Zhenyu Yu, Zhibao Wang, L. Bai, Liangfu Chen, J. Tao
With the rapid growth of remote sensing satellites, the volume of remote sensing data has been continuously increasing, which makes it necessary to utilize the big data platform for the rapid practical application of remote sensing inversion algorithms. This paper proposes an atmospheric remote sensing inversion processing method based on Spark. As a popular large-scale data processing framework, the memory-based iterable calculation model of Spark makes it suitable for the application of atmospheric remote sensing inversion. In this paper, we use the Spark computing framework to calculate the average value of the particulate matter in China over the past 10 years and the running time is much faster than the traditional single-node method. Furthermore, how Spark configuration parameters affect the performance of the task is explored. Different regression models in XGBoost are used to evaluate the performance of the parameters obtained by the parameter optimization algorithm in order to find the Spark optimal configuration parameters that meet the requirements.
{"title":"Parameter Optimization on Spark for Particulate Matter Estimation","authors":"Zhenyu Yu, Zhibao Wang, L. Bai, Liangfu Chen, J. Tao","doi":"10.1145/3456389.3456406","DOIUrl":"https://doi.org/10.1145/3456389.3456406","url":null,"abstract":"With the rapid growth of remote sensing satellites, the volume of remote sensing data has been continuously increasing, which makes it necessary to utilize the big data platform for the rapid practical application of remote sensing inversion algorithms. This paper proposes an atmospheric remote sensing inversion processing method based on Spark. As a popular large-scale data processing framework, the memory-based iterable calculation model of Spark makes it suitable for the application of atmospheric remote sensing inversion. In this paper, we use the Spark computing framework to calculate the average value of the particulate matter in China over the past 10 years and the running time is much faster than the traditional single-node method. Furthermore, how Spark configuration parameters affect the performance of the task is explored. Different regression models in XGBoost are used to evaluate the performance of the parameters obtained by the parameter optimization algorithm in order to find the Spark optimal configuration parameters that meet the requirements.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124318618","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 intelligent power system has developed rapidly in recent years. In face of the shortcomings of the existing infrared thermal imager in power inspection, an intelligent power infrared inspection device based on edge computing is developed. The problems found in the substation temperature measurement are analyzed. In order to find out the problems and predict the defects in time, the prediction for the substation equipment is carried out through the edge calculation. The results show that, compared with the traditional infrared thermal imager, many aspects of the intelligent inspection device are superior, including the field application ability of intelligent navigation, infrared temperature measurement and substation equipment identification, remote monitoring, and so on.
{"title":"Research on Intelligent Infrared Inspection Device Based on Edge Computing","authors":"Xiaolu Liu, Sufang Huang","doi":"10.1145/3456389.3456394","DOIUrl":"https://doi.org/10.1145/3456389.3456394","url":null,"abstract":"The intelligent power system has developed rapidly in recent years. In face of the shortcomings of the existing infrared thermal imager in power inspection, an intelligent power infrared inspection device based on edge computing is developed. The problems found in the substation temperature measurement are analyzed. In order to find out the problems and predict the defects in time, the prediction for the substation equipment is carried out through the edge calculation. The results show that, compared with the traditional infrared thermal imager, many aspects of the intelligent inspection device are superior, including the field application ability of intelligent navigation, infrared temperature measurement and substation equipment identification, remote monitoring, and so on.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"398 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115219036","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, the question of whether machine can have its “self-consciousness” has become a focus being concerned and thought of. e Researches on machine consciousness or artificial consciousness, has gradually become a hot spot in the field of artificial intelligence (AI). With the common sense of human being as the only intelligent life with “self-consciousness”, only human's self-consciousness can be taken as a model in order to build AI with self-consciousness. In this paper, the theories of self-consciousness from the perspectives of psychology, cognitive neuroscience, philosophy and cognitive science were introduced with the hope of providing new ideas for the development of AI with self-consciousness.
{"title":"Artificial Intelligence and Its Self-Consciousness","authors":"Hong Lu, Weibing Hu, Chaochao Pan","doi":"10.1145/3456389.3456402","DOIUrl":"https://doi.org/10.1145/3456389.3456402","url":null,"abstract":"Recently, the question of whether machine can have its “self-consciousness” has become a focus being concerned and thought of. e Researches on machine consciousness or artificial consciousness, has gradually become a hot spot in the field of artificial intelligence (AI). With the common sense of human being as the only intelligent life with “self-consciousness”, only human's self-consciousness can be taken as a model in order to build AI with self-consciousness. In this paper, the theories of self-consciousness from the perspectives of psychology, cognitive neuroscience, philosophy and cognitive science were introduced with the hope of providing new ideas for the development of AI with self-consciousness.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126161283","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}
This paper proposes a dynamic range model and algorithm that analyzes several key factors that affect the dynamic range of imaging equipment. Through this model and algorithm, we analyze several modules that affect the dynamic range of the system, including sensor noise, AD conversion noise, analog gain, digital gain and mapping, and give the quantitative relationships which are used to guide designers to design high performance dynamic range imaging equipment. According to the model and algorithm, a high performance dynamic range imaging equipment can be designed. A hardware extension method based on multi-slope integration is proposed, and the experimental results show that this method is an effective dynamic range expansion method.
{"title":"Research on Dynamic Range Analysis and Improvement of Imaging Equipment","authors":"Hao Wang, Youhui Huo, Hongbo Zhang","doi":"10.1145/3456389.3456392","DOIUrl":"https://doi.org/10.1145/3456389.3456392","url":null,"abstract":"This paper proposes a dynamic range model and algorithm that analyzes several key factors that affect the dynamic range of imaging equipment. Through this model and algorithm, we analyze several modules that affect the dynamic range of the system, including sensor noise, AD conversion noise, analog gain, digital gain and mapping, and give the quantitative relationships which are used to guide designers to design high performance dynamic range imaging equipment. According to the model and algorithm, a high performance dynamic range imaging equipment can be designed. A hardware extension method based on multi-slope integration is proposed, and the experimental results show that this method is an effective dynamic range expansion method.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130431364","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 charging technology of electric vehicles has always affected the development of electric vehicles. If you want to use electric vehicles to provide travel services like fuel cars, electric vehicle service providers must consider the charging problem of electric vehicles. In order to maintain the safe and stable operation of the power grid, smart grids usually adopt real-time electricity pricing strategies. Under the strategy of real-time electricity prices, electricity prices will increase with the increase of electricity consumption by electricity users, and decrease with the decrease of electricity consumption. Electricity users adjust their electricity demand based on real-time electricity prices, which is called user demand response based on real-time electricity prices. In this paper, our goal is to formulate a minimum-cost charging plan for electric vehicles while considering the demand response of other power users. Based on the competition relationship between electric vehicles and other power users, we model it as a game model and use the double oracle algorithm to solve it. Finally, a simulation experiment shows the feasibility of our model and algorithm, and reduces the cost of electricity for all users, and the burden on the power grid.
{"title":"Charging Planning of Electric Vehicle Manager Based on Price Demand","authors":"Zheng Zhao, Mingchu Li, Tingting Tang, Cheng Guo","doi":"10.1145/3456389.3456393","DOIUrl":"https://doi.org/10.1145/3456389.3456393","url":null,"abstract":"The charging technology of electric vehicles has always affected the development of electric vehicles. If you want to use electric vehicles to provide travel services like fuel cars, electric vehicle service providers must consider the charging problem of electric vehicles. In order to maintain the safe and stable operation of the power grid, smart grids usually adopt real-time electricity pricing strategies. Under the strategy of real-time electricity prices, electricity prices will increase with the increase of electricity consumption by electricity users, and decrease with the decrease of electricity consumption. Electricity users adjust their electricity demand based on real-time electricity prices, which is called user demand response based on real-time electricity prices. In this paper, our goal is to formulate a minimum-cost charging plan for electric vehicles while considering the demand response of other power users. Based on the competition relationship between electric vehicles and other power users, we model it as a game model and use the double oracle algorithm to solve it. Finally, a simulation experiment shows the feasibility of our model and algorithm, and reduces the cost of electricity for all users, and the burden on the power grid.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123220966","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}
K. Venkataravana Nayak, J. Arunalatha, K. Venugopal
Presence of inconsistency in the visual appearance of image tends to degrade the retrieval process performance. Increasing image data across several domains encourages to explore visual information of image representation to simplify the interpretation and concentrate on discriminative features of images so as to use them for retrieving relevant images for increasing machine learning model performance. Thus, features fusion and k-Nearest Neighbours (IR-FF-kNN); an Image Retrieval framework is proposed to increase retrieval performance. The Histogram of oriented Gradients (HoG), Color Moments (CM) and Center Symmetric Local Binary Pattern (CSLBP) descriptors are used to obtain multiple features of images in the features extraction phase and in similarity computation phase, the kNN classifier is used. The proposed framework is tested on MIR Flickr dataset and provides mean average precision of 85% compared to the state-of-the-arts.
{"title":"IR-FF-kNN: Image Retrieval Using Feature Fusion with k-Nearest Neighbour Classifier","authors":"K. Venkataravana Nayak, J. Arunalatha, K. Venugopal","doi":"10.1145/3456389.3456405","DOIUrl":"https://doi.org/10.1145/3456389.3456405","url":null,"abstract":"Presence of inconsistency in the visual appearance of image tends to degrade the retrieval process performance. Increasing image data across several domains encourages to explore visual information of image representation to simplify the interpretation and concentrate on discriminative features of images so as to use them for retrieving relevant images for increasing machine learning model performance. Thus, features fusion and k-Nearest Neighbours (IR-FF-kNN); an Image Retrieval framework is proposed to increase retrieval performance. The Histogram of oriented Gradients (HoG), Color Moments (CM) and Center Symmetric Local Binary Pattern (CSLBP) descriptors are used to obtain multiple features of images in the features extraction phase and in similarity computation phase, the kNN classifier is used. The proposed framework is tested on MIR Flickr dataset and provides mean average precision of 85% compared to the state-of-the-arts.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128011613","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}
Contemporary architecture design is featured by human-oriented. Getting the users’ feedback of schemes thus to optimize their positive interactions with spaces is essential to increase designs’ quality and attraction. However, uncovering users’ true thoughts is challenging because it is difficult for laypersons with no professional design background to describe how architectural elements can affect their emotions. This study aims to use digital technology to comprehend architectural elements’ impact on users’ perception. Specifically, it combines virtual reality (VR) with biosensing technology to detect users’ emotions, guiding designers to improve their design schemes. In this context, to propose a method of evaluation, this research adopted the International School of Design of Harbin Institute of Technology's outdoor spaces as the empirical case. VR had been applied as a visualization tool to construct a dynamic and immersion environment that imitates the campus space. Then some key design elements were changed to form contrast schemes. Participants were recruited to experience these contrast spaces and were collected Electrodermal Activity (EDA) and Photoplethysmography (PPG) signals for emotional arousal analysis. Biofeedback data were analyzed using the paired-samples T-test. The results indicated that some of the design elements did affect users’ emotional arousal during roaming; others did not cause a significant difference affection. According to the evaluation result, designers can understand the emotional connection between a design element and users to decide if it needs to be further refined. This research proposed a method that can be used for dynamic and repeatable evaluation of design before the project is completed. Simultaneously, this method can help designers quantitate users’ emotional perception and gain the objective basis for design optimization.
{"title":"Biofeedback in the Dynamic VR Environments:: A Method to Evaluate the Influence of Architectural Elements on Human Spatial Perception","authors":"Wanyu Pei, Xiangmin Guo, T. Lo","doi":"10.1145/3456389.3456400","DOIUrl":"https://doi.org/10.1145/3456389.3456400","url":null,"abstract":"Contemporary architecture design is featured by human-oriented. Getting the users’ feedback of schemes thus to optimize their positive interactions with spaces is essential to increase designs’ quality and attraction. However, uncovering users’ true thoughts is challenging because it is difficult for laypersons with no professional design background to describe how architectural elements can affect their emotions. This study aims to use digital technology to comprehend architectural elements’ impact on users’ perception. Specifically, it combines virtual reality (VR) with biosensing technology to detect users’ emotions, guiding designers to improve their design schemes. In this context, to propose a method of evaluation, this research adopted the International School of Design of Harbin Institute of Technology's outdoor spaces as the empirical case. VR had been applied as a visualization tool to construct a dynamic and immersion environment that imitates the campus space. Then some key design elements were changed to form contrast schemes. Participants were recruited to experience these contrast spaces and were collected Electrodermal Activity (EDA) and Photoplethysmography (PPG) signals for emotional arousal analysis. Biofeedback data were analyzed using the paired-samples T-test. The results indicated that some of the design elements did affect users’ emotional arousal during roaming; others did not cause a significant difference affection. According to the evaluation result, designers can understand the emotional connection between a design element and users to decide if it needs to be further refined. This research proposed a method that can be used for dynamic and repeatable evaluation of design before the project is completed. Simultaneously, this method can help designers quantitate users’ emotional perception and gain the objective basis for design optimization.","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125736815","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}
{"title":"2021 Workshop on Algorithm and Big Data","authors":"","doi":"10.1145/3456389","DOIUrl":"https://doi.org/10.1145/3456389","url":null,"abstract":"","PeriodicalId":124603,"journal":{"name":"2021 Workshop on Algorithm and Big Data","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123001633","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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