Advanced Control for Applications最新文献

{"title":"Exploration on 3D imaging model construction of clothing fitting based on virtual reality technology","authors":"Jingyuan Ren,&nbsp;Xiaoyan Hu","doi":"10.1002/adc2.173","DOIUrl":"10.1002/adc2.173","url":null,"abstract":"<p>With the continuous development of virtual reality technology (VRt), the clothing industry has begun to use VRt to build three-dimensional clothing fitting models, which provide consumers with a comprehensive range of fitting effects. However, the traditional 3D (three-dimensional) imaging model construction method for clothing has certain shortcomings. For example, the construction of fitting models is a process from planar to three-dimensional, while the three-dimensional fitting model is a process from two-dimensional to three-dimensional, which makes it impossible for users to obtain a more intuitive, visual, and comprehensive fitting effect during fitting. On the basis of summarizing the existing methods for building 3D imaging models of clothing, this paper proposed a method for building 3D clothing fitting models based on VRt and applied it to actual clothing fitting. This provided consumers with a comprehensive method for fitting clothing, thereby improving the shopping efficiency and quality of consumers when purchasing clothing. The research results show that the proportion of positive evaluations using VRt systems was 92%, while the proportion of positive evaluations using conventional technology systems was only 10%, indicating a positive relationship between VRt and the construction of 3D imaging models for clothing fitting. The VRt-based clothing fitting system has strong practical significance, but it is still in the preliminary exploration stage. The system proposed in this paper does not include face modeling function. Therefore, in the future, we can consider adding a face reconstruction module to build a more personalized human model through photo reconstruction and other ways.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139249048","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":"NSGA-II algorithm-based automated cigarette finished goods storage level optimization research","authors":"Yewei Hu,&nbsp;Guangjun Dong,&nbsp;Bin Wang,&nbsp;Xiyao Liu,&nbsp;Jun Wen,&nbsp;Ming Dai,&nbsp;Zongrui Wu","doi":"10.1002/adc2.171","DOIUrl":"10.1002/adc2.171","url":null,"abstract":"<p>With the growth of Internet of Things technology, more and more businesses are implementing automated cargo storage systems. By using an appropriate automated storage space allocation model, these businesses can significantly reduce their storage pressure while saving money on logistics and increasing the effectiveness of their product distribution. Therefore, the study is based on the non-dominated sorting genetic algorithms II (non-dominated sorting genetic algorithm, NSGA II), which combines the three basic principles of space allocation as the objective function applied to the allocation model of the algorithm, in order to optimize the space model for automated storage of finished cigarettes. The algorithm is run to obtain 20 Pareto solutions and examine their three objective functions. The experiment's findings revealed, after optimizing the NSGA-II algorithm in this study, the average reduction rate of shipping efficiency is 32%, the average reduction rate of shelf stability is 54%, and the average reduction rate of product correlation is about 77%, indicating that the algorithm optimization is highly effective.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135291697","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":"Application of improved support vector machine model in fault diagnosis and prediction of power transformers","authors":"Yanming Wang","doi":"10.1002/adc2.170","DOIUrl":"10.1002/adc2.170","url":null,"abstract":"<p>Power transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K-means algorithm into the model and constructs a high-precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135291334","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":"Design of auto obstacle avoidance system based on machine learning under the background of intelligent transportation","authors":"Ying Wang","doi":"10.1002/adc2.164","DOIUrl":"10.1002/adc2.164","url":null,"abstract":"<p>With the process of urbanization and the increase in car ownership, traffic problems are becoming increasingly prominent. In order to improve traffic mobility and improve traffic safety, a machine learning based autonomous obstacle avoidance system was studied and designed in the context of intelligent transportation. Design an obstacle avoidance hardware system consisting of a tracking sensor module, an intelligent patrol module, an obstacle avoidance sensor module, and a motor module. Through the coordination and cooperation of multiple modules, the adaptive ability of the obstacle avoidance system is improved. On the basis of hardware design, a road coordinate system is established, and the lane-changing path is planned with the longitudinal, lateral distance and speed of the ego vehicle and the preceding vehicle as input, and the vehicle steering and lane-changing control is completed using the front wheel angle of the ego vehicle as the control quantity. The model predictive control method is used for obstacle avoidance trajectory planning. Based on the obstacle avoidance path planning results, the reinforcement learning method is used to design the vehicle's autonomous obstacle avoidance early warning to improve the efficiency of obstacle avoidance. The experimental results show that the designed system can maintain the lateral stability of the vehicle under continuous steering conditions, and the fit between the path tracking and the reference path is better, that is, the vehicle obstacle avoidance control effect is better; the convergence speed is faster. The vehicle autonomous obstacle avoidance warning time is short, which can ensure the safety of the vehicle to the greatest extent. This research achievement will provide important support for the development and practical application of intelligent transportation systems, and promote innovation and progress in the transportation field.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136079053","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":"An almost globally stable adaptive phase-locked loop for synchronization of a voltage source converter to a weak grid","authors":"Daniele Zonetti,&nbsp;Alexey Bobtsov,&nbsp;Romeo Ortega,&nbsp;Nikolay Nikolaev,&nbsp;Oriol Gomis-Bellmunt","doi":"10.1002/adc2.166","DOIUrl":"10.1002/adc2.166","url":null,"abstract":"<p>In this article, we are interested in the problem of <i>adaptive synchronization</i> of a voltage source converter with a possibly weak grid with unknown angle and frequency. To guarantee a suitable synchronization with the angle of the three-phase grid voltage we design an adaptive observer for such a signal requiring measurements only at the point of common coupling. Then we propose an alternative certainty-equivalent, adaptive phase-locked loop that ensures the angle estimation error goes to zero for almost all initial conditions. Although well-known, for the sake of completeness, we also present a PI controller with feedforward action that ensures the converter currents converge to an arbitrary desired value. Relevance of the theoretical results and their robustness to variation of the grid parameters are thoroughly discussed and validated in the challenging scenario of a converter connected to a grid with low short-circuit-ratio.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135898621","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":"Multi-narrow type obstacle avoidance algorithm for UAV swarm based on game theory","authors":"Ye Lin,&nbsp;Zhenyu Na,&nbsp;Jialiang Liu,&nbsp;Yun Lin","doi":"10.1002/adc2.168","DOIUrl":"10.1002/adc2.168","url":null,"abstract":"<p>A Flocking obstacle avoidance algorithm based on the extensive game with perfect information is proposed for the blockage problem of UAV swarm in front of multi-narrow type obstacles. The two UAVs closest to the target are selected as participants of the game, and the game tree is defined to determine the combination of the motion strategies of the two UAVs to obtain the payoff matrix. Determine the subgame perfect Nash equilibrium to get the optimal strategy, and give the UAVs different motion states respectively so as to ensure that the UAVs can successfully pass multi-narrow type obstacles. Simulation results demonstrate that the proposed algorithm has a higher over-hole rate in the case of the multi-narrow type obstacle compared to the static game-based Flocking obstacle avoidance algorithm.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135537640","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":"Optimal path planning using psychological profiling in drone-assisted missing person search","authors":"Jan-Hendrik Ewers,&nbsp;David Anderson,&nbsp;Douglas Thomson","doi":"10.1002/adc2.167","DOIUrl":"10.1002/adc2.167","url":null,"abstract":"<p>Search and rescue operations are all time-sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit has begun the deployment of drones to assist in missing person searches with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of á priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a nonlinear optimization to determine the optimal flight path for a given search area and subject profile. Two optimization solvers were compared; genetic algorithms, and particle swarm optimization. Finally, the most effective algorithm was used to create a coverage path for a real-life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136062047","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":"Design of nonlinear control system for motion trajectory of industrial handling robot","authors":"Haoming Zhao,&nbsp;Xinling Zhang","doi":"10.1002/adc2.165","DOIUrl":"10.1002/adc2.165","url":null,"abstract":"<p>Industrial robot is a and multi-output complex system with strong coupling and high nonlinearity. The motion control accuracy of the system is affected by many factors. To solve the difficulty in establishing the input and output characteristics of robot dynamics modeling, the robot motion model is established through the Lagrangian energy function. At the same time, the nonlinear relationship between angular velocity, angular acceleration, and robot torque is accurately expressed through improved cascaded neural network. In addition, the optimal time planning of the robot's trajectory in joint space is studied using multinomial interpolation method and the particle swarm optimization (PSO). In the simulation experiment, the effect of the proposed dynamic model fitting was outstanding. Under the mixed multinomial difference calculation planning, the angular position trajectories of the three joints changed very smoothly. In the data set application test, the average error of the PSO algorithm was 0.4061 mm and the average task time was 9.101 s, which were lower than other planning algorithms. Experiments showed that the Lagrangian dynamic model analysis based on genetic algorithm cascaded neural network and PSO trajectory scheduling method under mixed multinomial difference had better trajectory planning performance in handling tasks.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136373969","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":"Flow field analysis of combustion fallout propensity test system based on CFD","authors":"Yaoshuo Sang,&nbsp;Hao Dong,&nbsp;Shizhu Ye,&nbsp;Chaohao Guo,&nbsp;Long Zhang,&nbsp;Zhigang Li,&nbsp;Yong Liu","doi":"10.1002/adc2.163","DOIUrl":"10.1002/adc2.163","url":null,"abstract":"<p>The flow field of the environment plays a crucial role in cigarette combustion cone fallout propensity test, with air velocity exhibiting a positive correlation with combustion volume. In order to minimize the impact of the environmental flow field on the test results, it is necessary to control the air speed within the range of 200 ± 30 mm/s in the test area of each tobacco test channel. To address this concern, which used the Realizable <i>k-ε</i> model to develop a mathematical model of the testing environment. The uniformity of air speed in each channel and its relationship with structural parameters were then analyzed. Based on these findings, the key structural parameters of the ventilation hood are optimized. After restimulated the optimized model, the results demonstrate a higher level of uniformity in the environmental flow field of the optimized section. To validate the accuracy of the simulation results, measurements indicated that the maximum air speed value at all points is 225.6 mm/s, while the minimum value is 178.44 mm/s. These values fall within the specified range of 200 ± 30 mm/s, thus meeting the design requirements. This study ensures that the cigarette can burn in a steady state during the cigarette combustion fallout propensity test and improves the stability of the cigarette combustion cone drop tendency test results.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73442344","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":"Short-term wind power prediction based on the combination of firefly optimization and LSTM","authors":"Rui Zhang,&nbsp;Xiu Zheng","doi":"10.1002/adc2.161","DOIUrl":"10.1002/adc2.161","url":null,"abstract":"<p>With the development of social resources, people's consumption of energy is huge, so renewable energy, such as wind energy, has been widely concerned and developed. Although there has been sufficient development of wind power generation, its output has some problems such as uncertainty, which leads to insufficient utilization of wind energy resources and uneven power output quality level, which brings great challenges to the grid connection. To solve this problem, a short-term wind power prediction model combining firefly algorithm and long term memory network is proposed. The main motivation of the research is to improve the accuracy of wind power prediction and thus improve the utilization of wind energy resources. Compared with the existing methods, the innovation of FA-LSTM model lies in the integration of the two algorithms, making full use of the advantages of FA in global search optimization and LSTM in time series data processing, and improving the accuracy and stability of prediction. During the experiment, we used different wind farm data to train and test the model. The results show that the FA-LSTM model can improve the optimal fitness by more than 50% compared with other algorithms, and the iterative prediction error is smaller. Standard root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the model. The accuracy of RMSE and MAE reached over 97% and 98% respectively. When the test data is highly volatile, the data accuracy of FA-LSTM model reaches 92% and 94%, and the FA-LSTM model drops to the stable value faster. FA-LSTM model has the best fitting degree with the true value curve, and the fitting degree reaches more than 90%. Comparing the actual power and predicted power of different units, the actual power of Unit 1 is 34.875, and the predicted power obtained by FA-LSTM model is 34.935, with an error of only 0.06. The key finding of this study is that the prediction model combining FA and LSTM has high accuracy and stability in wind power prediction, and can effectively deal with the uncertainty and volatility of wind energy resource utilization. FA-LSTM model provides an effective solution for wind power prediction, which is helpful to improve the utilization rate of wind energy resources.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88437103","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}