Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970998
Guo Luo, Xinying Xie, Xuejiao Peng, Angbo Xie, Shun Lu, Hu Min
In this paper, a new method, combined with stationary wavelet transform and Gaussian Radial Basis Function Neural Networks (GRBFNN), is proposed for solving the problem of human gait modelling. Firstly, the hardware system, consisting with MPU6050 sensor, wireless transform module, micro control unit and computer, is designed for collecting the gait signal. Secondly, stationary wavelet transform is applied for decomposing the gait signal with 5 scales. In order to remove the high frequency noise and baseline drift, the coefficients of high frequency and low frequency are set as zero. Thirdly, after wavelet denoising, setting a large enough space to cover the gait signal and establishing lattice points with equal intervals in this space, we take gait signal as input and use lattice points as mapping center in GRBFNN design. Fourthly, the identification equation of continuous dynamical system is rewritten into discrete one, and GRBFNN is used for modelling the dynamical function of gait signal. In order to ensure the stability of iteration, the chosen of gain parameter is proven by the Z transform. Finally, comparing with wavelet neural networks(WNN), the result of test in practice demonstrates the superiority of the proposed method for solving the problem of human gait modelling.
{"title":"Human Gait Modelling via Stationary Wavelet Transform and Radial Basis Function Neural Networks","authors":"Guo Luo, Xinying Xie, Xuejiao Peng, Angbo Xie, Shun Lu, Hu Min","doi":"10.1109/ISPCE-ASIA57917.2022.9970998","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970998","url":null,"abstract":"In this paper, a new method, combined with stationary wavelet transform and Gaussian Radial Basis Function Neural Networks (GRBFNN), is proposed for solving the problem of human gait modelling. Firstly, the hardware system, consisting with MPU6050 sensor, wireless transform module, micro control unit and computer, is designed for collecting the gait signal. Secondly, stationary wavelet transform is applied for decomposing the gait signal with 5 scales. In order to remove the high frequency noise and baseline drift, the coefficients of high frequency and low frequency are set as zero. Thirdly, after wavelet denoising, setting a large enough space to cover the gait signal and establishing lattice points with equal intervals in this space, we take gait signal as input and use lattice points as mapping center in GRBFNN design. Fourthly, the identification equation of continuous dynamical system is rewritten into discrete one, and GRBFNN is used for modelling the dynamical function of gait signal. In order to ensure the stability of iteration, the chosen of gain parameter is proven by the Z transform. Finally, comparing with wavelet neural networks(WNN), the result of test in practice demonstrates the superiority of the proposed method for solving the problem of human gait modelling.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130622602","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 : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9971012
Gou Luo, Shun Lu, Xinying Xie, Xuejiao Peng, Angbo Xie, Xinyan Mo, Xuan Li, Lijuan Chen, Xinru Lin
In order to identify the Electrocardiograph (ECG) dynamical system more accurately, a system identification method based on multi-scale wavelet neural networks is proposed in this paper. Firstly, the stationary wavelet transform is used to remove the baseline drift and high frequency noise of ECG signal; Secondly, wavelet theory, radial basis function neural networks and grid points are used to design the multi-scale wavelet neural networks architecture; Finally, in order to facilitate the iteration of discrete data, the discrete difference equation is used to replace the continuous differential equation in the system identification algorithm, and the value range of gain parameters is proved by Z-transform. In this paper, the effectiveness of this method is verified by using three-dimensional ECG signals from PTB database, which also opens up a new research method for the identification of ECG dynamical system.
{"title":"ECG Dynamical System Identification Based on Multi-scale Wavelet Neural Networks","authors":"Gou Luo, Shun Lu, Xinying Xie, Xuejiao Peng, Angbo Xie, Xinyan Mo, Xuan Li, Lijuan Chen, Xinru Lin","doi":"10.1109/ISPCE-ASIA57917.2022.9971012","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971012","url":null,"abstract":"In order to identify the Electrocardiograph (ECG) dynamical system more accurately, a system identification method based on multi-scale wavelet neural networks is proposed in this paper. Firstly, the stationary wavelet transform is used to remove the baseline drift and high frequency noise of ECG signal; Secondly, wavelet theory, radial basis function neural networks and grid points are used to design the multi-scale wavelet neural networks architecture; Finally, in order to facilitate the iteration of discrete data, the discrete difference equation is used to replace the continuous differential equation in the system identification algorithm, and the value range of gain parameters is proved by Z-transform. In this paper, the effectiveness of this method is verified by using three-dimensional ECG signals from PTB database, which also opens up a new research method for the identification of ECG dynamical system.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114333399","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 : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970775
S. Mak, S. Au, W. F. Tang, C. H. Li, W. H. Chiu, C. C. Lee, M. Y. Wu
The birth rate of children declined in the developed countries and the number of pets increased at the same time. The market size of pet product increased significantly. The customer is willing to pay more to buy safe and reliable products to their pets. This paper firstly reviews the reason why the birth rate of children declined in the developed countries. Secondly, the market trend of pet food products is discussed. Thirdly, the available international standards for pet foods are reviewed and discussed. Then the major hazardous substances for dogs and cats are listed with the adverse effects. The available analytical pet foods services is summarized and the limitations are discussed. Finally, a research project is proposed.
{"title":"A Critical Review on Safety of Pet Food Products","authors":"S. Mak, S. Au, W. F. Tang, C. H. Li, W. H. Chiu, C. C. Lee, M. Y. Wu","doi":"10.1109/ISPCE-ASIA57917.2022.9970775","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970775","url":null,"abstract":"The birth rate of children declined in the developed countries and the number of pets increased at the same time. The market size of pet product increased significantly. The customer is willing to pay more to buy safe and reliable products to their pets. This paper firstly reviews the reason why the birth rate of children declined in the developed countries. Secondly, the market trend of pet food products is discussed. Thirdly, the available international standards for pet foods are reviewed and discussed. Then the major hazardous substances for dogs and cats are listed with the adverse effects. The available analytical pet foods services is summarized and the limitations are discussed. Finally, a research project is proposed.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117119534","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 : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970978
Yo-Che Lee, Yang Wei, Hao Wang, Hoi-Ting Au, Yucheng Liu, K. Tsang
The concept of Internet of Things (ioT) has incubated a whole generation of smart applications to resolve problems in society. Despite cloud-based IoT systems inheriting the robustness and scalability of cloud computing, its high latency limits the implementation of time-sensitive applications. To encounter this, Edge-computing-based IoT systems make computing services closer to users which entails lower latencies. New technologies bring new attacks, and Distributed Denial of Service (DDoS) attack has been regarded as one of the major threats to Edge Internet of Things (EIoT) systems. Previous works often focus on the state-of-the-art and the defense mechanism of edge computing. However, there lacks a general standardized method for the security impact analysis, dedicated to EIoT. This paper proposes the DDoS Impact Analysis Index, or DIADex, which is compatible with the IEEE P2668 standard. From the aspects of performance and resources, this method quantifies the impact of DDoS attacks on EIoT systems with a scoring system, which can be used for evaluation in future research and penetration test on EIoT.
{"title":"DDoS Impact Analysis Index for Edge Internet of Things System Evaluation","authors":"Yo-Che Lee, Yang Wei, Hao Wang, Hoi-Ting Au, Yucheng Liu, K. Tsang","doi":"10.1109/ISPCE-ASIA57917.2022.9970978","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970978","url":null,"abstract":"The concept of Internet of Things (ioT) has incubated a whole generation of smart applications to resolve problems in society. Despite cloud-based IoT systems inheriting the robustness and scalability of cloud computing, its high latency limits the implementation of time-sensitive applications. To encounter this, Edge-computing-based IoT systems make computing services closer to users which entails lower latencies. New technologies bring new attacks, and Distributed Denial of Service (DDoS) attack has been regarded as one of the major threats to Edge Internet of Things (EIoT) systems. Previous works often focus on the state-of-the-art and the defense mechanism of edge computing. However, there lacks a general standardized method for the security impact analysis, dedicated to EIoT. This paper proposes the DDoS Impact Analysis Index, or DIADex, which is compatible with the IEEE P2668 standard. From the aspects of performance and resources, this method quantifies the impact of DDoS attacks on EIoT systems with a scoring system, which can be used for evaluation in future research and penetration test on EIoT.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115748692","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 : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970859
Yunqi Wang, B. Ling
This paper proposes a method in order to assess the level of relaxation of the subjects based on the photoplethysmograms (PPGs). Here, the PPGs are classified into five relaxation levels. In particular, the proposed system composes of a PPG acquisition device, a five class classification algorithm and a cloud system. The PPG acquisition device consists of a photo sensor operating at 880nm. For the algorithm, first three measurements are taken each time at the finger tip. Second, the PPGs are denoised. Third, the feature extraction is performed. More precisely, the heart rate (HR) and the heart rate variability (HRV) are extracted as the features. Fourth, the features are smoothed. Finally, the classification is performed via the random forest. For the cloud system, the PPGs are transmitted to the cloud system via the Wifi and the above processing is performed in the cloud system. Since our proposed system is non-invasive and wearable, it can provide the guideline on the relaxation level to the general public.
{"title":"Relaxation Assessment Based on Heart Rate Variability and Heart Rate Using Photoplethsmograms","authors":"Yunqi Wang, B. Ling","doi":"10.1109/ISPCE-ASIA57917.2022.9970859","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970859","url":null,"abstract":"This paper proposes a method in order to assess the level of relaxation of the subjects based on the photoplethysmograms (PPGs). Here, the PPGs are classified into five relaxation levels. In particular, the proposed system composes of a PPG acquisition device, a five class classification algorithm and a cloud system. The PPG acquisition device consists of a photo sensor operating at 880nm. For the algorithm, first three measurements are taken each time at the finger tip. Second, the PPGs are denoised. Third, the feature extraction is performed. More precisely, the heart rate (HR) and the heart rate variability (HRV) are extracted as the features. Fourth, the features are smoothed. Finally, the classification is performed via the random forest. For the cloud system, the PPGs are transmitted to the cloud system via the Wifi and the above processing is performed in the cloud system. Since our proposed system is non-invasive and wearable, it can provide the guideline on the relaxation level to the general public.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127157843","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 : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970786
Mingzhu Yu, Zhishan Yu, Bo Jin, Junfeng Wu
This paper focuses on a hinterland empty container transportation system involving a road-rail network. We formulate an integer programming model to characterize the empty container allocation and transshipment problem in the road-rail transportation network. The complexity of the studied problem comes from the diversity of inland container transportation modes and the flexibility of empty container allocation between different supply-demand pairs. An effective and efficient Greedy-SPFA (Shortest Path Faster Algorithm) method is proposed to solve this problem through transforming it into a minimum cost flow problem. Computational experiments show that the applied algorithm outperforms the established formulation. And some management inspirations are proposed through sensitivity analysis.
{"title":"Empty Container Allocation and Transshipment in Road-Rail Transportation Network","authors":"Mingzhu Yu, Zhishan Yu, Bo Jin, Junfeng Wu","doi":"10.1109/ISPCE-ASIA57917.2022.9970786","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970786","url":null,"abstract":"This paper focuses on a hinterland empty container transportation system involving a road-rail network. We formulate an integer programming model to characterize the empty container allocation and transshipment problem in the road-rail transportation network. The complexity of the studied problem comes from the diversity of inland container transportation modes and the flexibility of empty container allocation between different supply-demand pairs. An effective and efficient Greedy-SPFA (Shortest Path Faster Algorithm) method is proposed to solve this problem through transforming it into a minimum cost flow problem. Computational experiments show that the applied algorithm outperforms the established formulation. And some management inspirations are proposed through sensitivity analysis.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131418431","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 fast reconstruction method of temperature field based on principal component analysis (PCA) and convolutional autoencoder is proposed in this paper. The two-dimensional temperature field can be quickly reconstructed by inputting the small amounts of sensor data. Principal component analysis is first used to extract key features from high-dimensional prior dataset, and the extracted results are combined with the sensor measurement points information according to the coefficient optimization method to achieve the approximate reconstruction of the temperature field. Then, the reconstruction results are inputted into the convolutional autoencoder model for iterative learning to further reduce the reconstruction error and achieve accurate reconstruction of the temperature field. The effectiveness proposed method has been verified in the boiler combustion simulation experiment, and the experimental results show that the proposed method can reconstruct the two-dimensional temperature field quickly and accurately, which is of great significance to the research of some combustion systems.
{"title":"A Fast Reconstruction Method for Temperature Field Based on Principal Component Analysis and Convolutional Autoencoder","authors":"Fuqiang Sun, Anzhen Huang, Zhangang Wu, Weijie Huang, Menghua Zhang","doi":"10.1109/ISPCE-ASIA57917.2022.9971011","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971011","url":null,"abstract":"A fast reconstruction method of temperature field based on principal component analysis (PCA) and convolutional autoencoder is proposed in this paper. The two-dimensional temperature field can be quickly reconstructed by inputting the small amounts of sensor data. Principal component analysis is first used to extract key features from high-dimensional prior dataset, and the extracted results are combined with the sensor measurement points information according to the coefficient optimization method to achieve the approximate reconstruction of the temperature field. Then, the reconstruction results are inputted into the convolutional autoencoder model for iterative learning to further reduce the reconstruction error and achieve accurate reconstruction of the temperature field. The effectiveness proposed method has been verified in the boiler combustion simulation experiment, and the experimental results show that the proposed method can reconstruct the two-dimensional temperature field quickly and accurately, which is of great significance to the research of some combustion systems.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127275703","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 : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970885
Sheng Yi, Hao Zhang, Lu Jiang, Yangkai Zhou, Ke Xiao, Kai Liu
The monitoring of vehicle flow is critical to enable a variety of intelligent transportation systems (ITSs). Traditional vehicle flow monitoring solutions are mainly based on roadside cameras, which may suffer serious performance deterioration in dark environments. In view of this, this paper proposes a Lidar-based vehicle flow monitoring system, which consists three parts: target detection module, vehicle flow counting module and vehicle counting visualization module. Specifically, the target detection module is built based on self-training data and the YOLOv4 network. Vehicle information is collected and preprocessed to speed up the target detection and enhance the accuracy. The vehicles and their positions are then obtained by performing inference with the trained weights for Lidar-based vehicle detection. On this basis, the vehicle counting module applies a multi-object tracking technique to monitor the vehicles which are nearby the detected one. Additionally, the Hungarian algorithm is used to match the surrounding vehicles. In vehicle counting visualization module, we visualize the system output through OpenCv. Finally, we build the system prototype and evaluate the algorithm performance in realistic environments under different night-time traffic situations. The experimental results demonstrate the practicability and robustness of the proposed solutions.
{"title":"Towards Efficient and Robust Night-time Vehicle Flow Monitoring via Lidar-based Detection","authors":"Sheng Yi, Hao Zhang, Lu Jiang, Yangkai Zhou, Ke Xiao, Kai Liu","doi":"10.1109/ISPCE-ASIA57917.2022.9970885","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970885","url":null,"abstract":"The monitoring of vehicle flow is critical to enable a variety of intelligent transportation systems (ITSs). Traditional vehicle flow monitoring solutions are mainly based on roadside cameras, which may suffer serious performance deterioration in dark environments. In view of this, this paper proposes a Lidar-based vehicle flow monitoring system, which consists three parts: target detection module, vehicle flow counting module and vehicle counting visualization module. Specifically, the target detection module is built based on self-training data and the YOLOv4 network. Vehicle information is collected and preprocessed to speed up the target detection and enhance the accuracy. The vehicles and their positions are then obtained by performing inference with the trained weights for Lidar-based vehicle detection. On this basis, the vehicle counting module applies a multi-object tracking technique to monitor the vehicles which are nearby the detected one. Additionally, the Hungarian algorithm is used to match the surrounding vehicles. In vehicle counting visualization module, we visualize the system output through OpenCv. Finally, we build the system prototype and evaluate the algorithm performance in realistic environments under different night-time traffic situations. The experimental results demonstrate the practicability and robustness of the proposed solutions.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122043331","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 : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970823
Du Yin, Lingfeng Miao, Guanzhi Li, Choujun Zhan, Lulu Sun
Efficient and accurate short-term load forecasting (STLF) is significance in modern electricity markets. However, accurate short-term load forecasting is challenging due to the non-stationary power load patterns. In this work, we propose a short-term load forecasting framework based on maximal information coefficient (MIC), moving average filter (MAF) and sample convolution and interactive learning (SCINet), Firstly, MIC is used for feature selection. Secondly, the filtered input features are decomposed using MAF individually. Finally, the data are used in an advanced SCINet for short-term load forecasting. The performance of the proposed method is evaluated using datasets from two different regions of the US electricity market. In addition, we compare the prediction results with support vector regression machines (SVR), long short-term memory networks (LSTM), temporal convolutional networks (TCN), light gradient boosting machine (LightGBM), artificial neural network (ANN), random forest (RF), and sample convolution and interaction networks (SCINet). The proposed model achieves accurate prediction results among all the machine learning models used in this paper.
{"title":"Moving Average-Based Performance Enhancement of Sample Convolution and Interactive Learning for Short-Term Load Forecasting","authors":"Du Yin, Lingfeng Miao, Guanzhi Li, Choujun Zhan, Lulu Sun","doi":"10.1109/ISPCE-ASIA57917.2022.9970823","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970823","url":null,"abstract":"Efficient and accurate short-term load forecasting (STLF) is significance in modern electricity markets. However, accurate short-term load forecasting is challenging due to the non-stationary power load patterns. In this work, we propose a short-term load forecasting framework based on maximal information coefficient (MIC), moving average filter (MAF) and sample convolution and interactive learning (SCINet), Firstly, MIC is used for feature selection. Secondly, the filtered input features are decomposed using MAF individually. Finally, the data are used in an advanced SCINet for short-term load forecasting. The performance of the proposed method is evaluated using datasets from two different regions of the US electricity market. In addition, we compare the prediction results with support vector regression machines (SVR), long short-term memory networks (LSTM), temporal convolutional networks (TCN), light gradient boosting machine (LightGBM), artificial neural network (ANN), random forest (RF), and sample convolution and interaction networks (SCINet). The proposed model achieves accurate prediction results among all the machine learning models used in this paper.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115014240","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}
Vehicular edge caching (VEC) is expected to support real-time intelligent transportation systems by providing low-latency data services. However, dynamic vehicular environment, such as time-varying data freshness and dynamic data preference, may result in low cache efficiency. Based on the above motivation, this paper designs a system model of freshness-aware VEC system. Accordingly, we formulate the problem of Vehicular Edge Cache Update (VECU) by exploiting the concept of AoI and data heterogeneity for evaluating data freshness, which aims at maximizing the edge cache benefit. On this basis, the Contextual Multi-Armed Bandit for Caching Update (CMAB-CU) algorithm is designed to determine cache update decision by online estimating reward of each arm based on a linear function of dynamic vehicular features and historical observations. Finally, we modeling a simulation model and conduct simulation results, which demonstrates the effectiveness of the proposed algorithm in various service scenarios.
{"title":"Contextual Multi-Armed Bandit Learning for Freshness-aware Cache Update in Vehicular Edge Networks","authors":"Yaorong Huang, Penglin Dai, Kangli Zhao, Huanlai Xing","doi":"10.1109/ISPCE-ASIA57917.2022.9970879","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970879","url":null,"abstract":"Vehicular edge caching (VEC) is expected to support real-time intelligent transportation systems by providing low-latency data services. However, dynamic vehicular environment, such as time-varying data freshness and dynamic data preference, may result in low cache efficiency. Based on the above motivation, this paper designs a system model of freshness-aware VEC system. Accordingly, we formulate the problem of Vehicular Edge Cache Update (VECU) by exploiting the concept of AoI and data heterogeneity for evaluating data freshness, which aims at maximizing the edge cache benefit. On this basis, the Contextual Multi-Armed Bandit for Caching Update (CMAB-CU) algorithm is designed to determine cache update decision by online estimating reward of each arm based on a linear function of dynamic vehicular features and historical observations. Finally, we modeling a simulation model and conduct simulation results, which demonstrates the effectiveness of the proposed algorithm in various service scenarios.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116987043","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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