Pub Date : 2023-01-01DOI: 10.32604/jai.2023.043329
Gagan Deep, Jyoti Verma
This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information on the integration of AI and ML technologies in various aspects of smart cities, including infrastructure optimization, public safety enhancement, and citizen services improvement. The findings suggest that AI and ML technologies enable data-driven decision-making, predictive analytics, and optimization in smart city development. They are vital to the development of transport infrastructure, optimizing energy distribution, improving public safety, streamlining governance, and transforming healthcare services. However, ethical and privacy considerations, as well as technical challenges, need to be solved to guarantee the ethical and responsible usage of AI and ML in smart cities. The study concludes by discussing the challenges and future directions of AI and ML in shaping urban environments, highlighting the importance of collaborative efforts and responsible implementation. The findings highlight the transformative potential of AI and ML in optimizing resource utilization, enhancing citizen services, and creating more sustainable and resilient smart cities. Future studies should concentrate on addressing technical limitations, creating robust policy frameworks, and fostering fairness, accountability, and openness in the use of AI and ML technologies in smart cities.
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Pub Date : 2023-01-01DOI: 10.32604/jai.2023.043229
Rufus Gikera, Jonathan Mwaura, Elizaphan Muuro, Shadrack Mambo
{"title":"K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes","authors":"Rufus Gikera, Jonathan Mwaura, Elizaphan Muuro, Shadrack Mambo","doi":"10.32604/jai.2023.043229","DOIUrl":"https://doi.org/10.32604/jai.2023.043229","url":null,"abstract":"","PeriodicalId":70951,"journal":{"name":"人工智能杂志(英文)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134883935","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}
Structural health monitoring (SHM) is considered an effective approach to analyze the efficient working of several mechanical components. For this purpose, ultrasonic guided waves can cover long-distance and assess large infrastructures in just a single test using a small number of transducers. However, the working of the SHM mechanism can be affected by some sources of variations (i.e., environmental). To improve the final results of ultrasonic guided wave inspections, it is necessary to highlight and attenuate these environmental variations. The loading parameters, temperature and humidity have been recognized as the core environmental sources of variations that affect the SHM sensing mechanism. Environmental temperature has the most significant influence on SHM results. There is still a need for extensive research to develop such a damage inspection approach that should be insensitive to environmental temperature variations. In this framework, the current research study will not only illuminate the effect of environmental temperature through different intelligent approaches but also suggest the standard mechanism to attenuate it in actual ultrasonic guided wave based SHM. Hence, the work presented in this article addresses one of the open research challenges that are the identification of the effect of environmental and operating conditions in practical applications of ultrasonic guided waves and impedance-based SHM.
{"title":"Study of Intelligent Approaches to Identify Impact of Environmental Temperature on Ultrasonic GWs Based SHM: A Review","authors":"Saqlain Abbas, Zulkarnain Abbas, Xiaotong Tu, Yanping Zhu","doi":"10.32604/jai.2023.040948","DOIUrl":"https://doi.org/10.32604/jai.2023.040948","url":null,"abstract":"Structural health monitoring (SHM) is considered an effective approach to analyze the efficient working of several mechanical components. For this purpose, ultrasonic guided waves can cover long-distance and assess large infrastructures in just a single test using a small number of transducers. However, the working of the SHM mechanism can be affected by some sources of variations (i.e., environmental). To improve the final results of ultrasonic guided wave inspections, it is necessary to highlight and attenuate these environmental variations. The loading parameters, temperature and humidity have been recognized as the core environmental sources of variations that affect the SHM sensing mechanism. Environmental temperature has the most significant influence on SHM results. There is still a need for extensive research to develop such a damage inspection approach that should be insensitive to environmental temperature variations. In this framework, the current research study will not only illuminate the effect of environmental temperature through different intelligent approaches but also suggest the standard mechanism to attenuate it in actual ultrasonic guided wave based SHM. Hence, the work presented in this article addresses one of the open research challenges that are the identification of the effect of environmental and operating conditions in practical applications of ultrasonic guided waves and impedance-based SHM.","PeriodicalId":70951,"journal":{"name":"人工智能杂志(英文)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135501431","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 : 2023-01-01DOI: 10.32604/jai.2023.043970
Tian Lu, Bohong Liu
To realize a better automatic train driving operation control strategy for urban rail trains, an automatic train driving method with improved DQN algorithm (classical deep reinforcement learning algorithm) is proposed as a research object. Firstly, the train control model is established by considering the train operation requirements. Secondly, the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem. Finally, the priority experience playback and “restricted speed arrival time” are used to reduce the useless experience utilization. The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions. From the experimental results, the train operation meets the ATO requirements, the energy consumption is 15.75% more energy-efficient than the actual operation, and the algorithm convergence speed is improved by about 37%. The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation, thereby contributing meaningfully to the advancement of automatic train operation intelligence.
{"title":"Automatic Driving Operation Strategy of Urban Rail Train Based on Improved DQN Algorithm","authors":"Tian Lu, Bohong Liu","doi":"10.32604/jai.2023.043970","DOIUrl":"https://doi.org/10.32604/jai.2023.043970","url":null,"abstract":"To realize a better automatic train driving operation control strategy for urban rail trains, an automatic train driving method with improved DQN algorithm (classical deep reinforcement learning algorithm) is proposed as a research object. Firstly, the train control model is established by considering the train operation requirements. Secondly, the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem. Finally, the priority experience playback and “restricted speed arrival time” are used to reduce the useless experience utilization. The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions. From the experimental results, the train operation meets the ATO requirements, the energy consumption is 15.75% more energy-efficient than the actual operation, and the algorithm convergence speed is improved by about 37%. The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation, thereby contributing meaningfully to the advancement of automatic train operation intelligence.","PeriodicalId":70951,"journal":{"name":"人工智能杂志(英文)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134890142","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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