Aiming at the problems of low search efficiency of A* algorithm in traditional path planning, many redundant points, and inability to avoid unknown obstacles in real-time in complex environments, this paper proposes a path planning algorithm based on A* combined with Dynamic Window Approach (DWA) algorithm. First, the evaluation function of the traditional A* algorithm and the expansion strategy of sub-nodes are improved to improve the safety and search efficiency of the global path. Then the redundant nodes in the global path are processed to reduce the number of turning points and improve the smoothness of the global path. to improve the instability and energy consumption of the robot during travel; finally, based on the global path planning, the DWA algorithm is introduced to perform path planning in the local unknown environment. The local path planning is completed by retaining the key path turning points as intermediate path guidance. Real-time obstacle avoidance. Through simulation experiments, the effectiveness and feasibility of the fusion algorithm are verified.
针对传统路径规划中 A* 算法搜索效率低、冗余点多、无法在复杂环境中实时避开未知障碍物等问题，本文提出了一种基于 A* 算法并结合动态窗口法（DWA）的路径规划算法。首先，改进了传统 A* 算法的评估函数和子节点的扩展策略，提高了全局路径的安全性和搜索效率。然后，对全局路径中的冗余节点进行处理，减少转弯点的数量，提高全局路径的平滑度，改善机器人在行进过程中的不稳定性和能耗；最后，在全局路径规划的基础上，引入 DWA 算法，进行局部未知环境下的路径规划。通过保留关键路径转折点作为中间路径引导，完成局部路径规划。实时避障。通过仿真实验，验证了融合算法的有效性和可行性。
{"title":"Robot Path Planning Algorithm based on Improved A* and DWA","authors":"Haisheng Song, Deyang Zhang","doi":"10.54097/fcis.v6i1.07","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.07","url":null,"abstract":"Aiming at the problems of low search efficiency of A* algorithm in traditional path planning, many redundant points, and inability to avoid unknown obstacles in real-time in complex environments, this paper proposes a path planning algorithm based on A* combined with Dynamic Window Approach (DWA) algorithm. First, the evaluation function of the traditional A* algorithm and the expansion strategy of sub-nodes are improved to improve the safety and search efficiency of the global path. Then the redundant nodes in the global path are processed to reduce the number of turning points and improve the smoothness of the global path. to improve the instability and energy consumption of the robot during travel; finally, based on the global path planning, the DWA algorithm is introduced to perform path planning in the local unknown environment. The local path planning is completed by retaining the key path turning points as intermediate path guidance. Real-time obstacle avoidance. Through simulation experiments, the effectiveness and feasibility of the fusion algorithm are verified.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229608","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}
With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource allocation. These data will be used to train BP neural network (BPNN). In order to predict the bidding price, a BPNN is designed, which usually includes input layer, hidden layer and output layer. The number of nodes in the input layer is equal to the dimension of the input feature, and the number of nodes in the output layer is 1, which indicates the prediction of the bidding price. Through experiments and simulations, we verify the effectiveness of the dynamic resource allocation mechanism driven by neural network. The results show that this mechanism can better adapt to the changing workload requirements, improve resource utilization and reduce resource waste. In addition, it provides better performance and user experience, thus enhancing the competitiveness of cloud computing systems.
随着云计算技术的普及，云计算资源的动态分配机制已成为提高资源利用率、满足多样化工作负载需求的重要研究领域。本研究旨在探索神经网络驱动的云计算资源动态分配机制，将深度学习的强大能力引入云计算环境。我们提出了一个集数据收集、分析、决策和执行于一体的综合框架，以实现资源的智能分配。这些数据将用于训练 BP 神经网络（BPNN）。为了预测投标价格，设计了一个 BPNN，通常包括输入层、隐藏层和输出层。输入层的节点数等于输入特征的维数，输出层的节点数为 1，表示预测投标价格。通过实验和仿真，我们验证了神经网络驱动的动态资源分配机制的有效性。结果表明，该机制能更好地适应不断变化的工作负载需求，提高资源利用率，减少资源浪费。此外，它还能提供更好的性能和用户体验，从而增强云计算系统的竞争力。
{"title":"Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network","authors":"Yining Ou","doi":"10.54097/fcis.v6i1.03","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.03","url":null,"abstract":"With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource allocation. These data will be used to train BP neural network (BPNN). In order to predict the bidding price, a BPNN is designed, which usually includes input layer, hidden layer and output layer. The number of nodes in the input layer is equal to the dimension of the input feature, and the number of nodes in the output layer is 1, which indicates the prediction of the bidding price. Through experiments and simulations, we verify the effectiveness of the dynamic resource allocation mechanism driven by neural network. The results show that this mechanism can better adapt to the changing workload requirements, improve resource utilization and reduce resource waste. In addition, it provides better performance and user experience, thus enhancing the competitiveness of cloud computing systems.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139234910","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}
In the development of unmanned two-wheeled self-balancing vehicle, it is very important to consider the trajectory tracking of unmanned two-wheeled self-balancing vehicle. In order to study the track tracking problem of unmanned two-wheeled self-balancing vehicle, the kinematic model of unmanned two-wheeled vehicle and the relation between its roll Angle and the kinematic model were established. The simulation platform is built to realize the unmanned two-wheeled vehicle and realize the circular track tracking at 5m/s, and the tracking performance of the track tracking controller under the circular track is verified.
{"title":"Trajectory Tracking Control Algorithm of Two-wheel Rutting Model","authors":"Yuanhong Dan, Haiyang Zhong","doi":"10.54097/fcis.v6i1.09","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.09","url":null,"abstract":"In the development of unmanned two-wheeled self-balancing vehicle, it is very important to consider the trajectory tracking of unmanned two-wheeled self-balancing vehicle. In order to study the track tracking problem of unmanned two-wheeled self-balancing vehicle, the kinematic model of unmanned two-wheeled vehicle and the relation between its roll Angle and the kinematic model were established. The simulation platform is built to realize the unmanned two-wheeled vehicle and realize the circular track tracking at 5m/s, and the tracking performance of the track tracking controller under the circular track is verified.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139234479","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}
Cold joints pose great safety risks to the safe operation of railways. In view of the existing cold joint detection methods, which have low detection efficiency and difficulty in data analysis, a tunnel secondary lining cold joint detection classification method based on the improved hybrid leader CNN-BiLSTM-SVM model is proposed. First, the Rayleigh wave method is used to extract the waveform information of the cold joints. Secondly, CNN-BILSTM is used to perform feature extraction and fusion processing on the waveform information and then input into the support vector machine, and the improved hybrid leader algorithm is used to optimize the parameters in the SVM. Finally, the information is input into the optimized CNN-BiLSTM-SVM to obtain the cold joints detection classification results. In order to verify the effectiveness of this method, the waveform data collected using the Rayleigh wave method in the tunnel under construction and the verified coring detection results are used as the data set. The results show that the results of this method are higher than the unoptimized CNN-BILSTM-SVM and the CNN-BILSTM-SVM optimized by the seagull optimization algorithm and the sparrow search optimization algorithm.
{"title":"Classification Method for Railway Tunnel Secondary Lining Cold Joint Detection based on CNN-BiLSTM-SVM Model with Improved Hybrid Leader Algorithm","authors":"Honggu Zhu, Jiaye Wu","doi":"10.54097/fcis.v6i1.05","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.05","url":null,"abstract":"Cold joints pose great safety risks to the safe operation of railways. In view of the existing cold joint detection methods, which have low detection efficiency and difficulty in data analysis, a tunnel secondary lining cold joint detection classification method based on the improved hybrid leader CNN-BiLSTM-SVM model is proposed. First, the Rayleigh wave method is used to extract the waveform information of the cold joints. Secondly, CNN-BILSTM is used to perform feature extraction and fusion processing on the waveform information and then input into the support vector machine, and the improved hybrid leader algorithm is used to optimize the parameters in the SVM. Finally, the information is input into the optimized CNN-BiLSTM-SVM to obtain the cold joints detection classification results. In order to verify the effectiveness of this method, the waveform data collected using the Rayleigh wave method in the tunnel under construction and the verified coring detection results are used as the data set. The results show that the results of this method are higher than the unoptimized CNN-BILSTM-SVM and the CNN-BILSTM-SVM optimized by the seagull optimization algorithm and the sparrow search optimization algorithm.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139233471","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}
The mechanical performance of steel materials is crucial for the design, selection, and application of materials. In order to better predict the mechanical performance through chemical composition and process parameters, this paper establishes a predictive model for the mechanical properties of steel materials based on the random forest algorithm. The model predicts yield strength, tensile strength, and elongation based on chemical composition and process parameters. The results indicate that the random forest algorithm model demonstrates excellent performance in predicting the mechanical properties of steel materials.
{"title":"The Mechanical Performance Prediction of Steel Materials based on Random Forest","authors":"Shihao Wang, Xiangxiang Wu","doi":"10.54097/fcis.v6i1.01","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.01","url":null,"abstract":"The mechanical performance of steel materials is crucial for the design, selection, and application of materials. In order to better predict the mechanical performance through chemical composition and process parameters, this paper establishes a predictive model for the mechanical properties of steel materials based on the random forest algorithm. The model predicts yield strength, tensile strength, and elongation based on chemical composition and process parameters. The results indicate that the random forest algorithm model demonstrates excellent performance in predicting the mechanical properties of steel materials.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139234580","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}
In order to effectively carry out aviation service quality evaluation, based on the service quality characteristics of Air China, through research on service quality evaluation models and methods, a questionnaire based on SERVPREF and a fuzzy comprehensive evaluation method were designed to evaluate the service quality of Air China. evaluate. Through literature analysis and expert consultation, the service quality evaluation indicators of Air China were screened and a service quality evaluation indicator system was established. Through the questionnaire survey method, a questionnaire is designed from the two dimensions of passenger satisfaction and indicator importance, and the questionnaire is distributed to collect data. Based on the questionnaire, the analytic hierarchy process was used to determine the weight of the service quality evaluation indicators of Air China. Through statistical analysis of the results of the questionnaire, the results of the comprehensive evaluation of Air China were sorted and analyzed, and it was concluded that Air China The advantages and disadvantages of the company's service quality are analyzed using the fuzzy evaluation method to comprehensively evaluate the service quality of Air China. Finally, it provides the basis for strategic research on the improvement of service quality of Air China.
{"title":"Service Quality Evaluation of Air China based on SERVPERF Model","authors":"Ying Wang, Nutteera Phakdeephirot","doi":"10.54097/fcis.v6i1.10","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.10","url":null,"abstract":"In order to effectively carry out aviation service quality evaluation, based on the service quality characteristics of Air China, through research on service quality evaluation models and methods, a questionnaire based on SERVPREF and a fuzzy comprehensive evaluation method were designed to evaluate the service quality of Air China. evaluate. Through literature analysis and expert consultation, the service quality evaluation indicators of Air China were screened and a service quality evaluation indicator system was established. Through the questionnaire survey method, a questionnaire is designed from the two dimensions of passenger satisfaction and indicator importance, and the questionnaire is distributed to collect data. Based on the questionnaire, the analytic hierarchy process was used to determine the weight of the service quality evaluation indicators of Air China. Through statistical analysis of the results of the questionnaire, the results of the comprehensive evaluation of Air China were sorted and analyzed, and it was concluded that Air China The advantages and disadvantages of the company's service quality are analyzed using the fuzzy evaluation method to comprehensively evaluate the service quality of Air China. Finally, it provides the basis for strategic research on the improvement of service quality of Air China.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139235180","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}
Jerry Yao, Yuan Zou, Shuqian Du, Hong Wu, Bin Yuan
The mammary gland is an important human organ that secretes milk and feeds offspring, while breast tumors are benign or malignant tumors that occur in the breast tissue. There are many causes of breast cancer, and the incidence continues to rise, which is an important killer that threatens women's health. In recent years, a large number of researchers have been interested in the study of AI diagnosis of breast cancer. Artificial intelligence uses a specific algorithm to intelligently process ultrasound images, and develops a high-precision and high-efficiency breast cancer recognition model through training and optimization of the algorithm. At present, the application of computer-aided detection methods in breast cancer ultrasound has been gradually promoted, and the combined application of artificial intelligence has played an advantageous role in the field of breast disease ultrasound diagnosis, such as shortening the examination time, effectively improving the detection rate and diagnostic accuracy rate. The main reasons are: the accuracy of traditional AI diagnosis model of breast cancer based on machine learning is not high; The aim of this paper is to improve the accuracy of early diagnosis of breast diseases effectively and reduce the misdiagnosis rate caused by overwork of doctors on the basis of clear medical images and computer-aided diagnosis technology.
{"title":"Progress in the Application of Artificial Intelligence in Ultrasound Diagnosis of Breast Cancer","authors":"Jerry Yao, Yuan Zou, Shuqian Du, Hong Wu, Bin Yuan","doi":"10.54097/fcis.v6i1.11","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.11","url":null,"abstract":"The mammary gland is an important human organ that secretes milk and feeds offspring, while breast tumors are benign or malignant tumors that occur in the breast tissue. There are many causes of breast cancer, and the incidence continues to rise, which is an important killer that threatens women's health. In recent years, a large number of researchers have been interested in the study of AI diagnosis of breast cancer. Artificial intelligence uses a specific algorithm to intelligently process ultrasound images, and develops a high-precision and high-efficiency breast cancer recognition model through training and optimization of the algorithm. At present, the application of computer-aided detection methods in breast cancer ultrasound has been gradually promoted, and the combined application of artificial intelligence has played an advantageous role in the field of breast disease ultrasound diagnosis, such as shortening the examination time, effectively improving the detection rate and diagnostic accuracy rate. The main reasons are: the accuracy of traditional AI diagnosis model of breast cancer based on machine learning is not high; The aim of this paper is to improve the accuracy of early diagnosis of breast diseases effectively and reduce the misdiagnosis rate caused by overwork of doctors on the basis of clear medical images and computer-aided diagnosis technology.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139235434","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}
Jingyu Xu, Linying Pan, Qiang Zeng, Wenjian Sun, Weixiang Wan
Deep learning frameworks are mainly divided into pytorch in academia and tensorflow in industry, where pytorch is a dynamic graph and tensor flow is a static graph, both of which are essentially directed and loopless computational graphs. In TensorFlow, data input into the model requires a good computational graph structure to be executed, and static graphs have more optimization methods and higher performance. The node of the graph is OP and the edge is tensor. The static diagram is fixed after the compilation is completed, so it is easier to deploy on the server. How to compile a static graph. It is found that in the compilation process of static graphs, the configuration of the compiler (config) affects the way the compiler compiles and optimizes the model, and ultimately affects the running time of the model. We propose a reliable model, which can predict the best compilation configuration of the model according to the compilation configuration and runtime of the machine learning model in the training dataset to minimize the running time.
{"title":"Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks","authors":"Jingyu Xu, Linying Pan, Qiang Zeng, Wenjian Sun, Weixiang Wan","doi":"10.54097/fcis.v6i1.13","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.13","url":null,"abstract":"Deep learning frameworks are mainly divided into pytorch in academia and tensorflow in industry, where pytorch is a dynamic graph and tensor flow is a static graph, both of which are essentially directed and loopless computational graphs. In TensorFlow, data input into the model requires a good computational graph structure to be executed, and static graphs have more optimization methods and higher performance. The node of the graph is OP and the edge is tensor. The static diagram is fixed after the compilation is completed, so it is easier to deploy on the server. How to compile a static graph. It is found that in the compilation process of static graphs, the configuration of the compiler (config) affects the way the compiler compiles and optimizes the model, and ultimately affects the running time of the model. We propose a reliable model, which can predict the best compilation configuration of the model according to the compilation configuration and runtime of the machine learning model in the training dataset to minimize the running time.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236390","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}
The development of social network has brought a large amount of image information, and the research on image emotion has gradually attracted wide attention. The current image emotion analysis methods based on multi-level features simply splice the features at each level and then classify the emotions, which not only ignores the correlation between features at different levels, but also ignores the synergistic effect between global features and local features. Therefore, this paper proposes an emotion model (MAML) based on mixed attention and multi-level dependence of images, which uses spatial and channel attention mechanisms to extract local emotion region features of images. Bi-directional Long Short Term Memory network (BiLSTM) is used to establish correlation between multi-level image global features. The experimental results of MAML model on artphoto and abstract data sets prove the validity of MAML model.
{"title":"Image Emotion Analysis Combining Attention Mechanism and Multi-level Correlation","authors":"Shuxia Ren, Simin Li","doi":"10.54097/fcis.v6i1.12","DOIUrl":"https://doi.org/10.54097/fcis.v6i1.12","url":null,"abstract":"The development of social network has brought a large amount of image information, and the research on image emotion has gradually attracted wide attention. The current image emotion analysis methods based on multi-level features simply splice the features at each level and then classify the emotions, which not only ignores the correlation between features at different levels, but also ignores the synergistic effect between global features and local features. Therefore, this paper proposes an emotion model (MAML) based on mixed attention and multi-level dependence of images, which uses spatial and channel attention mechanisms to extract local emotion region features of images. Bi-directional Long Short Term Memory network (BiLSTM) is used to establish correlation between multi-level image global features. The experimental results of MAML model on artphoto and abstract data sets prove the validity of MAML model.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256147","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}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13801
Xinqiang Yu
The purpose of this paper is to discuss the application of AI (Artificial Intelligence) algorithm with NN (Neural Network) in complex system optimization. Therefore, this paper takes the optimization of power system as the research object and designs a hybrid NN model. The model can effectively extract features from historical power data and predict future power demand and supply. At the same time, the NN model is optimized by genetic algorithm. The algorithm can efficiently search the optimal solution in a large solution space and has the ability to deal with multi-objective optimization problems. Finally, it is verified by experiments. By using test data sets to evaluate the model, it is found that the algorithm in this paper has high accuracy and applicability in dealing with power system optimization problems. At the same time, the model can effectively reduce the cost of power generation and improve the stability of the system. These achievements provide new ideas and methods for future complex system optimization, and provide useful reference for promoting the development and application of AI technology. In order to provide some guidance for practical application.
本文旨在讨论 AI（人工智能）算法与 NN（神经网络）在复杂系统优化中的应用。因此，本文以电力系统的优化为研究对象，设计了一个混合 NN 模型。该模型能有效地从历史电力数据中提取特征，并预测未来的电力需求和供应。同时，利用遗传算法对 NN 模型进行优化。该算法能在较大的解空间中高效搜索最优解，并具有处理多目标优化问题的能力。最后，通过实验进行验证。通过使用测试数据集对该模型进行评估，发现本文中的算法在处理电力系统优化问题时具有较高的准确性和适用性。同时，该模型能有效降低发电成本，提高系统稳定性。这些成果为未来复杂系统优化提供了新的思路和方法，为推动人工智能技术的发展和应用提供了有益的借鉴。以期为实际应用提供一定的指导。
{"title":"Optimization of Artificial Intelligence Algorithm based on Neural Network in Complex System","authors":"Xinqiang Yu","doi":"10.54097/fcis.v5i3.13801","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13801","url":null,"abstract":"The purpose of this paper is to discuss the application of AI (Artificial Intelligence) algorithm with NN (Neural Network) in complex system optimization. Therefore, this paper takes the optimization of power system as the research object and designs a hybrid NN model. The model can effectively extract features from historical power data and predict future power demand and supply. At the same time, the NN model is optimized by genetic algorithm. The algorithm can efficiently search the optimal solution in a large solution space and has the ability to deal with multi-objective optimization problems. Finally, it is verified by experiments. By using test data sets to evaluate the model, it is found that the algorithm in this paper has high accuracy and applicability in dealing with power system optimization problems. At the same time, the model can effectively reduce the cost of power generation and improve the stability of the system. These achievements provide new ideas and methods for future complex system optimization, and provide useful reference for promoting the development and application of AI technology. In order to provide some guidance for practical application.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"53 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276983","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}
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