Pub Date : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238089
Anrui Fu, Bo Wang
The research of portfolio optimization is to rationally allocate capital in an uncertain environment so as to realize the balance between returns and risks. In this paper, a prediction-based multi-period portfolio model is proposed to provide investors with a more economical and reliable resource allocation scheme. It utilizes LSTM neural network to predict the future stock prices, while the improved particle swarm optimization algorithm is used to solve the problem. Finally, the feasibility and validity of the model is verified through empirical research.
{"title":"Portfolio Optimization based on LSTM Neural Network Prediction","authors":"Anrui Fu, Bo Wang","doi":"10.1109/ICNSC48988.2020.9238089","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238089","url":null,"abstract":"The research of portfolio optimization is to rationally allocate capital in an uncertain environment so as to realize the balance between returns and risks. In this paper, a prediction-based multi-period portfolio model is proposed to provide investors with a more economical and reliable resource allocation scheme. It utilizes LSTM neural network to predict the future stock prices, while the improved particle swarm optimization algorithm is used to solve the problem. Finally, the feasibility and validity of the model is verified through empirical research.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115733159","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 intelligent transportation systems, LiDAR has been used to acquire traffic information on the roadside. Due to the sensing range and occlusions between vehicles, single LiDAR can only be applied in simple scenes and limited scope. In this paper, multiple LiDARs are applied to solve the problems of traffic information sensing in the complex traffic environment. A new point cloud registration method is proposed. This method combines the advantages of the iterative closest point (ICP) algorithm and the Zhang's calibration method for camera calibration. First of all, a reference system is made for registration, so that the registration of two sets of points is converted to the registration of reference points with different coordinates. Second, filtering based on intensity is conducted to extract the points on the reference system. To remove noises, we apply the density-based spatial clustering of applications with noise (DBSCAN) algorithm for denoising in this paper. Then, a robust ICP algorithm based on M-estimation is applied to realize the registration of reference points in two coordinate systems. Finally, this method has been demonstrated by some experiments in real traffic scenes, experiment results show that the proposed method can achieve accurate registration of point cloud data from multiple LiDARs. Besides, the convergence time of this method is about 10 seconds, which can achieve better performance compared with traditional point registration methods.
{"title":"3D Point Cloud Registration for Multiple Roadside LiDARs with Retroreflective Reference","authors":"Zheyuan Zhang, Jianying Zheng, Rongchuan Sun, Zhenyao Zhang","doi":"10.1109/ICNSC48988.2020.9238070","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238070","url":null,"abstract":"In intelligent transportation systems, LiDAR has been used to acquire traffic information on the roadside. Due to the sensing range and occlusions between vehicles, single LiDAR can only be applied in simple scenes and limited scope. In this paper, multiple LiDARs are applied to solve the problems of traffic information sensing in the complex traffic environment. A new point cloud registration method is proposed. This method combines the advantages of the iterative closest point (ICP) algorithm and the Zhang's calibration method for camera calibration. First of all, a reference system is made for registration, so that the registration of two sets of points is converted to the registration of reference points with different coordinates. Second, filtering based on intensity is conducted to extract the points on the reference system. To remove noises, we apply the density-based spatial clustering of applications with noise (DBSCAN) algorithm for denoising in this paper. Then, a robust ICP algorithm based on M-estimation is applied to realize the registration of reference points in two coordinate systems. Finally, this method has been demonstrated by some experiments in real traffic scenes, experiment results show that the proposed method can achieve accurate registration of point cloud data from multiple LiDARs. Besides, the convergence time of this method is about 10 seconds, which can achieve better performance compared with traditional point registration methods.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124863299","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238128
Manting Yao, Weina Yuan, Nan Wang, Zeyu Zhang, Yuan Qiu, Yichuan Liu
Design for trust approaches can protect an MPSoC system from hardware Trojan attack due to the high penetration of third-party intellectual property. However, this incurs significant design cost by purchasing IP cores from various IP vendors, and the IP vendors providing particular IP are always limited, making these approaches unable to be performed in practice. This paper treats IP vendor as constraint, and tasks are scheduled with a minimized security constraint violations, furthermore, the area of MPSoC is also optimized during scheduling. Experimental results demonstrate the effectiveness of our proposed algorithm, by reducing 0.37% security constraint violations.
{"title":"SS3: Security-Aware Vendor-Constrained Task Scheduling for Heterogeneous Multiprocessor System-on-Chips","authors":"Manting Yao, Weina Yuan, Nan Wang, Zeyu Zhang, Yuan Qiu, Yichuan Liu","doi":"10.1109/ICNSC48988.2020.9238128","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238128","url":null,"abstract":"Design for trust approaches can protect an MPSoC system from hardware Trojan attack due to the high penetration of third-party intellectual property. However, this incurs significant design cost by purchasing IP cores from various IP vendors, and the IP vendors providing particular IP are always limited, making these approaches unable to be performed in practice. This paper treats IP vendor as constraint, and tasks are scheduled with a minimized security constraint violations, furthermore, the area of MPSoC is also optimized during scheduling. Experimental results demonstrate the effectiveness of our proposed algorithm, by reducing 0.37% security constraint violations.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"265 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123293112","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238097
J. Bi, Yongze Lin, Quanxi Dong, Haitao Yuan, Mengchu Zhou
The prediction of accurate water quality has great significance to the sustainable management of water resources and pollution prevention. Due to the complexity of water environment, it is difficult to do so. Traditional prediction methods are mainly linear methods. Their prediction accuracy is limited since they fail to reflect nonlinear characteristics in water quality data. To achieve much higher accuracy, this work proposes to combines a Savitzky-Golay filter with Attention-based Long Short-Term Memory to perform a multi-step prediction of water quality. The proposed model uses a Savitzky-Golay filter for smoothing sequences to reduce noise interference. The adoption of an attention mechanism can extract effective information from complex, long, and temporal dependence. Experimental results demonstrate that the proposed method outperforms other state-of-the-art peers.
{"title":"An Improved Attention-based LSTM for Multi-Step Dissolved Oxygen Prediction in Water Environment","authors":"J. Bi, Yongze Lin, Quanxi Dong, Haitao Yuan, Mengchu Zhou","doi":"10.1109/ICNSC48988.2020.9238097","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238097","url":null,"abstract":"The prediction of accurate water quality has great significance to the sustainable management of water resources and pollution prevention. Due to the complexity of water environment, it is difficult to do so. Traditional prediction methods are mainly linear methods. Their prediction accuracy is limited since they fail to reflect nonlinear characteristics in water quality data. To achieve much higher accuracy, this work proposes to combines a Savitzky-Golay filter with Attention-based Long Short-Term Memory to perform a multi-step prediction of water quality. The proposed model uses a Savitzky-Golay filter for smoothing sequences to reduce noise interference. The adoption of an attention mechanism can extract effective information from complex, long, and temporal dependence. Experimental results demonstrate that the proposed method outperforms other state-of-the-art peers.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123676394","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238065
Li Ding, Deng-ping Tang, Wei Wei, Fan Li, Wenjia Cai
In today's widely used electric power, the performance and quality requirements for watt-hour meters are higher and higher. In practical application, watt-hour meters often fail for various reasons. For the fault data of watt-hour meter, how to make scientific analysis to improve the utilization rate of watt-hour meter is a practical problem. This paper analyzes the fault data of the watt-hour meter given by Hubei Power Grid. Firstly, the fault data of watt-hour meter is preprocessed, that is, the data is filtered, the abnormal data is eliminated, and then the data is cleaned based on Gaussian mixture model (GMM). Finally, a fault analysis method of watt-hour meter based on data normalization is proposed, that is, by analyzing the fault causes of watt-hour meter, a suitable mathematical model based on analytic hierarchy process (AHP) is established to obtain the quality of the manufacturer's watt-hour meter. According to the results of quality analysis, quantitative evaluation and hierarchical management of supplier product quality and design scheme are carried out to provide reference for bidding of intelligent watt-hour meter. The model method can effectively prevent a wide range of electrical energy meter failures, and it is of great significance to study the reliability and stability of power system.
{"title":"Research on Fault Analysis Technology of Watt-hour Meter Based on Analytic Hierarchy Process","authors":"Li Ding, Deng-ping Tang, Wei Wei, Fan Li, Wenjia Cai","doi":"10.1109/ICNSC48988.2020.9238065","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238065","url":null,"abstract":"In today's widely used electric power, the performance and quality requirements for watt-hour meters are higher and higher. In practical application, watt-hour meters often fail for various reasons. For the fault data of watt-hour meter, how to make scientific analysis to improve the utilization rate of watt-hour meter is a practical problem. This paper analyzes the fault data of the watt-hour meter given by Hubei Power Grid. Firstly, the fault data of watt-hour meter is preprocessed, that is, the data is filtered, the abnormal data is eliminated, and then the data is cleaned based on Gaussian mixture model (GMM). Finally, a fault analysis method of watt-hour meter based on data normalization is proposed, that is, by analyzing the fault causes of watt-hour meter, a suitable mathematical model based on analytic hierarchy process (AHP) is established to obtain the quality of the manufacturer's watt-hour meter. According to the results of quality analysis, quantitative evaluation and hierarchical management of supplier product quality and design scheme are carried out to provide reference for bidding of intelligent watt-hour meter. The model method can effectively prevent a wide range of electrical energy meter failures, and it is of great significance to study the reliability and stability of power system.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125680512","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238123
Hang Liu, Yunni Xia, Lei Wu, Peng Chen
Recently, the Cloud Computing paradigm is becoming increasingly popular in supporting large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service (QoS), attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations, e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge and thus they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real-time. To address this problem, we propose a novel Reinforcement-Learning-Based algorithm to multi-workflow scheduling over IaaS clouds. The proposed algorithm aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. In the experiment, our algorithm is evaluated for famous scientific workflow templates and real-world industrial IaaS cloud platforms by a simulation process and we compare our algorithm to the current state-of-the-art heuristic algorithms, e.g., NSGA-II, MOPSO, GTBGA. The result shows that our algorithm performs better than compared algorithm.
{"title":"A Novel Reinforcement-Learning-Based Approach to Scientific Workflow Scheduling","authors":"Hang Liu, Yunni Xia, Lei Wu, Peng Chen","doi":"10.1109/ICNSC48988.2020.9238123","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238123","url":null,"abstract":"Recently, the Cloud Computing paradigm is becoming increasingly popular in supporting large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service (QoS), attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations, e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge and thus they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real-time. To address this problem, we propose a novel Reinforcement-Learning-Based algorithm to multi-workflow scheduling over IaaS clouds. The proposed algorithm aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. In the experiment, our algorithm is evaluated for famous scientific workflow templates and real-world industrial IaaS cloud platforms by a simulation process and we compare our algorithm to the current state-of-the-art heuristic algorithms, e.g., NSGA-II, MOPSO, GTBGA. The result shows that our algorithm performs better than compared algorithm.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114426295","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238126
Jing Wang, Deqiang Xin, Zihao Chen, Yunsen Zhou
Traditional industrial data interface follows the object linking and embedding for Process Control (OPC) standard and relies on the personal computers or on-site computers, which has some limitations, e.g., the dependence of Windows platform, too much service couplings, and inflexible usages. There is few researches on scalable real-time data interface with other platforms, and an efficient and scalable real-time data interface that supports mobile applications is one of the key technologies to be solved in software engineering for Internet of Things(IOT). So in this paper, we consider scalable real-time data interface with application to Android, based on OPC. We develop an architecture of the scalable real-time data interface by sufficient analysis, and then present several corresponding modules on Android for application. Finally, we test our designed scalable real-time data interface based on a process control device, and apply it to Android platform. The results show that our designed scalable real-time data interface is practical and efficient.
{"title":"Scalable Real-time Data Interface With Application to Android Based on OPC for IOT","authors":"Jing Wang, Deqiang Xin, Zihao Chen, Yunsen Zhou","doi":"10.1109/ICNSC48988.2020.9238126","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238126","url":null,"abstract":"Traditional industrial data interface follows the object linking and embedding for Process Control (OPC) standard and relies on the personal computers or on-site computers, which has some limitations, e.g., the dependence of Windows platform, too much service couplings, and inflexible usages. There is few researches on scalable real-time data interface with other platforms, and an efficient and scalable real-time data interface that supports mobile applications is one of the key technologies to be solved in software engineering for Internet of Things(IOT). So in this paper, we consider scalable real-time data interface with application to Android, based on OPC. We develop an architecture of the scalable real-time data interface by sufficient analysis, and then present several corresponding modules on Android for application. Finally, we test our designed scalable real-time data interface based on a process control device, and apply it to Android platform. The results show that our designed scalable real-time data interface is practical and efficient.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121872339","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238093
Jin-Yi Deng, Hao Feng, Yu Bai, H. Zhan, Dong-Xiu Feng, Kaixiang Guo, Rong Zeng, Jian-Jian Xia, Yong-Jun Xie
In recent years, modern tramcar has developed rapidly. The grooved rail is used widely in its main line track. However, at present, the main detection method of its geometric parameters of grooved rail is manual detection, with low working efficiency. Therefore, this paper proposes a set of dynamic detection system of grooved rail irregularity based on inertial reference method, which can realize the detection of grooved rail irregularity such as longitudinal irregularity, alignment irregularity, etc. On the basis of previous work, improvement & application of measuring longitudinal irregularity and alignment irregularity has been proposed. The key points of this improved algorithm are to put forward synthesized filtering algorithm based upon grooved rail's geometric features, which enhances the stability of the detect system; and use the frequency domain integration algorithm, which improves the accuracy of the detect system. Experiment verification shows that the dynamic detection system has the characteristics of high detection accuracy and splendid stability, which provides a new tool for dynamic detection of geometric parameters of grooved rail of modern tramcars.
{"title":"Dynamic Detection of Grooved Rail Irregularity Based on Inertial Reference Method","authors":"Jin-Yi Deng, Hao Feng, Yu Bai, H. Zhan, Dong-Xiu Feng, Kaixiang Guo, Rong Zeng, Jian-Jian Xia, Yong-Jun Xie","doi":"10.1109/ICNSC48988.2020.9238093","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238093","url":null,"abstract":"In recent years, modern tramcar has developed rapidly. The grooved rail is used widely in its main line track. However, at present, the main detection method of its geometric parameters of grooved rail is manual detection, with low working efficiency. Therefore, this paper proposes a set of dynamic detection system of grooved rail irregularity based on inertial reference method, which can realize the detection of grooved rail irregularity such as longitudinal irregularity, alignment irregularity, etc. On the basis of previous work, improvement & application of measuring longitudinal irregularity and alignment irregularity has been proposed. The key points of this improved algorithm are to put forward synthesized filtering algorithm based upon grooved rail's geometric features, which enhances the stability of the detect system; and use the frequency domain integration algorithm, which improves the accuracy of the detect system. Experiment verification shows that the dynamic detection system has the characteristics of high detection accuracy and splendid stability, which provides a new tool for dynamic detection of geometric parameters of grooved rail of modern tramcars.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128656110","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}
although online education has improved efficiency of learners' access to high-quality educational resources, realtime interaction between instructors and learners have not yet been achieved in online personalized learning. Intelligent Tutoring Systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research &development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed.
{"title":"A Parallel Education Based Intelligent Tutoring Systems Framework","authors":"Sifeng Jing, Ying Tang, Xiwei Liu, Xiaoyan Gong, Wei Cui, Joleen Liang","doi":"10.1109/ICNSC48988.2020.9238052","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238052","url":null,"abstract":"although online education has improved efficiency of learners' access to high-quality educational resources, realtime interaction between instructors and learners have not yet been achieved in online personalized learning. Intelligent Tutoring Systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research &development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129193903","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238074
Shudong Guo, Weisong Qiao, Binbin Chen, Bo Wang
Climate change, as an important environmental issue, has been widely investigated in recent decades. On the one hand, the climate prediction is an essential part for policy makers to response to the change of climate, which has received many attentions. On the other hand, there is another challenging problem facing us today that some abnormal weathers occur globally, which seems to have relation to climate change, e.g., the global greenhouse effect, but with little existing researches on this relation. Therefore, in this paper, we propose two kinds of climatic and meteorological models based on statistical data: 1) an autoregressive-moving-average (ARMA) prediction model with principal component analysis (PCA) and 2) abnormal analysis model based on Pearson correlation coefficient (PCC). In detail, firstly, we propose the PCA-ARMA prediction model to predict climate change in the next 25 years, including two steps: 1) generation of new components for data reduction by PCA using the past 75 years' data, and 2) prediction based on step 1 by ARMA for next 25 years. Then, we establish another model to find out the relation between climate change and abnormal weathers, e.g., the extreme cold weather, mainly by PCC. The relevant data are collected, and by these two models, we get the corresponding results, which show that our prediction fits well and the abnormal weather is strongly connected with the climate change.
{"title":"Prediction and Abnormality Analysis of Climate Change Based on PCA-ARMA and PCC","authors":"Shudong Guo, Weisong Qiao, Binbin Chen, Bo Wang","doi":"10.1109/ICNSC48988.2020.9238074","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238074","url":null,"abstract":"Climate change, as an important environmental issue, has been widely investigated in recent decades. On the one hand, the climate prediction is an essential part for policy makers to response to the change of climate, which has received many attentions. On the other hand, there is another challenging problem facing us today that some abnormal weathers occur globally, which seems to have relation to climate change, e.g., the global greenhouse effect, but with little existing researches on this relation. Therefore, in this paper, we propose two kinds of climatic and meteorological models based on statistical data: 1) an autoregressive-moving-average (ARMA) prediction model with principal component analysis (PCA) and 2) abnormal analysis model based on Pearson correlation coefficient (PCC). In detail, firstly, we propose the PCA-ARMA prediction model to predict climate change in the next 25 years, including two steps: 1) generation of new components for data reduction by PCA using the past 75 years' data, and 2) prediction based on step 1 by ARMA for next 25 years. Then, we establish another model to find out the relation between climate change and abnormal weathers, e.g., the extreme cold weather, mainly by PCC. The relevant data are collected, and by these two models, we get the corresponding results, which show that our prediction fits well and the abnormal weather is strongly connected with the climate change.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126233181","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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