Flexible learning is based on studying learners' psychology and behavior, using the principle of flexible management and flexible decision-making and adopting the mode of flexible contingency thinking to transform the will of teaching organizers into learners' conscious behavior through imperceptible change and cultivation. Flexible learning model can solve the problems, such as short of innovation, weak autonomy and lack of collective learning atmosphere, existing in network education. This paper introduces the concept and mechanism of flexible learning in the SPOC (Small Private Online Course), relies on big data and intelligent technology, optimizes the design of SPOC platform architecture, and arranges a series of interactive learning sites with the community interface of Knowledge Farm, and finally, constructs the learning situation of a multi-level, three-dimensional intelligent SPOC game.
{"title":"Scenario Design of Intelligent SPOC Knowledge Farm Based on Flexible Learning","authors":"Huangxing Zeng","doi":"10.1145/3421766.3421784","DOIUrl":"https://doi.org/10.1145/3421766.3421784","url":null,"abstract":"Flexible learning is based on studying learners' psychology and behavior, using the principle of flexible management and flexible decision-making and adopting the mode of flexible contingency thinking to transform the will of teaching organizers into learners' conscious behavior through imperceptible change and cultivation. Flexible learning model can solve the problems, such as short of innovation, weak autonomy and lack of collective learning atmosphere, existing in network education. This paper introduces the concept and mechanism of flexible learning in the SPOC (Small Private Online Course), relies on big data and intelligent technology, optimizes the design of SPOC platform architecture, and arranges a series of interactive learning sites with the community interface of Knowledge Farm, and finally, constructs the learning situation of a multi-level, three-dimensional intelligent SPOC game.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127894426","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}
Recently, with the advancement of technologies in AI and Knowledge Base, several museums are using chatbots for visitors. One of the problems with these technologies, however is that gradually tends to be of no real interest to visitors owing to the lack of significant interaction, this eventually distracts visitors from experiencing the exhibits. In the demo, we present AIMuBot, an interactive system for searching the information from the museum's knowledge base with natural language. The system has the following characteristics: (1) It supports natural language voice-based interaction with the visitors to ask questions; (2) It provides a voice-based graphical interface to help visitors refine the questions. (3) It retrieves information from the knowledge base for the visitors.
{"title":"An AI chatbot for the museum based on user Interaction over a knowledge base","authors":"Chunyan Zhou, Baivab Sinha, Minghua Liu","doi":"10.1145/3421766.3421888","DOIUrl":"https://doi.org/10.1145/3421766.3421888","url":null,"abstract":"Recently, with the advancement of technologies in AI and Knowledge Base, several museums are using chatbots for visitors. One of the problems with these technologies, however is that gradually tends to be of no real interest to visitors owing to the lack of significant interaction, this eventually distracts visitors from experiencing the exhibits. In the demo, we present AIMuBot, an interactive system for searching the information from the museum's knowledge base with natural language. The system has the following characteristics: (1) It supports natural language voice-based interaction with the visitors to ask questions; (2) It provides a voice-based graphical interface to help visitors refine the questions. (3) It retrieves information from the knowledge base for the visitors.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128944833","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 data mining algorithm based on rough set plays a very important role in dealing with various application-oriented problems. The suitable algorithm can quickly and accurately mine the core of time attribute and simplify the problem. Based on the characteristics of the teaching and appraise system of English linguistics, this paper optimizes the teaching design of English Linguistics in terms of teaching. Under the framework of systemic functional linguistics, this paper makes a follow-up analysis of the appraise resources in English texts, and verifies the feasibility and effectiveness of this method through an example. Some key problems of English linguistics teaching and price system are solved by data mining algorithm.
{"title":"Application of Data Mining in English Linguistics Teaching and Appraisal System","authors":"Wei Zhang, Xue Wang","doi":"10.1145/3421766.3421824","DOIUrl":"https://doi.org/10.1145/3421766.3421824","url":null,"abstract":"The data mining algorithm based on rough set plays a very important role in dealing with various application-oriented problems. The suitable algorithm can quickly and accurately mine the core of time attribute and simplify the problem. Based on the characteristics of the teaching and appraise system of English linguistics, this paper optimizes the teaching design of English Linguistics in terms of teaching. Under the framework of systemic functional linguistics, this paper makes a follow-up analysis of the appraise resources in English texts, and verifies the feasibility and effectiveness of this method through an example. Some key problems of English linguistics teaching and price system are solved by data mining algorithm.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134433965","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}
Mathematical model of ship steam turbine unit including steam turbine, gear reducer and condenser is established. Given the reasonable boundary conditions, the weight and volume multi-objective optimization of a typical ship steam turbine is carried out by using a modified optimization algorithm to harmonize the selected design variables. The results show that the weight and volume of the optimized ship steam turbine unit decrease by 4.2% and 6.3% respectively, which demonstrates the capability of the optimization method in optimizing the weight and volume of ship steam turbine unit.
{"title":"Multi-Objective Optimization of Ship Steam Turbine Unit","authors":"Cheng Wang, Zhengming Tang, Xiang Wan, K. Cheng, Haijun Sun, Yuan Fang, Shan Gao","doi":"10.1145/3421766.3421893","DOIUrl":"https://doi.org/10.1145/3421766.3421893","url":null,"abstract":"Mathematical model of ship steam turbine unit including steam turbine, gear reducer and condenser is established. Given the reasonable boundary conditions, the weight and volume multi-objective optimization of a typical ship steam turbine is carried out by using a modified optimization algorithm to harmonize the selected design variables. The results show that the weight and volume of the optimized ship steam turbine unit decrease by 4.2% and 6.3% respectively, which demonstrates the capability of the optimization method in optimizing the weight and volume of ship steam turbine unit.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130115195","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}
A robust design based on DFSS is presented for double wishbone suspension system kinematic and compliance (K&C) performance. Variations in suspension K&C caused by the uncertainties of hard points and bushing stiffness coefficients are minimized. The robust design involves two steps. In the first step, suspension kinematic characteristic are optimized. The objective functions are the toe angle and camber angle, and random design variables are the hardpoints of joints. The bushing stiffness coefficients are assumed as constant design parameters. In the second step, suspension compliance characteristics are optimized, where the bushing stiffness coefficients are random design variables. The optimized hardpoints in the first step are treated as constant design parameters. The optimization result shows that the robustness of suspension K&C performance is improved.
{"title":"Double Wishbone Suspension Design Based on Design for Six Sigma (DFSS)","authors":"Zhu Kaimin, Gu Jinxiang","doi":"10.1145/3421766.3421866","DOIUrl":"https://doi.org/10.1145/3421766.3421866","url":null,"abstract":"A robust design based on DFSS is presented for double wishbone suspension system kinematic and compliance (K&C) performance. Variations in suspension K&C caused by the uncertainties of hard points and bushing stiffness coefficients are minimized. The robust design involves two steps. In the first step, suspension kinematic characteristic are optimized. The objective functions are the toe angle and camber angle, and random design variables are the hardpoints of joints. The bushing stiffness coefficients are assumed as constant design parameters. In the second step, suspension compliance characteristics are optimized, where the bushing stiffness coefficients are random design variables. The optimized hardpoints in the first step are treated as constant design parameters. The optimization result shows that the robustness of suspension K&C performance is improved.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134345357","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}
To clarify the problems of aviation radar in heat dissipation, optimize the overall operational capability of radar equipment, and improve the safety of aviation radar equipment, under the premise of studying the structure of the radar heat dissipation system, by analyzing the operation of the radar heat dissipation system and the motor of the front-end prototype structure, the main reasons for heat dissipation faults are deeply analyzed. The method of statistical process control is utilized to predict the performance of the front-end motor and remind maintenance personnel to monitor the radar heat dissipation system in real-time. At the same time, by using the improved particle swarm optimization (PSO) algorithm model, the factors and kernel functions of the support vector machine (SVM) are optimized, and the regression accuracy of the SVM is improved. Furthermore, the motor failure prediction model is established, thereby ensuring the efficient and safe operating state of the radar system. The results show: (1) the failure of the radar motor is the major cause of heat dissipation faults; (2) compared to other algorithms, the efficiency of the PSO algorithm is improved by 30%, but the accuracy rate drops by 5%; (3) the applications of forewarning model for front-end prototype under statistical process control (SPC) can reduce the workload of maintenance personnel by 50%. The simulation results show that the combined method of SPC and SVM can predict the failure of the powering devices in radar heat dissipation systems. Besides, if the classification and regression models are combined, the difference between the predicted voltage and the true voltage will be smaller, and the accuracy will be higher. The above results provide a theoretical basis for the research of radar heat dissipation system and motor failure, which ensures the overall safety of the radar system and provides the necessary guarantee for the crew and the aviation command system.
{"title":"The Performance of Radar Heat Dissipation System under Particle Swarm Optimization Algorithm and Structural Design of Front-end Prototype","authors":"Zhen Wang, Jinwen Zhou","doi":"10.1145/3421766.3421810","DOIUrl":"https://doi.org/10.1145/3421766.3421810","url":null,"abstract":"To clarify the problems of aviation radar in heat dissipation, optimize the overall operational capability of radar equipment, and improve the safety of aviation radar equipment, under the premise of studying the structure of the radar heat dissipation system, by analyzing the operation of the radar heat dissipation system and the motor of the front-end prototype structure, the main reasons for heat dissipation faults are deeply analyzed. The method of statistical process control is utilized to predict the performance of the front-end motor and remind maintenance personnel to monitor the radar heat dissipation system in real-time. At the same time, by using the improved particle swarm optimization (PSO) algorithm model, the factors and kernel functions of the support vector machine (SVM) are optimized, and the regression accuracy of the SVM is improved. Furthermore, the motor failure prediction model is established, thereby ensuring the efficient and safe operating state of the radar system. The results show: (1) the failure of the radar motor is the major cause of heat dissipation faults; (2) compared to other algorithms, the efficiency of the PSO algorithm is improved by 30%, but the accuracy rate drops by 5%; (3) the applications of forewarning model for front-end prototype under statistical process control (SPC) can reduce the workload of maintenance personnel by 50%. The simulation results show that the combined method of SPC and SVM can predict the failure of the powering devices in radar heat dissipation systems. Besides, if the classification and regression models are combined, the difference between the predicted voltage and the true voltage will be smaller, and the accuracy will be higher. The above results provide a theoretical basis for the research of radar heat dissipation system and motor failure, which ensures the overall safety of the radar system and provides the necessary guarantee for the crew and the aviation command system.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121845482","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}
Existing dehazing methods are usually to appear visual problems. In the paper, we put forward a truncated total variation method (TTV) to eliminate haze. A histogram analysis is firstly developed to obtain global atmospheric light. Then, using an adaptive boundary constraint TTV to optimize the transmission properly. Finally, a new DCP is presented to remove haze. Shown in experimental results, our method can outperform existent methods on the visual effect.
{"title":"Single Fog Image Dehazing via Truncated Total Variation Method","authors":"Yin Gao, Yijing Su, Jun Li","doi":"10.1145/3421766.3421772","DOIUrl":"https://doi.org/10.1145/3421766.3421772","url":null,"abstract":"Existing dehazing methods are usually to appear visual problems. In the paper, we put forward a truncated total variation method (TTV) to eliminate haze. A histogram analysis is firstly developed to obtain global atmospheric light. Then, using an adaptive boundary constraint TTV to optimize the transmission properly. Finally, a new DCP is presented to remove haze. Shown in experimental results, our method can outperform existent methods on the visual effect.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123754494","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}
A model renderer with style image generation is designed combine with illumination technology and style transfer technology for the illumination problems, that may be encountered in the process of computer image graphics design and production.In order to achieve a better rendering effect of the model, the rendering results are combined with style transfer innovatively.Different styles of model images can be applied in scenes such as games and movies to facilitate future development.
{"title":"Model Renderer Design with Style Image","authors":"Xiaozi Guo, Juan Zhang, Mingquan Zhou","doi":"10.1145/3421766.3421797","DOIUrl":"https://doi.org/10.1145/3421766.3421797","url":null,"abstract":"A model renderer with style image generation is designed combine with illumination technology and style transfer technology for the illumination problems, that may be encountered in the process of computer image graphics design and production.In order to achieve a better rendering effect of the model, the rendering results are combined with style transfer innovatively.Different styles of model images can be applied in scenes such as games and movies to facilitate future development.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115252528","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}
Artificial intelligence is changing the world. The application of the artificial intelligence in logistics has brought a significant reduction in labor costs and huge increase in logistics efficiency. Artificial intelligence will lead the logistics industry truly enter the era of intelligent logistics. Warehousing is one of the most important function of logistics, so the intelligent level of warehousing has great effects on the construction and promotion of intelligent logistics. This article aims to study the applications of artificial intelligence in logistics warehousing, and analyze the limiting factors of artificial intelligence applied in the construction of intelligent warehousing, finally briefly conclude its general situation and prospects of future development.
{"title":"Research on the Artificial Intelligence Applied in Logistics Warehousing","authors":"Xinke Du","doi":"10.1145/3421766.3421798","DOIUrl":"https://doi.org/10.1145/3421766.3421798","url":null,"abstract":"Artificial intelligence is changing the world. The application of the artificial intelligence in logistics has brought a significant reduction in labor costs and huge increase in logistics efficiency. Artificial intelligence will lead the logistics industry truly enter the era of intelligent logistics. Warehousing is one of the most important function of logistics, so the intelligent level of warehousing has great effects on the construction and promotion of intelligent logistics. This article aims to study the applications of artificial intelligence in logistics warehousing, and analyze the limiting factors of artificial intelligence applied in the construction of intelligent warehousing, finally briefly conclude its general situation and prospects of future development.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114699829","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}
CCTV News Broadcast is one of the most popular news programs in China, and it is also the most important propaganda platform in China. CCTV News Broadcast is established to "Improve the quality of publicity", so it is "A product of visual culture of national ideology" and "Taking politics as the standard" is primary appeal. [1] At present, there is little research on the text of CCTV News Broadcast. This paper focuses on the CCTV News Broadcast, using the visualization model based statistics and semantic based keyword extraction model (SKE) to extract the text features of CCTV News Broadcast. It can help the public quickly capture the key information of CCTV News Broadcast. Moreover, this paper also forms a set of Chinese corpus with keywords tagging in the field of CCTV News Broadcast. It provides important data support for machine learning method and subsequent research. In addition, aiming at some important problems found in this paper, this paper proposes further research direction for text data processing in CCTV News Broadcast field.
{"title":"CCTV News Broadcast Information Mining: Keyword Extraction Based on Semantic Model and Statistics Visualization","authors":"Yujie Xie, Fenghai Liu","doi":"10.1145/3421766.3421827","DOIUrl":"https://doi.org/10.1145/3421766.3421827","url":null,"abstract":"CCTV News Broadcast is one of the most popular news programs in China, and it is also the most important propaganda platform in China. CCTV News Broadcast is established to \"Improve the quality of publicity\", so it is \"A product of visual culture of national ideology\" and \"Taking politics as the standard\" is primary appeal. [1] At present, there is little research on the text of CCTV News Broadcast. This paper focuses on the CCTV News Broadcast, using the visualization model based statistics and semantic based keyword extraction model (SKE) to extract the text features of CCTV News Broadcast. It can help the public quickly capture the key information of CCTV News Broadcast. Moreover, this paper also forms a set of Chinese corpus with keywords tagging in the field of CCTV News Broadcast. It provides important data support for machine learning method and subsequent research. In addition, aiming at some important problems found in this paper, this paper proposes further research direction for text data processing in CCTV News Broadcast field.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114269767","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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