電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501018
Jian-Fang Xue Jian-Fang Xue, Qing-Chuan Liu Jian-Fang Xue, Xiao-Yang Zhang Qing-Chuan Liu, Rui Fan Xiao-Yang Zhang, Wei-Min Liu Rui Fan
{"title":"Research on Real Time Data Monitoring Method for Intelligent Factory Equipment Based on Ethernet Communication","authors":"Jian-Fang Xue Jian-Fang Xue, Qing-Chuan Liu Jian-Fang Xue, Xiao-Yang Zhang Qing-Chuan Liu, Rui Fan Xiao-Yang Zhang, Wei-Min Liu Rui Fan","doi":"10.53106/199115992024023501018","DOIUrl":"https://doi.org/10.53106/199115992024023501018","url":null,"abstract":"\u0000 This article focuses on the production and processing of sleeve parts, using Ethernet as a data transmission medium to complete data collection and monitoring of typical intelligent factory processing workshops. Firstly, the layout design of the intelligent factory equipment layer, communication layer, and system application layer was completed. Then, unified management of standard and non-standard protocols was implemented for communication between different devices. Then, based on the sensors and sensor data used in each production process, a data collection scheme was designed, and finally, the data collection system was designed, Real time monitoring of data between various devices in the system can guide enterprise production.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"985 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140467591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501021
Xue-Feng Jiang Xue-Feng Jiang, Ken-Cheng Xue-Feng Jiang, Zhi-De Li Ken-Cheng
{"title":"Retinal OCT Image Classification Based on CNN-RNN Unified Neural Networks","authors":"Xue-Feng Jiang Xue-Feng Jiang, Ken-Cheng Xue-Feng Jiang, Zhi-De Li Ken-Cheng","doi":"10.53106/199115992024023501021","DOIUrl":"https://doi.org/10.53106/199115992024023501021","url":null,"abstract":"\u0000 Computer-aided diagnosis of retinopathy is a hot research topic in the field of medical image classification, where optical coherence tomography (OCT) is an important basis for the diagnosis of ophthalmic diseases. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, two publicly available retinal OCT image datasets are integrated and screened. Then, an end-to-end deep learning algorithmic framework based on CNN-RNN Unified Neural Networks was proposed to automatically and reliably classify six categories of retinal OCT images. Numerical results suggest that the proposed algorithm works well in terms of accuracy, precision, sensitivity and specificity, approaching or even partially surpassing the performance of clinical experts. It is valuable in promoting computer-aided diagnosis towards practical clinical applications and improving the efficiency of clinical diagnosis of retinal diseases.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"10 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140463325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501020
Zhao-Nian Li Zhao-Nian Li, Yao-Chen Zhang Zhao-Nian Li, Zhen-Jiang Zhang Yao-Chen Zhang, Wen-Hui Wang Zhen-Jiang Zhang, Mao-Jie Zhang Wen-Hui Wang, Guo-Hua Shi Mao-Jie Zhang
{"title":"Research on Vehicle Task Management Based on TBMADDPG","authors":"Zhao-Nian Li Zhao-Nian Li, Yao-Chen Zhang Zhao-Nian Li, Zhen-Jiang Zhang Yao-Chen Zhang, Wen-Hui Wang Zhen-Jiang Zhang, Mao-Jie Zhang Wen-Hui Wang, Guo-Hua Shi Mao-Jie Zhang","doi":"10.53106/199115992024023501020","DOIUrl":"https://doi.org/10.53106/199115992024023501020","url":null,"abstract":"\u0000 Recently, the traditional cloud computing network of vehicle networking has some problems to be solved: 1) the security trust between the vehicle and the subgrade unit; 2) The vehicle may be attacked by potentially malicious edge servers during task unloading. In this paper, in order to solve the above problems, aiming at the security problem of the edge computing network in the vehicle task unloading and resource allocation problems of multi-vehicle and multi-roadbed units in the urban intersection scene, the vehicle task unloading and resource management optimization algorithm and trust model based on the vehicle edge computing network are constructed, and the approximate optimal simulation is carried out for the urban intersection scene. Simulation results show that the proposed algorithm can effectively improve the overall efficiency of the system.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"879 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140467476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501014
Rui Fan Rui Fan, Wei-Min Liu Rui Fan, Jian-Fang Xue Wei-Min Liu, Qing-Chuan Liu Jian-Fang Xue, Xiao-Yang Zhang Qing-Chuan Liu
{"title":"Design of Data Monitoring System for Intelligent Production Line System Based on Digital Twin","authors":"Rui Fan Rui Fan, Wei-Min Liu Rui Fan, Jian-Fang Xue Wei-Min Liu, Qing-Chuan Liu Jian-Fang Xue, Xiao-Yang Zhang Qing-Chuan Liu","doi":"10.53106/199115992024023501014","DOIUrl":"https://doi.org/10.53106/199115992024023501014","url":null,"abstract":"\u0000 This article establishes a digital twin model for automated production lines. By establishing a more complete digital model, it achieves comprehensive reproduction of the production line, and then extracts and maps data from various production modules in the digital twin to ensure real-time data. In the software development phase, data analysis is conducted on the production and processing process to achieve prediction and management of production. Finally, based on the established digital twin, online simulation virtual debugging can be achieved when flexibly changing production goals, thereby saving production cycles and costs. Therefore, the digital twin platform established in this article has practical significance.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"464 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501016
Xiao-Yang Zhang Xiao-Yang Zhang, Rui Fan Xiao-Yang Zhang, Wei-Min Liu Rui Fan, Jian-Fang Xue Wei-Min Liu, Qing-Chuan Liu Jian-Fang Xue
{"title":"Optimization Method for Robot Moving Object Recognition and Grasping Strategy Based on Binocular Vision","authors":"Xiao-Yang Zhang Xiao-Yang Zhang, Rui Fan Xiao-Yang Zhang, Wei-Min Liu Rui Fan, Jian-Fang Xue Wei-Min Liu, Qing-Chuan Liu Jian-Fang Xue","doi":"10.53106/199115992024023501016","DOIUrl":"https://doi.org/10.53106/199115992024023501016","url":null,"abstract":"\u0000 This article proposes a more accurate grasping strategy for the recognition and grasping of moving targets based on binocular vision cameras. Firstly, the front and back scene separation algorithm is used to identify the moving target grabbing object in the production line. Then, by setting an appropriate threshold, the SiamMask target tracking algorithm is improved to achieve dynamic target tracking. Finally, the conveyor belt speed is detected and the real-time position of the object is obtained. Then, the Cartesian strategy is used to achieve path planning and optimization methods for the robotic arm during movement. Through experimental simulation, the effectiveness and stability of the proposed method in this paper have been demonstrated.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"290 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140468604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501012
Qing-Chuan Liu Qing-Chuan Liu, Xiao-Yang Zhang Qing-Chuan Liu, Rui Fan Xiao-Yang Zhang, Wei-Min Liu Rui Fan, Jian-Fang Xue Wei-Min Liu
{"title":"A Method for Industrial Robots to Grasp and Detect Parts of Instrument under 3D Visual Guidance","authors":"Qing-Chuan Liu Qing-Chuan Liu, Xiao-Yang Zhang Qing-Chuan Liu, Rui Fan Xiao-Yang Zhang, Wei-Min Liu Rui Fan, Jian-Fang Xue Wei-Min Liu","doi":"10.53106/199115992024023501012","DOIUrl":"https://doi.org/10.53106/199115992024023501012","url":null,"abstract":"\u0000 Guiding industrial robots to complete grasping tasks through machine vision is an important part of achieving autonomous robot operation. This article explores the control method of industrial robots under 3D vision, focusing on the feature that two-dimensional vision can only perform color and pose recognition but lacks depth recognition. Firstly, a high-precision point cloud registration calibration matrix solution method is proposed. Then, an improved recognition model is designed to address the issue of how vision guides robots to grasp and detect. This model integrates feature extraction and object detection modules, and describes the parameters of each module. Finally, the effectiveness of the proposed method is verified in the assembly scene of gas instruments. Finally, experimental results show that, the method proposed in this article can limit the grasping accuracy to within 2 millimeters in guiding robots to grasp detection scenes, achieving the expected effect.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"378 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140466396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501004
Wei-Kai Sun Wei-Kai Sun, Xiao-Mei Wang Wei-Kai Sun, Bin Wang Xiao-Mei Wang, Jia-Sen Zhang Bin Wang, Hai-Yang Du Jia-Sen Zhang
{"title":"MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks","authors":"Wei-Kai Sun Wei-Kai Sun, Xiao-Mei Wang Wei-Kai Sun, Bin Wang Xiao-Mei Wang, Jia-Sen Zhang Bin Wang, Hai-Yang Du Jia-Sen Zhang","doi":"10.53106/199115992024023501004","DOIUrl":"https://doi.org/10.53106/199115992024023501004","url":null,"abstract":"\u0000 In recent years, with the rapid development of science and technology, many new technologies have made people’s exploration of the ocean deeper and deeper, and due to the requirements of national defense and marine development, the underwater acoustic sensor network (UASN) has been paid more and more attention. Nevertheless, the underwater acoustic channel has the properties of considerable propagation delay, limited bandwidth, and unstable network topology. In order to improve the performance of the medium access control (MAC) protocol in UASN, we propose a new MAC protocol based on the Slotted-FAMA of Multiple Reception (MR-SFAMA) protocol. The protocol uses the Q-Learning algorithm to optimize the multi-receiver handshake mechanism. The current state is judged according to the received node request, and the Q-table is established. Through the multi-round interaction between the node and the environment, the Q-table is continuously updated to obtain the optimal strategy and determine the optimal data transmission scheduling scheme. The reward function is set according to the total back-off time and frame error rate, which can reduce the packet loss rate during network data transmission while reducing the delay. In addition, the matching asynchronous operation and uniform random back-off algorithm are used to solve the problem of long channel idle time and low channel utilization. This new protocol can be well applied to unstable network topology. The simulation results show that the protocol performs better than Slotted-FAMA and MR-SFAMA regarding delay and normalized throughput.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"209 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140468546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501007
Xiao-Xiao Ge Xiao-Xiao Ge, Wen-Feng Wang Xiao-Xiao Ge, Lalit Mohan Patnaik Wen-Feng Wang
{"title":"A Novel Deep Neural Network for Facial Beauty Improvement","authors":"Xiao-Xiao Ge Xiao-Xiao Ge, Wen-Feng Wang Xiao-Xiao Ge, Lalit Mohan Patnaik Wen-Feng Wang","doi":"10.53106/199115992024023501007","DOIUrl":"https://doi.org/10.53106/199115992024023501007","url":null,"abstract":"\u0000 This study delves into how to combine deep learning and fuzzy logic reasoning to evaluate facial aesthetics and provide targeted makeup recommendations. To further optimize the prediction results, we adopted the BLS method to correct the prediction residuals generated by ResNet-50. Specifically, the predicted appearance score can be expressed as score = p + δ, where p is the predicted result and δ represents the predicted residual of the system. After determining the beauty rating, we further studied four different makeup combinations (x1, x2, x3, x4). Moreover, we introduced fuzzy logic reasoning, defined fuzzy sets and fuzzy relationships, and established membership matrices for each makeup combination. The results of these fuzzy logical reasoning allow us to set a value range of m, n for each makeup method. Based on these reasoning results, we have come up with makeup recommendations for different facial aesthetics. Performance our system with the data collected from internet (accuracy of the calculation = 93.26%), from one volunteer (accuracy of the calculation = 98.14%) and from the both with different makeup skills (accuracy of the calculation = 95.63%) demonstrated that the visual sensing problem is feasible and will be a novel direction for the related engineering applications.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"85 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140462707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501010
Hua-Ping Zhou Hua-Ping Zhou, Jie Zhang Hua-Ping Zhou, Ke-Lei Sun Jie Zhang, Qi-Fen Wen Ke-Lei Sun, Qi Zhao Qi-Fen Wen, Ying-Jie Guo Qi Zhao
{"title":"Small Object Detection in Remote Sensing Based on Contextual Information and Attention","authors":"Hua-Ping Zhou Hua-Ping Zhou, Jie Zhang Hua-Ping Zhou, Ke-Lei Sun Jie Zhang, Qi-Fen Wen Ke-Lei Sun, Qi Zhao Qi-Fen Wen, Ying-Jie Guo Qi Zhao","doi":"10.53106/199115992024023501010","DOIUrl":"https://doi.org/10.53106/199115992024023501010","url":null,"abstract":"\u0000 Many small objects, for instance vehicles and small ships, are encountered in remotely sensed images. However, small object detection has been a challenging task in remote sensing because of the problem that small objects are easily missed and influenced by the background. To address this challenge, we propose a detection method based on contextual information and attention, divided into two main parts. Firstly, for purpose of further improve the backbone network features to derive more contextual information, a multi-branch feature enhancement module is constructed to fuse multiple sensory field features to improve the ability of the backbone network to extract feature information; secondly, a new effective channel attention mechanism is proposed to reduce problems such as information confusion caused by the feature fusion process, thus reducing the influence of the background. Compared with other methods, it effectively improves the detection of small object among remote sensing images.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"35 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140465058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
電腦學刊Pub Date : 2024-02-01DOI: 10.53106/199115992024023501011
Xiang-Yun Yi Xiang-Yun Yi, Xiao-Bo Dong Xiang-Yun Yi, Liang-Gui Zhang Xiao-Bo Dong, Yan-Chao Sun Liang-Gui Zhang, Wen-Tao Li Yan-Chao Sun, Tao Zhang Wen-Tao Li
{"title":"Compressive Perception Image Reconstruction Technology for Basic Mixed Sparse Basis in Metal Surface Detection","authors":"Xiang-Yun Yi Xiang-Yun Yi, Xiao-Bo Dong Xiang-Yun Yi, Liang-Gui Zhang Xiao-Bo Dong, Yan-Chao Sun Liang-Gui Zhang, Wen-Tao Li Yan-Chao Sun, Tao Zhang Wen-Tao Li","doi":"10.53106/199115992024023501011","DOIUrl":"https://doi.org/10.53106/199115992024023501011","url":null,"abstract":"\u0000 Applying Compressed Sensing (CS) technology to robot vision image transmission, an effective method for image reconstruction in robot imaging is proposed to improve the accuracy of reconstruction. Reconstructing images using a mixed sparse representation of DCT and circularly symmetric contour wave transform, the basic algorithm used is the Smoothed Projection Landweber (SPL) algorithm, which optimizes the coefficients under different sparse transformations by incorporating hard thresholding and binary thresholding methods for different sparse bases during iterations. The experiment shows that compared with single sparse base image reconstruction, the proposed reconstruction method has improved reconstruction accuracy.\u0000 \u0000","PeriodicalId":345067,"journal":{"name":"電腦學刊","volume":"1339 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140466995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}