Advanced Control for Applications最新文献

{"title":"Design of intelligent behavior analysis software based on speaker identity classification algorithm in microgrid mode","authors":"Weijie Guo","doi":"10.1002/adc2.209","DOIUrl":"10.1002/adc2.209","url":null,"abstract":"<p>Digital technology still has a low level of intelligence in the microgrid mode of teaching behavior analysis, resulting in the traditional manual observation and recording stage still being used for speaker identity classification, and the efficiency of teaching behavior analysis is also low. In response to the above issues, the research is based on the teacher-student analysis method and proposes a dual clustering algorithm based on the general background model Gaussian mixture model for speaker identity classification, thereby realizing the development and design of intelligent behavior analysis software. The research results indicate that the average recall rate of behavior transition points in the classroom teaching discourse corpus of the intelligent behavior analysis software is 89.03%, which is better than traditional analysis methods. Therefore, the intelligent behavior analysis software constructed by the dual clustering algorithm has high effectiveness and practicality. The research proposes a method model and implements intelligent visualization for classroom teaching behavior analysis, improving the efficiency of analyzing current microgrid teaching behavior.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140687904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D localization of wireless sensor IoT nodes based on weighted DV-Hop algorithm","authors":"Kui Zhang,&nbsp;Haihua Cui,&nbsp;Xiaomei Yan","doi":"10.1002/adc2.212","DOIUrl":"10.1002/adc2.212","url":null,"abstract":"<p>With the widespread popularity of smart wearable devices and the rise of emerging Internet of Things applications, such as smart cities, smart homes, and smart cars, the demand for Internet of Things devices is growing. The technology for positioning Internet of Things nodes using traditional wireless sensors only provides approximate location information, which is insufficient for high-precision applications. To achieve accurate sensor node location in a specific area, this study proposes an advanced weighted distance vector jump location algorithm. This paper proposes using optical wireless networks, a new wireless communication technology, to enhance the distance vector jump algorithm. It is considered the core technology in researching the three-dimensional positioning of wireless sensor IoT nodes. The experimental data validated that by comparing with existing positioning algorithms, the improved algorithm significantly improved the location accuracy, and its average orientation error was significantly lower than other algorithms. In three cases where the wireless sensor communication radius was between 10 and 30 m, the average positioning errors of the improved algorithm were 0.363, 0.264, and 0.258, respectively. Compared with the pre improved Distance Vector Hop algorithm, its accuracy has increased by 41.1%, indicating the better positioning performance. Overall, the improved weighted algorithm significantly improves the positioning effect, providing strong technical support for the three-dimensional positioning of wireless sensor Internet of Things nodes.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.212","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140691195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on trajectory control of multi-degree-of-freedom industrial robot based on visual image","authors":"Ruiling Hu","doi":"10.1002/adc2.210","DOIUrl":"10.1002/adc2.210","url":null,"abstract":"<p>In order to improve the trajectory control effect of multi-degree-of-freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi-degree-of-freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi-degree-of-freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140692960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based data fusion method for wireless sensor networks","authors":"Chunda Liang,&nbsp;Qi Yao","doi":"10.1002/adc2.208","DOIUrl":"10.1002/adc2.208","url":null,"abstract":"<p>For wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non-replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto-Encoder (DSAE) model is constructed and trained using a layer-wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto-Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back-Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140705511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonlinear control of electro-hydraulic screw conveyor system for shield machine based on disturbance observer and back-stepping method","authors":"Liu Xuanyu,&nbsp;Cheng Xunlei","doi":"10.1002/adc2.206","DOIUrl":"10.1002/adc2.206","url":null,"abstract":"<p>In order to improve the precision of earth pressure balance control and anti-interference ability of shield sealing chamber, this paper proposes a nonlinear control strategy for the screw conveyor based on disturbance observer and back-stepping method, so as to ensure the safe and efficient tunneling of the shield machine. According to the hydraulic flow dynamic balance principle of shield machine, the mechanism model of electro-hydraulic screw conveyor system is established, and the system state space model is derived. The nonlinear controller of the screw conveyor is designed by using the inverse step method and the disturbance observer compensation characteristic, so that the system responds quickly and compensates for the flow disturbance and external force disturbance in real time. At last, the system stability is proven by using the Lyapunov function. The experimental results show that the method has high control accuracy with fast response and strong anti-interference ability.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on adaptive particle swarm optimization particle filter target tracking algorithm in wireless sensor networks","authors":"Chun-Yan Jiang,&nbsp;Jing Wu,&nbsp;Rong Gou,&nbsp;Jing-Fang Fu","doi":"10.1002/adc2.205","DOIUrl":"10.1002/adc2.205","url":null,"abstract":"<p>With regard to target tracking in wireless sensor networks, we are faced with problems like deficient occlusion handling and tracking failures during rapid movements due to complex and diverse circumstances. In order to effectively improve the accuracy of particle filter tracking caused by particle degradation, we propose an adaptive particle swarm optimization (APSO) particle filter algorithm. This algorithm uses particle filters to predict the target location in a particular area and introduces the particle swarm optimization (PSO) algorithm, of which both the evolutionary speed and the convergence accuracy are further improved by investigating the particle distribution through an entropy analysis, employing three different inertial weighting strategies and dynamic double mutation strategy, and exploiting the capabilities of the adaptive balancing algorithm in global and local searching. The simulation results show that the improved algorithm has a reduced root mean square error, shorter time consumption, faster speed, reduced target tracking error, and higher average success rate, so this algorithm exhibits sound real-time performance and accuracy in terms of occlusion handling and tracking loss.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140753712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on image processing of electric power system terminals based on reinforcement learning and mobile edge computing optimization","authors":"Hui Zhou,&nbsp;Jun Yu,&nbsp;Huafeng Luo,&nbsp;Liuwang Wang,&nbsp;Binbin Yang","doi":"10.1002/adc2.198","DOIUrl":"10.1002/adc2.198","url":null,"abstract":"<p>This research is dedicated to the optimization of power system terminal image processing based on RL and MEC. With the continuous development of power system, the demand for image processing of terminal equipment is increasing day by day. However, traditional image processing methods have the problems of high computing complexity and real-time and energy consumption. To solve this problem, this study introduces the idea of RL and MEC to improve the efficiency and performance of image processing of power system terminals. By modeling and optimizing the image processing task of the power system terminal equipment, the intelligent adjustment of the processing parameters is realized to adapt to the needs of different scenarios. MEC technology is introduced to move image processing tasks from the central server to the edge device, reducing data transmission delay and network burden, thus improving real-time performance and reducing energy consumption. The experimental results show that the proposed optimization method based on RL and MEC has a significant performance improvement compared with the traditional method in the power system terminal image processing. The framework our proposed has achieved significant improvement in task completion latency, achieving higher system energy efficiency compared to traditional methods.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.198","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis","authors":"Hongqiang Li","doi":"10.1002/adc2.203","DOIUrl":"10.1002/adc2.203","url":null,"abstract":"<p>Temperature control in the beer manufacturing process is crucial for product quality. Given the gap between China's automation in beer production and the international level, improving the technology in this area has gradually become a core issue in optimizing domestic beer production. This study combines a proportional integral derivative controller with a fuzzy modeling strategy and incorporates a variable-domain structure to propose a variable-domain fuzzy proportional integral derivative controller control method. To cope with the challenges of production interaction, the study also introduces neural network technology. The experimental data indicated that the variable-domain fuzzy proportional integral derivative controller outperforms the conventional proportional integral derivative controller and the fuzzy proportional integral differential controller in terms of overshooting, with a maximum overshoot of only 1.0, compared with 0.50 and 0.70, respectively. The variable-domain fuzzy proportional integral differential controller exhibited a minimal overshoot of only 0.01 when the model parameter is increased by 20%. In comparison, the other methods reach overshoot values of 0.92 and 1.0. The proposed method maintained superior stability even under the influence of impulse disturbance, step disturbance, and modeling variations. These results demonstrated that the research method is significantly more stable than both the proportion integration differentiation (PID) controller and fuzzy PID controller in complex dynamic parameter environments. The proposed method involved 60 rounds of neural network control, which was successfully implemented. The temperature readings T1 and T2 remained stable within the range of 1.0%–1.02% throughout the experiments. The study demonstrates that the proposed methods have higher accuracy and less fluctuation in actual application, making them more available. Taken together, the above results show that the combination of variable-domain fuzzy PID controller and neural network technology in beer production has achieved excellent control results. This study not only provides a strong technical support for the progress of beer production technology in China, but also has important industrial application value and wide promotion prospects.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140379465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new propulsion system GUI based control amenable model development for high-power rockets","authors":"Soham Prajapati,&nbsp;Parth S. Thakar,&nbsp;Anilkumar Markana","doi":"10.1002/adc2.204","DOIUrl":"10.1002/adc2.204","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a new algorithm to model and characterize an autonomous high-power rocket using an indigenously developed graphical user interface (GUI) platform. This platform features a newly devised app, termed as <i>THIEC Rocketry App</i> which embeds the simulation based analysis to determine the design parameters of the rocket, required for a vertical flight. A solid propellant using potassium nitrate and sucrose, also known as rocket-candy, is considered for the GUI development. The GUI facilitates the designer to specify the desired flight parameters for the rocket propulsion system. Various characteristic plots for visualization and analysis are made available in GUI. The obtained parameters from the GUI are then utilized in computer-aided designing (CAD) for further identification of geometrical parameters like inertia tensor, center of gravity (CG) and center of pressure (CP). The mathematical control amenable model of the rocket is then developed using first principles so as to achieve an altitude up to 3 km. The overall system represents a complex nonlinear multi-input multi-output (MIMO) dynamics, having six degrees of freedom. The Newton-Euler formulation is employed to develop the equations of motion. The attitude control using canards is analyzed via simulations for the complete flight path - the boost and coast flights. Finally, the developed GUI based model is validated by practically manufacturing the components of the propulsion system for the small-scale high-power rocket. The proposed model will create the pathway for the development of some robust model-based control schemes for such autonomous rockets in future.</p>\u0000 </div>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140378972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CNN-based defect detection in manufacturing","authors":"Ming Hou,&nbsp;Pengcheng Li,&nbsp;Shiqi Cheng,&nbsp;Jingyao Yv","doi":"10.1002/adc2.196","DOIUrl":"10.1002/adc2.196","url":null,"abstract":"<p>This research introduces an advanced algorithm based on convolutional neural networks for the detection and categorization of surface defects in manufacturing processes. At its core, the algorithm employs a deep learning model that integrates residual networks and attention mechanisms to effectively extract features. Additionally, we have developed a novel feature selection method, named NR, which synergistically combines neighborhood component analysis and ReliefF techniques. This approach enables the selection of more representative deep features for subsequent analysis. For the classification task, we utilize the support vector machine technique, which demonstrates versatility in handling both binary and multi-class classification scenarios. The reliability and superiority of our algorithm are further validated through a comparative analysis using a dataset specifically tailored for this context. The results indicate that our approach outperforms existing algorithms in accurately identifying manufacturing defects.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}