AUTOMATIC CONTROL AND COMPUTER SCIENCES最新文献

The Application of Deep Network and Reinforcement Learning for Art Design in Graphical User Interface Wireframe Generation 深度网络和强化学习在图形用户界面线框图生成中的应用

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700567

Yun Zhou

{"title":"The Application of Deep Network and Reinforcement Learning for Art Design in Graphical User Interface Wireframe Generation","authors":"Yun Zhou","doi":"10.3103/S0146411625700567","DOIUrl":"10.3103/S0146411625700567","url":null,"abstract":"The development of graphical user interfaces has made significant progress in the past few decades, playing an important role in computer user experience and human-computer interaction. However, at present, there is a lack of professional experienced workers in graphical user interfaces, and the art design of graphical user interfaces has low attention in real life. Therefore, this research introduces reinforcement learning algorithm, combines it with deep network, and realizes automation and intelligence in the generation of art design oriented graphical user interface and graphical user interface wireframe. The test results indicate that the graphical user interface method proposed in this study has average values of 0.075 and 0.869 for the Fréchet inception distance and one nearest neighbor accuracy in the category subset, and 0.070 and 0.823 for the development company subset. The comprehensive average scores for the three indicators of aesthetics, color coordination, and structure in manual evaluation are 3.11, 3.30, and 3.21, respectively. The research proposes a wireframe generation method with average values of Fréchet inception distance and one nearest neighbor accuracy of 0.082 and 0.911, respectively. The average value of position deviation index is 1.018, the average score of manual evaluation is 3.32, and the average values of structural similarity and spatial Euclidean distance are 0.363 and 3.683. The experimental results indicate that the method designed in this study generates a graphical user interface with higher quality than traditional common methods, and is more aesthetically pleasing, in line with popular art aesthetics.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 3","pages":"402 - 415"},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007837","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}

引用次数: 0

Resolving the Trilemma Challenge in Blockchain: An Integrated Consensus Mechanism for Balancing Security, Scalability, and Decentralization 解决区块链中的三难困境挑战：平衡安全性，可扩展性和去中心化的集成共识机制

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700476

Khandakar Md Shafin, Saha Reno

引用次数: 0

Design of Monitoring and Control System of Hydraulic Pump Station Based on Internet of Things 基于物联网的液压泵站监控系统设计

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700506

Ping Xu, Shuangfei Zhang

引用次数: 0

Design of Real Time Target Tracking Method for Film and Television Video Based on Deep Learning under Visual Communication 视觉传达下基于深度学习的影视视频实时目标跟踪方法设计

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700543

Qiang Fu

{"title":"Design of Real Time Target Tracking Method for Film and Television Video Based on Deep Learning under Visual Communication","authors":"Qiang Fu","doi":"10.3103/S0146411625700543","DOIUrl":"10.3103/S0146411625700543","url":null,"abstract":"Video target tracking has gained a lot of interest and applications due to the quick development of computer vision and artificial intelligence. Adaptive modified target tracking approach based on target prediction algorithm and deep reinforcement learning is researched to realize exact positioning of the occluded target and to increase the efficiency, precision, and accuracy of real-time tracking of video targets. And combined with secondary correlation, a multitarget tracking algorithm is proposed to realize target tracking accuracy. The validation experiments are conducted in this research, and the findings indicate that the target tracking effect is at its greatest when the weight adjustment coefficient (p = 0.061) is attained, along with the peak area ratio and similarity of the correlation filtering response reaching their ideal advantage. The target frame only needs to move less than 5 movements in most of the images to successfully capture the target. It is found that the tracking accuracy of the proposed research method has comparable tracking accuracy with the MDNet with optimal performance, while the processing efficiency is improved by 80%, which is an accurate and efficient target tracking method. It is useful as a reference for target recognition in video and has some relevance for target localisation research in subsequent tracking systems.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 3","pages":"376 - 388"},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007942","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}

引用次数: 0

Load Balancing Algorithms for Comparative Pattern Mining 比较模式挖掘的负载平衡算法

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700488

Boqiang Cao

{"title":"Load Balancing Algorithms for Comparative Pattern Mining","authors":"Boqiang Cao","doi":"10.3103/S0146411625700488","DOIUrl":"10.3103/S0146411625700488","url":null,"abstract":"For addressing the issues of ineffective mining and memory overflow when dealing with high-dimensional and large-scale datasets with traditional comparative pattern mining, and to further lift the limitation of a single machine’s own hardware, the study proposes a parallel comparative pattern mining algorithm based on Spark cluster environment. By constructing an extended data collection project tree, introducing an optimised decision tree for mining, and improving the related load balancing strategy, effective mining of large-scale and high-dimensional datasets is achieved. Experiments show that the algorithm proposed in the study has a maximum value of 1883 and a minimum value of 1549 for the number of contrasting patterns mined in the small-scale and low-dimensional Mushroom dataset, which is slightly higher than the mining method of strong jump revealed patterns with good classification performance. In the large-scale and high-dimensional dataset US census1990, the overall running time of the algorithm of the study is low compared to the cryptogrowth algorithm (Tmax 43.2 min, Tmin 18.4 min), and finally the failure request rate of the algorithm itself and the improved and weighted polling algorithms are ompared separately, and the results show that the improved algorithm takes the lowest time of 4%. The experiment showcases that the classification effect of the studied algorithm is good, the load balancing strategy of the improved algorithm is effective, and the overall performance of the algorithm is good.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 3","pages":"317 - 327"},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007839","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}

引用次数: 0

Modeling and Control Research of Fractional-Order Memristor Based on Optimization Algorithm in Frequency Domain 基于频域优化算法的分数阶忆阻器建模与控制研究

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700452

Lanfeng Chen, Dinyyu Xue, Xinshu Cui

引用次数: 0

Feature Extraction and Recognition Based on Integrated Material Painting Images 基于综合材料绘画图像的特征提取与识别

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S014641162570052X

Ying Cheng, Hongwei Li, Tao Meng, Lu Bai, Yan Li

{"title":"Feature Extraction and Recognition Based on Integrated Material Painting Images","authors":"Ying Cheng, Hongwei Li, Tao Meng, Lu Bai, Yan Li","doi":"10.3103/S014641162570052X","DOIUrl":"10.3103/S014641162570052X","url":null,"abstract":"With the rise of digital art, the demand for classification and recognition technology of images is increasing. The purpose of this study is to improve the accuracy of feature extraction and classification recognition in Chinese painting images. A method combining multicolor gamut texture analysis and block color feature extraction is introduced. The multiresolution grayscale co-occurrence matrix technology is applied to enhance the expression ability of feature vectors. Form the results, the average accuracy and recall were improved by 12.2 and 14% respectively compared to traditional grayscale co-occurrence matrix methods. In terms of noise resistance, the algorithm proposed in the study showed a 7 and 7.5% decrease in average accuracy and recall under 30dB noise conditions, which was significantly better than traditional methods, proving the significant advantage of the algorithm in terms of robustness. In summary, the feature extraction method proposed in the research has effectively improved the accuracy and robustness of Chinese painting image classification. This provides a new technological path for image analysis in the field of digital art, laying the foundation for the development of art image processing technology.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 3","pages":"355 - 367"},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007881","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}

引用次数: 0

Slow Sawtooth Voltage in a Stroboscopic Converter of a Georadar Receiver. Effect of Its Nonlinearity on Signal Distortions 雷达接收机频闪变换器中的慢锯齿电压。它的非线性对信号畸变的影响

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700531

V. Aristov

引用次数: 0

Ensemble System for Skin Cancer Detection Based on the Analysis of Heterogeneous Dermatological Data Using Multimodal Neural Networks 基于多模态神经网络异构皮肤病学数据分析的皮肤癌检测集成系统

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S014641162570049X

P. A. Lyakhov, U. A. Lyakhova, D. I. Kalita

{"title":"Ensemble System for Skin Cancer Detection Based on the Analysis of Heterogeneous Dermatological Data Using Multimodal Neural Networks","authors":"P. A. Lyakhov, U. A. Lyakhova, D. I. Kalita","doi":"10.3103/S014641162570049X","DOIUrl":"10.3103/S014641162570049X","url":null,"abstract":"Currently, skin cancer is the most common cancer pathology in the human body and one of the leading causes of death in the world. Therefore, it is relevant to develop high-precision intelligent systems for auxiliary diagnostics of skin cancer in the early stages. Ensemble models are one of the promising methods for increasing the accuracy of intelligent classification systems by reducing the dispersion and variability of forecasts of individual components of the overall system. Multimodal architectures can significantly increase the accuracy of neural network classification through methods for parallel analysis of heterogeneous dermatological data. The work proposes ensemble intelligent systems for analyzing heterogeneous dermatological data based on multimodal neural networks with various convolutional architectures. The accuracy of the weighted average ensemble based on multimodal systems using convolutional architectures AlexNet, Inception_v4, Densenet_161 and ResNeXt_50 for multi-class classification was 86.88%, and for binary estimation the accuracy was 94.10%. The accuracy of the weighted ensemble based on similar multimodal systems with weights corresponding to the accuracy of each base classifier was 86.82% for the original multiclass classification and 93.82% for the binary evaluation. The developed ensemble systems can be implemented as a high-precision auxiliary diagnostic tool to help make a medical decision.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 3","pages":"328 - 339"},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007882","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}

引用次数: 0

Design of Experimental Simulation Test Bed for Regenerative Braking System 再生制动系统实验仿真试验台设计

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700464

Ying Fang

{"title":"Design of Experimental Simulation Test Bed for Regenerative Braking System","authors":"Ying Fang","doi":"10.3103/S0146411625700464","DOIUrl":"10.3103/S0146411625700464","url":null,"abstract":"To gain a deeper understanding of the regenerative braking system and to demonstrate its operation in a practical setting, a regenerative braking simulation system was developed. The mechanical components of the system were designed first, including the calculation of the flywheel group, motor selection, hydraulic pump selection, and accumulator selection. Following this, a control system based on a data acquisition card was designed, encompassing both hardware and software elements. This system is capable of collecting data such as flywheel speed and accumulator pressure, as well as controlling the electromagnetic clutches. Subsequently, experiments were conducted using the developed system. The experimental results indicate that the maximum accumulator pressure reached 15.5 MPa when the initial flywheel speed was 796 rpm, while it only reached 8.8 MPa at an initial speed of 296 rpm. This demonstrates that more energy can be recovered at higher initial speeds. Moreover, the results confirm that the designed control system effectively manages the energy recovery process and facilitates the demonstration of the regenerative braking process. The proposed system offers a valuable platform for studying vehicle regenerative braking systems.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 3","pages":"287 - 296"},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007943","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}

引用次数: 0