorithm is employed to identify finite optimization parameters under various working conditions. Subsequently, the optimal parameters obtained for the finite working conditions are utilized to train a neural network. the trained network is then capable of predicting the optimal parameters across the entire range of working conditions. the proposed method is validated through simulations conducted in MATLAB, demonstrating its effectiveness in optimizing the parameters for MPDSC. © 2023 SPIE.

关键词： Speed control

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Optimization of ancient Chinese characters recognition in particular conditions 5

Optimization of ancient Chinese characters recognition in pa...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Lyu, Cheng Liu, Yuannan Wu, Chen Xu, Yinghao School of Mathematical Sciences Qufu Normal University Shandong Qufu273165 China Qingdao No. 2 Middle School Shandong Qingdao266061 China Department of Computer Science New Jersey Institute of Technology Newark07102 United States School of Management and Engineering Capital University of Economics and Business Beijing100070 China

ISBN: (纸本)9781510668621

this paper discusses optimization of ancient Chinese characters recognition in particular conditions. It aims to improve the image recognition technique exerted on ancient Chinese text. During the process, we first use images under good conditions to train the model, AlexNet and ResNet, and predict the input images. Assuming the accuracy rate is over 70%, the image is identified as one in good condition. then, the results of the models are used as output. If the accuracy does not achieve the expected rate, the images will be filtered and inputted into the models trained by the filtered picture in bad condition to predict. After several epochs of training the two models, ResNet is more appropriate for the process discussed above. Due to its high accuracy of 89%, ResNet is chosen as the model being used in the recognition process. Overall, ancient Chinese characters recognition is improved by process mentioned above. © 2023 SPIE.

关键词： Image enhancement

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Causality-inspired Comparative Learning Supervised Model for Domain Generalization 15

Causality-inspired Comparative Learning Supervised Model for...

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15th international conference on Graphics and Image Processing, ICGIP 2023

作者： Zhu, Xiaosong Yang, Bin Guo, Jingfeng Yang, Genghuang Tianjin University of Technology and Education China Tianjin Key Laboratory Information Sensing and Intelligent Control China Yanshan University China

ISBN: (纸本)9781510675018

Machine learning is good at learning general knowledge and predictive knowledge from known and limited environments, and perform well in similar environments. However, their performance in unknown environments is not satisfactory. Machine learning focus on correlation learning, but correlation is not causality. the non-causal part of the correlation forms spurious correlation that affect the model's generalization ability. therefore, it is necessary to examine machine learning from a causal perspective. By constructing structural causal models, it is found that the targets of different domains are instrumental variables of each other and can be mutually represented. they have same essential features and different domain features, which lead to causal correlation and spurious correlation with labels respectively. In this paper, a causal-inspired contrastive learning supervised model is designed to strengthen essential features and weaken domain features. With the target of improving the ability of model to capture causal correlations and reducing the interference of spurious correlations, we join transfer and autoencoder with image classification model as a cross-domain contrastive learning model. Experiments show that the proposed framework has a simple and easy-to-implement structure, it performs well on public datasets. By adopting visualization technology, the effects of this method are intuitively demonstrated. © 2024 SPIE. All rights reserved.

关键词： Machine learning

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A Rotary Kiln Temperature Detection System Based on a One-Dimensional Heat Transfer Model 5

A Rotary Kiln Temperature Detection System Based on a One-Di...

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5th international conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Wang, Cong Zhang, Qian Qi, Yuanyuan Zhu, Miaoqing Zhang, Ruiheng Zhang, Nian School of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing China School of Intelligent Technology and Engineering Chongqing University of Science and Technology Engineering Research Center for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Technology and Business University Chongqing China School of Economics and Finance Chongqing University of Science and Technology Chongqing China

ISBN: (纸本)9798331527662

As indispensable core equipment in metallurgy sectors, the operating status of rotary kilns directly impacts product quality, energy consumption, production safety, and other aspects. However, traditional temperature monitoring methods often rely on manual close-range inspections, which are not only inefficient but also pose significant safety risks. To address this challenge, the paper proposes a rotary kiln temperature detection system based on a one-dimensional heat transfer model. the system aims to achieve remote, real-time, precisely monitoring and prediction of rotary kiln temperature parameters by integrating advanced infrared thermal imaging technology with intelligent algorithms. this innovative solution can not only replaced traditional manual inspection methods, enhancing production safety and effectively, but also optimized it efficiency and reduce energy consumption through real-time data analysis and prediction, thereby achieving efficient resource utilization. Furthermore, the application of this system contributes to advancing traditional metallurgy and other industrial sectors towards a more high-end, intelligent, and green direction, making a significant contribution to the overall transformation and upgrading of China’s manufacturing industries. © 2024 IEEE.

关键词： Rotary kilns

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Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection

Spatial-Temporal Graph-Based AU Relationship Learning for Fa...

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2023 IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798350302493

this paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. this graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. the experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5. © 2023 IEEE.

关键词： Graphic methods

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the Debris Detection of High Voltage Tower Based on Visual three-Dimensional Reconstruction technology

The Debris Detection of High Voltage Tower Based on Visual T...

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international conference on Machine Learning, big data and Business Intelligence (MLBDBI)

作者： Peipei Sun Jieqiu Bao Chong Tang Yang Zhang Da Peng Jian Li Shenyang Institute of Engineering College of Electric Power Shenyang China Shenyang Institute of Engineering Engineering Training Centre Shenyang China Shenyang Institute of Engineering College of Automation Shenyang China

At present, the main method for detecting foreign bodies in high voltage towers is manual detection. this method is not effective and cannot find all foreign bodies. therefore, this study proposes a foreign object detection technology based on unsupervised depth estimation and three-dimensional reconstruction, which can detect foreign objects on high voltage towers efficiently and accurately. the results show that after obtaining the distance information of the depth map, the coordinates of each point are determined by applying the three-dimensional reconstruction theory. According to these coordinate information, the model can be restored, and there are no too many and missing stray points in the boundary part of the model, which can minimize the subjective judgment error when applied to daily inspection. this method provides a new direction for power system detection.

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Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph databases 39

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Dat...

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39th IEEE international conference on data engineering, ICDE 2023

作者： Qiu, Rui Ming, Yi Hong, Yisen Li, Haoyu Yang, Tong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peking University Academy for Advanced Interdisciplinary Studies China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Graphs are good at presenting relational and structural information, making it powerful in the representation of various data. For the efficient storage and processing of graph-like data, graph databases have been rapidly developed and extensively studied. However, graph databases mostly use adjacency lists as their basic data structure (e.g., Neo4j), which could result in poor performance of edge due to the skewed degree distribution of *** design the Wind-Bell Index to address this problem. Wind-Bell Index is a memory-efficient index data structure, which can be attached to existing graph databases to speed up the edge. We have fully implemented our data structure in Neo4j, the most popular graph database today, and conduct theoretical and experimental analysis to evaluate the performance. theoretical results prove the high query efficiency of our algorithm. And experimental results show that the average edge query speed is increased by hundreds of times compared with the original query interface of Neo4j. We believe that the excellent performance and scalability of Wind-Bell Index make it suitable for the application in a variety of graph databases. © 2023 IEEE.

关键词： Graph databases

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Analysis on the Construction of College Campus Sports Culture System Platform Under the Background of big data 5th

Analysis on the Construction of College Campus Sports Cultur...

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5th international conference on Innovative Computing, IC 2022

作者： Wang, Wanchun Guangzhou College of Technology and Business Guangzhou510850 China

ISBN: (纸本)9789811941313

the combination of college campus sports and culture itself is a complex thing. Under the background of big data, campus sports culture has added new conceptual value. Based on the big data background, this paper analyzes the construction of college campus sports culture system platform, so as to more deeply and comprehensively understand sports culture and clearly interpret the significance of campus sports culture to the whole modern society. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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Design of digital simulation software for flight control software based on multi-source data fusion 5

Design of digital simulation software for flight control sof...

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5th international conference on Computer Information Science and Application technology, CISAT 2022

作者： Zhang, Chao Shao, Ai ju Cheng, Guang yao Wang, Dong dong Zhang, Tian Wang, Yue xi Fang, Hai hong Beijing Institute of Space Long March Vehilce 1 Nandahongmen Fengtai District Beijing China

ISBN: (纸本)9781510660076

Aiming at the difficulty of verification and test caused by the complex structure and timing logic of multi-source data fusion flight control software algorithm, this paper puts forward the digital simulation technology of flight control software, which simulates the reference clock of flight control software, the external link equipment of flight control software, and the functions of sensor data, actuator data, data communication interface, hardware driver, thread scheduling and so on. the simulation software technology enables the self-development stage of flight control software to get rid of the constraints of hardware simulator debugging conditions, quickly and conveniently verify the correctness of the timing logic and algorithm implementation of flight control software, greatly shorten the cycle of software development and unit test, save the development cost and improve the efficiency and quality of flight control software development. © 2022 SPIE.

关键词： Program debugging

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