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检索条件"任意字段=7th International Conference on Computer and Information Processing Technology, ISCIPT 2022"

共 4197 条记录，以下是71-80 订阅

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Texture Image Feature Enhancement processing Method Based on Visual Saliency Model 7th

Texture Image Feature Enhancement Processing Method Based on...

引用

7th EAI international conference on Advanced Hybrid information processing, ADHIP 2023

作者： Wang, Yuan Wuhan Institute of Design and Sciences Wuhan430205 China

ISBN: (纸本)9783031505454

To improve the feature visualization effect of texture images, a texture image feature enhancement processing method based on visual saliency model is proposed. After collecting texture images, use soft and hard threshold denoising algorithms to denoise the texture images. Extract and decompose the features of the denoised image based on the visual saliency model. Based on the results of feature decomposition, the resolution of the texture image is reconstructed using deep learning technology, and then the texture image is described using shear wave transformation method to enhance the expression of the image’s feature information. According to the experiment, it can be seen that after applying this method, the distortion coefficient of the texture image is smaller and the clarity is higher, indicating the feasibility of this method. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Shear flow

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Personalized recommendations based on vehicle portraits 7

Personalized recommendations based on vehicle portraits

引用

7th IEEE international conference on information Systems and computer Aided Education, ICISCAE 2024

作者： Li, Na Zhang, Xinzheng Ji, Zhenlei Li, Jincheng Song, Gang Tian, Ye Research and Development Center Tianjin Richsoft Electric Power Information Technology Co. Ltd Tianjin China Internet of Vehicles Operation Division Tianjin Richsoft Electric Power Information Technology Co. Ltd Tianjin China

ISBN: (纸本)9798350350760

At present, the personalized recommendation system based on vehicle portrait has become a research hotspot. the system uses the powerful processing power of the big data platform to deeply mine the multivariate data of the vehicle. through complex data processing and analysis technology, it constructs a comprehensive and accurate vehicle portrait, and realizes a personalized recommendation algorithm suitable for a certain scenario based on the construction of the vehicle portrait. this paper uses the vehicle-user interaction behavior, user information, and vehicle information to construct a vehicle-user bipartite graph, and constructs a vehicle similarity network based on the vehicle-user bipartite graph projection method. At the same time, according to the clustering results, the vehicle similarity network is adjusted using the similarity attenuation factor to complete the recommendation of the vehicle personalized list. the algorithm not only considers the historical behavior pattern of the user's vehicle selection and the preference characteristics of the vehicle, but also adds real-time data analysis and prediction of the vehicle, realizing accurate prediction and personalized recommendation of the user's vehicle selection. In summary, the research on the personalized recommendation system based on vehicle portrait in this paper not only provides enterprises with efficient vehicle management tools, helping them to accurately manage vehicle resources and optimize operating costs, it also provides convenient vehicle selection solutions for staff using vehicles, improves the work efficiency and travel experience of staff, and realizes the accurate docking and efficient matching of vehicle resources and user needs. ©2024 IEEE.

关键词： Recommender systems

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Research on abnormal behavior detection algorithm of Internet of things devices with computer vision assistance 7

Research on abnormal behavior detection algorithm of Interne...

引用

7th IEEE international conference on information Systems and computer Aided Education, ICISCAE 2024

作者： Yuan, Limin Zhang, Wancai Zhang, Nan Yu, Jun Yang, Wenqing Zhang, Wei Sun, Zhen Wang, Tao China of Limited Company of Jilin Province Power Communication Company Changchun China Nari Technology Co. Ltd. Nanjing China

ISBN: (纸本)9798350350760

Convolutional Random Forest Vision (CRFV) is an innovative technology that cleverly incorporates Convolutional Neural networks from the field of computer Vision (CV). the Random Forest (RF) algorithm in machine learning aims to provide a powerful and efficient solution for abnormal behavior detection of Internet of things (IoT) devices. the CNN layer is responsible for front-end feature extraction and uses its depth structure to accurately capture complex patterns in image and video data, while RF integrates multiple decision trees at the back-end and uses its powerful classification and regression capabilities to classify and identify anomalies in high-dimensional features extracted by CNN. this not only makes full use of the advantages of CNN in processing visual data, but also plays into the advantages of RF in processing high-dimensional feature Spaces and reducing overfitting. It is found that CRFV not only enhances the recognition accuracy of complex and hidden abnormal behavior, but also improves the generalization ability of the model and reduces the false positive rate. It can not only effectively identify and warn the abnormal operation of IoT devices, but also provide advanced intelligent tools for maintaining network security, preventing failures and optimizing equipment management, and is an important step towards autonomous and intelligent IoT monitoring. ©2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Recognition and Visual Measurement System Based on Workpiece Primitives to be Measured 7

Intelligent Recognition and Visual Measurement System Based ...

引用

7th international conference on computer and information processing technology, iscipt 2022

作者： Li, Senwei Zhai, Tianxiang Yin, Shukai Huang, Xiaolong Mei, Xue Nanjing Tech University No. 30 Puzhu South Road Jiangbei new area Nanjing211816 China University of California 405 Hilgard Avenue Los AngelesCA90095-1405 United States Nanjing Dashu Intelligent Technology Co. Ltd No. 9 Qiande Road Jiangning National High Tech Park Nanjing211122 China

With the improvement of industrial manufacturing level, there are many kinds of workpieces and complex parameters. Traditional measurement methods are inefficient in the face of new products or different customer needs. It is urgent to develop a general and efficient measurement system. Based on primitive extraction technology of the workpiece to be measured, the intelligent recognition and vision system can automatically identify the type of workpiece and measure and calculate the relevant parameters. the core of this system is image processing and automatic recognition. On the premise of ensuring high precision and high stability, it has stronger universality and higher intelligence. © 2022 Copyright for this paper by its authors.

关键词： Image processing

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Using Bipartite Graph Embedding and Deep Learning Method Predicting RNA Protein Interactions 7

Using Bipartite Graph Embedding and Deep Learning Method Pre...

引用

7th international conference on computer information Science and Application technology, CISAT 2024

作者： Li, Danyu Ma, Yunci Chen, Jinhuang Xie, Jiedong Guangzhou Xinhua University School of Information and Intelligent Engineering Dongguan China The Fifth Affiliated Hospital Southern Medical University Division of Nephrology Guangzhou China

ISBN: (纸本)9798350375107

RNA-protein interaction (RPI) play an important role in biological activities. Using deep learning techniques to predict RPI can not only process complex data, but also obtain accurate results. this article first used a bipartite graph network to embed nodes between RNA and protein, obtaining similarity information and interaction data between RNA and protein. then, a convolutional neural network (CNN) was used for high-dimensional feature extraction and prediction. In addition, the graph neural network (GNN) GraphSAGE was combined to aggregate neighboring nodes and supplement ignored nodes. Finally, the results of the CNN and GNN were voted on to obtain the final prediction result. the experiment shown that our model received an AUC of 0.932, an AUPR of 0.935. All evaluation results indicated that that our model outperformed the other state-of-the-art methods. © 2024 IEEE.

关键词： Graph embeddings

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Robust DOA Estimation Against Outliers Based on Bias-compensated Adaptive Filtering 7

Robust DOA Estimation Against Outliers Based on Bias-compens...

引用

7th IEEE international conference on Electronic information and Communication technology, ICEICT 2024

作者： Xiao, Wudang Yu, Canping Li, Yingsong Li, Liping Zou, Dingcheng Zlatanov, Nikola Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Innopolis University Faculty of Computer and Engineering Sciences Innopolis Russia

ISBN: (纸本)9798350384437

the direction-of-arrival (DOA) estimation approach utilizing the adaptive nulling array framework has gained widespread attention due to its low complexity. However, in impulsive noise environments, traditional least mean squares (LMS) algorithm may experience a sharp decline in performance or even fail to work properly. To address this challenge, we propose a DOA estimation approach designed for impulsive noise situations based on a robust nonlinear function. In addition, the algorithm can mitigate bias caused by auxiliary array noise and uses the variable step-size strategy to enhance its performance. Furthermore, the mean stability of the proposed approach is also assessed. Simulation results demonstrate its superior performance in impulsive noise environments. © 2024 IEEE.

关键词： Adaptive filtering

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Research on target detection model of remote sensing image based on dual attention YOLO 7

Research on target detection model of remote sensing image b...

引用

7th IEEE international conference on information Systems and computer Aided Education, ICISCAE 2024

作者： Xu, Fan College of Computer Science and Technology Ocean University of China Qingdao266404 China

ISBN: (纸本)9798350350760

With the widespread application of remote sensing technology, accurate and rapid detection and identification of targets in remote sensing images have become an important research area. Traditional target detection methods often face issues with low recognition accuracy and slow response when dealing with complex backgrounds or small targets. this study proposes an improved YOLO (You Only Look Once) model integrated with a dual attention mechanism to enhance the performance of target detection in remote sensing images. this mechanism utilizes spatial attention to increase the model's sensitivity to target locations, while channel attention enhances the recognition of target features, enabling more accurate identification of small and complex environment targets. the model was tested across multiple remote sensing image datasets, and the results show significant improvements in detection accuracy and real-time performance compared to traditional YOLO models. Additionally, the implementation of the model reveals the potential of the dual attention mechanism in improving the efficiency and accuracy of remote sensing image processing. With further optimization and adjustment, this model is expected to play a significant role in various application scenarios such as environmental monitoring, urban planning, and disaster assessment. ©2024 IEEE.

关键词： Environmental monitoring

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Multi-Scale SE-SK Network for Human Falling Detection using IR-UWB Radars 7

Multi-Scale SE-SK Network for Human Falling Detection using ...

引用

7th IEEE international conference on Electronic information and Communication technology, ICEICT 2024

作者： Yang, Qinhuan Wang, Xiaoye Kang, Xinyue Zhang, Yulan Zheng, Jianchao Huizhou University College of Computer Science and Engineering Huizhou China

ISBN: (纸本)9798350384437

Impulse radio ultra-wideband (IR-UWB) radars are increasingly utilized for human fall detection owing to their non-contact, high precision, and high privacy attributes. However, the existing single-branch networks-based methods on falling detection only extract single feature maps, and fail to capture complete fall information. Furthermore, processing multiple spectra without the target unique feature results in feature loss after fusion, leading to decreased accuracy. this paper proposes a novel multi-scale network, which combines squeeze-and-excitation (SE) and selective kernel (SK) (SE-SK) networks, for human falling detection using IR-UWB Radars. Specifically, three features spectra of IR-UWB Radar data, that is, the time-Doppler, range-Doppler, and range-time feature spectra, are firstly obtained through the pre-processing of the radar echoes. then, a multiscale SE-SK network is developed for human falling detection. Experimental results demonstrate improved performance with detection accuracy of 98.2% compared to the existing singlebranch networks-based method. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Research on Software Defects Prediction and Peformance Analysis based on Machine Learning 7

Research on Software Defects Prediction and Peformance Analy...

引用

7th international conference on computer information Science and Application technology, CISAT 2024

作者： Wang, Yuqi Huang, Shuwen Xu, Yuhan Zhang, Hongwei XuZhou Medical University School of Medical Information and Engineering XuZhou China University of Science and Technology of China School of Software Hefei China

ISBN: (纸本)9798350375107

With the rapid increase in the number of various types of software, the importance of software quality has become increasingly prominent, and software testing has become an indispensable part of the software development process. With the increasing demand for software testing, various emerging technologies have been introduced into the testing field, among which machine learning has gradually become the mainstream technology in software defect prediction due to its predictive model and scalability. In this work, we utilize XGBoost model for training and prediction. Specifically, data preprocessing including missing value processing and data standardization, followed by feature selection through statistical analysis and algorithmic methods to extract the most representative features. During the model training phase, grid search is used to optimize XGBoost's hyperparameters, such as the number of trees, depth, and learning rate. During training, XGBoost improves prediction performance by gradually building a decision tree and minimizing the loss function. Finally, cross-validation and confusion matrix are used to evaluate the performance of the model to ensure high prediction accuracy and stability. Experimental analysis shows that the XGBoost model has excellent performance in prediction accuracy and performance, which effectively improves the efficiency and accuracy of software defect detection. © 2024 IEEE.

关键词： Prediction models

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Research on Visualization processing of Military Time Series Data 7

Research on Visualization Processing of Military Time Series...

引用

7th IEEE international conference on computer and Communication Engineering technology, CCET 2024

作者： Feng, Jianhong Xu, Qinghua Li, Haisheng Guo, Jiangna Lv, Haoyang Xie, Yubao Unit 32366 of the Chinese People's Liberation Army Beijing China China Academy of Electronics and Information Technology Beijing China

ISBN: (纸本)9798350355673

With the rapid development of information technology, the demand for the application of massive temporal data in the military field is gradually increasing. the visualization of time series data in the military field especially contributes to enhance battlefield situational awareness. Traditional relational databases can only record the current value of data, while temporal databases can record temporal change data in the dimension of time. In response to the problems of slow response to massive temporal data queries and low efficiency in processing temporal data in relational databases, this paper studies and analyses the relevant technologies for processing time series data in the military field. In order to achieve efficient time series data visualization processing, we design and implement the system which was based on HBase, a distributed time series database OpenTSDB and data visualization plugin DataV. Finally, we illustrate the visualization system using four different types of time series data, and demonstrate its superiority and wide application prospects in processing complex time series data. © 2024 IEEE.

关键词： Spatio-temporal data

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