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Deep Clustering Model for Time-Series data Based on Recurrence Plot and Variational Auto-Encoder 3

Deep Clustering Model for Time-Series Data Based on Recurren...

引用

3rd international conference on Digital signal and Computer Communications, DSCC 2023

作者： Xu, Cheng Chen, Junjie College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Huhhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Hohhot010018 China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

Time-series data are trendy and periodic, and have high data dimensionality, the current clustering methods cannot effectively target these characteristics. A recurrence plot variational auto-encoder deep clustering (RPVAEDC) model based on recurrence plot and variational auto-encoder is proposed to address the characteristics of time-series data. The time-series data are first transformed into recurrence plots to reveal their trends and periodicity;then the recurrence plots are fed into a deep clustering model for feature extraction and dimensionality reduction, and the distribution of the transformed data is normalized by variational auto-encoder;then the clustering results are obtained by adding a clustering layer to combine the auto-encoder reconstruction loss and clustering loss. It is experimentally demonstrated that the silhouette coefficient scores are achieved significantly better than other clustering algorithms on the public data sets. © 2023 SPIE.

关键词： Clustering algorithms

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Intelligent Campus Management System Combining Computer data processing and GIS Technology 3

Intelligent Campus Management System Combining Computer Data...

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3rd international conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Liu, Yue Zhang, Ying Ji, Xiang School of Urban Construction Hainan Vocational University of Science and Technology Hainan Haikou571126 China School of Information Hainan Vocational University of Science and Technology Hainan Haikou571126 China

ISBN: (纸本)9798400716775

In order to improve the management effect of intelligent campus in colleges and universities, this paper combines computer data processing technology and GIS technology to build an intelligent campus management system. This design system uses it to deal with the integration of multiple data, associate spatial geographical position with attributes, or associate spatial position with spatial position, realize the position query of attribute map, and realize the interaction between maps. Based on spatial information technology (BIM + GIS), Internet of Things technology, big data and cloud computing technology, through the "technology + operation and maintenance management + campus management"model, this paper build a smart campus operation and maintenance management information system. After that, this paper studies the system from two angles of campus teaching management and campus daily management, and compares the methods proposed in this paper with data mining methods. Through the results of experimental analysis, we can see that the model proposed in this paper can effectively improve the management effect of smart campus, and provide an effective reference for the construction of smart campus in colleges and universities. © 2023 ACM.

关键词： Geographic information systems

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A More Efficient Way to Control Traffic Lights Through AI-Led Smart City Management 3

A More Efficient Way to Control Traffic Lights Through AI-Le...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Choudhary, Shilpa Ali, Shaik Shabbir Babu, N. Ramesh Sharma, Harshita Kaliraman, Bhawna Dhankhar, Yash Hyderabad India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Vaddeswaram India Koneru Lakshmaiah Education Foundation Department of Mathematics Vaddeswaram India Dpg Institute of Technology and Management Department of Computer Science and Engineering Gurugram India

ISBN: (纸本)9798350342338

Urban regions are nevertheless plagued by traffic congestion, which increases travel times, fuel use, and greenhouse gas emissions. To lessen traffic congestion and enhance the performance of traffic systems, effective traffic control is crucial. The ideal flow of traffic is not achieved by traditional traffic light control technologies, which are based on planned signal plans or fixed timings that do not adjust to real-time traffic circumstances. By dynamically changing signal timing based on real-time traffic data, artificial intelligence (AI) has emerged as a viable method to optimize traffic signal regulation. This paper suggests a real-time traffic signal timing optimization system that is powered by AI and leverages machine learning methods. This suggested work is a traffic light control system that uses artificial intelligence and offers a number of *** instance, a shorter trip distance, improved fuel economy, and lower pollutants. Additionally, the system is easily upgradeable with new data sources or algorithms and may be incorporated with current traffic management systems. It suggested using an artificial intelligence-based traffic light control system (TLCS) to greatly enhance traffic flow and relieve congestion in metropolitan areas. Real-time data and machine learning algorithms combined to enable dynamic and adaptive signal control, resulting in more effective traffic management and improved quality of life for city dwellers. This clarifies their potential to enhance urban transportation and lessen congestion. © 2023 IEEE.

关键词： Fog Haze OpenCv radars Sensors YOLOv3

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Citrus Detection Application of Based on Improved YOLOv3 Algorithm 3

Citrus Detection Application of Based on Improved YOLOv3 Alg...

引用

3rd international conference on Machine Learning and Computer Application, ICMLCA 2022

作者： Long, Jinhui Wang, Xiaokan Henan Electrical and Mechanical Vocational College Xinzheng451191 China

ISBN: (纸本)9781510664814

In order to solve the problem of missed detection and false detection caused by light change, leaf occlusion, fruit overlap and other citrus detection factor, an improved YOLOv3 citrus detection depth learning algorithm is proposed to achieve accurate and rapid recognition of citrus in natural environment. The algorithm uses image scaling and rotation transformation, homomorphic filtering processing and other methods to process the citrus object pictures. The 1− IOU value is the distance function, and the genetic algorithm based on K-means clustering is used to optimize the initial anchor box size, all that are enriched the citrus image data set. So the algorithm achieves effective image enhancement and improves the detection resolution and accuracy. The experimental results show that the average accuracy of the citrus detection algorithm reaches 88.7%, the average detection time of single width pixels 640×448 pixels is 131 ms and the system efficiency increases by 9.6%. © 2023 SPIE.

关键词： Genetic algorithms

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Research on data Encryption Technology of Power Satellite IoT Based on IPK 3

Research on Data Encryption Technology of Power Satellite Io...

引用

3rd international conference on Digital signal and Computer Communications, DSCC 2023

作者： Zheng, Ying Mo, Ting Wang, Dingguo Wu, Weinong Zhao, Yanyan Fu, Quanyong Information and Telecommunication Branch of State Grid Chongqing Electric Power Com. Ltd Chongqing400014 China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

Under the new technology and application of the power satellite Internet of Things, satellite communication will inevitably face more security risks. This paper studies the delay problem of satellite communication, analyzes the security risk caused by the delay, and proposes a route for the realization of satellite channel encryption technology. Low-latency, high-efficiency IPK identification public key technology is used for power satellite IoT applications. By comparing with the traditional PKI certificate authentication technology, the advantages of IPK technology in satellite channel encryption technology are established, and the environment is built to test the IPK signature verification time. By contrast with communication delay, it is negligible. Based on IPK technology, satellite communication data can be encrypted and transmitted, with low-latency and high-efficiency secure computing, which has no impacts on satellite communication delay, and can fully meet the needs of power satellite IoT applications. © 2023 SPIE.

关键词： Authentication

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Optimization of compression algorithm for snapshot mosaic hyperspectral images based on CCSDS 123 standard 3

Optimization of compression algorithm for snapshot mosaic hy...

引用

3rd international conference on Optics and Image processing, ICOIP 2023

作者： Zeng, Xu Huang, Haowen Lv, Deyuan Zeng, Min Ping, Yongfu Wei, Shibi Luo, Xudong Zhao, Zhigang College of New Materials and New Energies Shenzhen Technology University Shenzhen518118 China NBL Imaging System Ltd. Guangzhou510670 China

ISBN: (纸本)9781510667426

In this paper, the compression algorithm recommended by the Consultative Committee on Space data System (CCSDS) 123 standard is optimized according to the spectral correlation of snapshot mosaic hyperspectral images (SSM HSI) to achieve a higher compression ratio. A novel inter-spectral processing module is added to the predictor to calculate the inter-spectral correlation between each spectral band of the hyperspectral image. The required one-dimensional spectral neighborhoods for prediction are selected based on the highest to lowest correlation order, which allow more accurate prediction of the current sample. This approach improves the efficiency of compression. The optimization was performed for Fast Lossless (FL) predictor with sample adaptive encoder recommended by CCSDS-123.0-B-1 standard, FL Extended (FLEX) predictor with sample adaptive encoder, and hybrid encoder recommended by CCSDS-123.0-B-2 standard, respectively, to compare the compression ratio before and after optimization. The results showed that the compression performance was improved by 0.015%-3.1%, 0.01%-2.29% and 0.01%-1.95% respectively. Further optimization with larger compression ratio could be realized by parameters adjustment of the algorithm proposed in this paper. © 2023 SPIE. All rights reserved.

关键词： signal encoding

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Analyzing the Performance of BERT for the Sentiment Classification Task in Bengali Text 3rd

Analyzing the Performance of BERT for the Sentiment Classifi...

引用

3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Banshal, Sumit Kumar Uddin, Ashraf Piryani, Rajesh Alliance Univ Dept Comp Sci & Engn Bangalore Karnataka India Amer Int Univ Bangladesh Dhaka Bangladesh Univ Toulouse III Paul Sabatier UT3 IRIT Toulouse France

ISBN: (纸本)9783031640698;9783031640704

The recent era has seen significant growth of technologies in the field of Natural Language processing (NLP). But the scarce resource languages like Bengali have not got much attention from the research community. The BERT language model has laid a very positive impact on the performance of the NLP tasks. Although several others language models came into the scenario, we investigate the performance of BERT model and other conventional methods for the sentiment classification task in Bengali text. The obtained result shows that BERT overperformed other conventional machine learning and lexicon-based methods in all aspects of the performance metrics. Along with BERT, conventional methods namely Logistic Regression, Decision Tree, SVM, Random Forest, Naive Bayes and Neural Network were implemented. Besides these methods a lexicon-based approach was used to see the overall variation in the results. The lexicon resource for Benali was created for this implementation.

关键词： Sentiment analysis NLP BERT Bengali textual data

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data Mining of Garment Pattern Based on Decision Tree Algorithm 3rd

Data Mining of Garment Pattern Based on Decision Tree Algori...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Li, Jing Li, Chen Shandong Vocational College of Light Industry Shandong Zibo255300 China

ISBN: (纸本)9783031631320

With the development of economy, society and culture, the traditional tailor-made clothing has been unable to meet people’s needs for personalization, fit and online customization of clothing. The clothing industry has gradually changed from the original large-scale and standardized production to flexible and personalized customization. data mining is the process of extracting useful information from data. data mining is a process of discovering patterns in a large amount of data by applying statistical and computational techniques to data. Decision tree algorithm is such a technology that helps to find patterns in a large amount of data. It uses decision tree to find the best method to classify objects according to their attributes or characteristics. In recent years, the application of advanced technologies, such as computer graphics, digital image processing and artificial intelligence, has brought new opportunities to the garment industry, promoted the mode update of the garment industry, and also promoted the digitalization and intelligence of garment design. Garment digital version is the key problem and research hotspot of garment digital technology. In this paper, the research and analysis of data mining of garment pattern based on decision tree algorithm. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Decision trees

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Research of Footwear Virtual Simulation Design Laboratory Based on Computer Big data Technology

Research of Footwear Virtual Simulation Design Laboratory Ba...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Guobao Huang Tao Yin School of Art and Design Guangdong Baiyun University Guangzhou China

This paper studies the application of multi-modal modeling method in the design and development of sneakers samples based on two-dimensional digital collaborative design and three-dimensional digital simulation. The multi-body system is modeled by using AD-AMS. A walking simulation system based on neural network is proposed, and the ILC walking simulation system is realized by combining it with MA TLAB. The purpose of this paper is to explore the comfort of shoes in the state of exercise. Simulation analysis shows that the mathematical model is reasonable and feasible, and lays a theoretical basis for the development of prototype.

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Latent ODEs-based data compression method for power system 3

Latent ODEs-based data compression method for power system

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2023 3rd international conference on Electrical Engineering and Computer Technology, ICEECT 2023

作者： Xue, Xiangxi Su, Ya School of Electrical Engineering Shandong University Jinan Shandong250061 China

In order to effectively compress the power system data and reduce the space occupied by data storage and transmission, this paper proposes a data compression method based on Latent Segmented ODE (LatSegODE) for the large-scale steady-state data generated during the working process of the power system. By training the ODE model using a large amount of steady-state data, a latent ODE model is generated to characterize the steady-state data of the power system, thereby significantly reducing the storage space occupied by the data. During the experimental stage, compression testing is carried out on the static data of the power system of a power grid company. According to the quantitative evaluation results of the two indices of normalized mean square error and data compression ratio, the proposed method has obvious compression advantages, and the compressed signals have a high degree of fitting to the actual sampled signal waveforms. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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