Pub Date : 2022-11-04DOI: 10.1109/ISPCE-ASIA57917.2022.9970805
Bin Song, Weiyang Chen, Tian Chen, Xinyu Zhou, Bingyi Liu
Vehicle path planning problems have been studied for decades. The existing path planning methods are suitable for simple objectives. However, for complex tasks such as planning paths for vehicles considering the effects of pedestrians, traffic lights, etc., it is difficult to design a reasonable cost function for the deterministic algorithm or a reasonable heuristic function for the heuristic algorithm. In this paper, we proposes a path planning model based on traffic light status and traffic condition awareness. When a vehicle arrives at a new road section, it senses the traffic light status, distribution and vehicle positions in the road network on each road section through V2V and V2I communication, and based on this information, we use an A2C-based deep reinforcement learning method to dynamically plan the shortest path for the vehicle in real time. Experiments show that the proposed method works effectively in terms of saving on driving time and waiting time to reach any destinations, compared to the existing solutions.
{"title":"Path Planning in Urban Environment Based on Traffic Condition Perception and Traffic Light Status","authors":"Bin Song, Weiyang Chen, Tian Chen, Xinyu Zhou, Bingyi Liu","doi":"10.1109/ISPCE-ASIA57917.2022.9970805","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970805","url":null,"abstract":"Vehicle path planning problems have been studied for decades. The existing path planning methods are suitable for simple objectives. However, for complex tasks such as planning paths for vehicles considering the effects of pedestrians, traffic lights, etc., it is difficult to design a reasonable cost function for the deterministic algorithm or a reasonable heuristic function for the heuristic algorithm. In this paper, we proposes a path planning model based on traffic light status and traffic condition awareness. When a vehicle arrives at a new road section, it senses the traffic light status, distribution and vehicle positions in the road network on each road section through V2V and V2I communication, and based on this information, we use an A2C-based deep reinforcement learning method to dynamically plan the shortest path for the vehicle in real time. Experiments show that the proposed method works effectively in terms of saving on driving time and waiting time to reach any destinations, compared to the existing solutions.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"54 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":"123320761","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-ASIA57917.2022.9970874
Hongyu Wang, Cuili Yang
In wastewater treatment process (WWTP), which has nonlinear and dynamic characteristics, it is difficult to realize the tracking control of dissolved oxygen (DO). To solve this problem, the online double heuristic programming (DHP) controller based on leaky echo state network (LESN) is proposed, the controller is named as DHP-LESN for short. Firstly, three Leaky ESNs are used in DHP to produce the control strategy, the system state and the derivatives of evaluation function, respectively. Then, the online gradient algorithm is used to update the output weights of three LESNs. Finally, the performance of the proposed DHP-LESN controller is tested and evaluated on Benchmark Simulation Model 1 (BSM1). The simulation results show that the proposed DHP-LESN controller can achieve better control nerformance than PID controller.
{"title":"Design and Application of DHP Controller Based on Online Leaky Echo State Network","authors":"Hongyu Wang, Cuili Yang","doi":"10.1109/ISPCE-ASIA57917.2022.9970874","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970874","url":null,"abstract":"In wastewater treatment process (WWTP), which has nonlinear and dynamic characteristics, it is difficult to realize the tracking control of dissolved oxygen (DO). To solve this problem, the online double heuristic programming (DHP) controller based on leaky echo state network (LESN) is proposed, the controller is named as DHP-LESN for short. Firstly, three Leaky ESNs are used in DHP to produce the control strategy, the system state and the derivatives of evaluation function, respectively. Then, the online gradient algorithm is used to update the output weights of three LESNs. Finally, the performance of the proposed DHP-LESN controller is tested and evaluated on Benchmark Simulation Model 1 (BSM1). The simulation results show that the proposed DHP-LESN controller can achieve better control nerformance than PID controller.","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":"123412467","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-ASIA57917.2022.9971062
Shu Wu, Shiyi Deng, Jingjing Cao
The three-dimensional stacking problem (3D-SP) is a challenging task in cold chain warehouse. Different from common three-dimensional packing problem, 3D-SP problem is more operable and can be stacked from all directions of the pallet. Based on this characteristic, we construct our model by considering the utilization rate of pallet space and the stability criterion of goods together as objective function. Further, four constraints are designed, which are placement direction, pallet space and no overlapping. According to the characteristics of the problem, a new improved genetic algorithm is proposed. In specific, the order of goods placement is regarded as individual, and with the consideration of order feature, we designed a more reasonable crossover and mutation operator. Compared with traditional greedy and genetic algorithm, our algorithm outperforms them and proved to be effective on 3D-SP problem.
{"title":"A Three-dimension Stacking Model with Modified Genetic Algorithm","authors":"Shu Wu, Shiyi Deng, Jingjing Cao","doi":"10.1109/ISPCE-ASIA57917.2022.9971062","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971062","url":null,"abstract":"The three-dimensional stacking problem (3D-SP) is a challenging task in cold chain warehouse. Different from common three-dimensional packing problem, 3D-SP problem is more operable and can be stacked from all directions of the pallet. Based on this characteristic, we construct our model by considering the utilization rate of pallet space and the stability criterion of goods together as objective function. Further, four constraints are designed, which are placement direction, pallet space and no overlapping. According to the characteristics of the problem, a new improved genetic algorithm is proposed. In specific, the order of goods placement is regarded as individual, and with the consideration of order feature, we designed a more reasonable crossover and mutation operator. Compared with traditional greedy and genetic algorithm, our algorithm outperforms them and proved to be effective on 3D-SP problem.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"15 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":"129115899","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-ASIA57917.2022.9970870
Yang Liu, Jianghong Ma, Guangqian Zhu, Kang Liu
This paper presents a novel pico-watt dual-output voltage reference for implantable medical devices (IMDs). In IMDs, excellent capabilities to reject the noise from power source and work with low power consumption and small active area are critical for voltage references. In the proposed design, a dual-output reference voltage is generated through two sets of 2-transistor (2-T) structure and a shared 4-bit trimming circuit to reduce the effects of process variations. At a typical corner, the proposed circuit generates two reference voltages Vref1 and Vref2 of about 88mV and 228mV and the voltage difference is 0.756mV and 6.546mV respectively from 0 °C to 120 °C, The noise rejection ratio greater than 35dB achieved in the simulation shows that Vref2 has a strong ability to suppress the noise of Vref1. Therefore, two noise-isolated reference voltages are generated, providing accurate and interference-free reference voltages for noisy and noiseless functional circuits. Furthermore, the power consumption is only 8.52 pW at room temperature and the active area is only 0.0019 mm2.
{"title":"A 8pW Noise Interference-Free Dual-Output Voltage Reference for Implantable Medical Devices","authors":"Yang Liu, Jianghong Ma, Guangqian Zhu, Kang Liu","doi":"10.1109/ISPCE-ASIA57917.2022.9970870","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970870","url":null,"abstract":"This paper presents a novel pico-watt dual-output voltage reference for implantable medical devices (IMDs). In IMDs, excellent capabilities to reject the noise from power source and work with low power consumption and small active area are critical for voltage references. In the proposed design, a dual-output reference voltage is generated through two sets of 2-transistor (2-T) structure and a shared 4-bit trimming circuit to reduce the effects of process variations. At a typical corner, the proposed circuit generates two reference voltages Vref1 and Vref2 of about 88mV and 228mV and the voltage difference is 0.756mV and 6.546mV respectively from 0 °C to 120 °C, The noise rejection ratio greater than 35dB achieved in the simulation shows that Vref2 has a strong ability to suppress the noise of Vref1. Therefore, two noise-isolated reference voltages are generated, providing accurate and interference-free reference voltages for noisy and noiseless functional circuits. Furthermore, the power consumption is only 8.52 pW at room temperature and the active area is only 0.0019 mm2.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"30 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":"114696846","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-ASIA57917.2022.9970914
Bin Xu, Yu Rong, Mingde Zhao
In autopilot field, 3D object detection is typically done with a complimentary pair of sensors: RGB cameras and LIDARs, either alone or in tandem. Cameras provide rich information in color and texture, while LIDARs focus on geometric and relative distance information. However, the challenge of 3D object detection lies in the difficulty of effectively fusing the 2D camera images with the 3D LIDAR point cloud. In this paper, we propose a two-stage cross-modal fusion panoramic driving perception network for 3D object detection, drivable area segmentation and lane segmentation tasks in parallel and in real time, based on the Carla autopilot dataset. On the one hand, this detector uses a pre-trained semantic segmentation model to decorate the point cloud and complete the drivable area segmentation and lane line segmentation tasks, and then performs the 3D target detection task on the BEV-encoded point cloud. On the other hand, thanks to the novelty data enhancement algorithms and enhanced training strategies designed in this paper, they significantly improve the robustness of the detector. Our detector outperforms existing mainstream 3D object detectors based on pure LIDAR sensors when it comes to detecting tiny targets like pedestrians.
{"title":"3D Object Detection for Point Cloud in Virtual Driving Environment","authors":"Bin Xu, Yu Rong, Mingde Zhao","doi":"10.1109/ISPCE-ASIA57917.2022.9970914","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970914","url":null,"abstract":"In autopilot field, 3D object detection is typically done with a complimentary pair of sensors: RGB cameras and LIDARs, either alone or in tandem. Cameras provide rich information in color and texture, while LIDARs focus on geometric and relative distance information. However, the challenge of 3D object detection lies in the difficulty of effectively fusing the 2D camera images with the 3D LIDAR point cloud. In this paper, we propose a two-stage cross-modal fusion panoramic driving perception network for 3D object detection, drivable area segmentation and lane segmentation tasks in parallel and in real time, based on the Carla autopilot dataset. On the one hand, this detector uses a pre-trained semantic segmentation model to decorate the point cloud and complete the drivable area segmentation and lane line segmentation tasks, and then performs the 3D target detection task on the BEV-encoded point cloud. On the other hand, thanks to the novelty data enhancement algorithms and enhanced training strategies designed in this paper, they significantly improve the robustness of the detector. Our detector outperforms existing mainstream 3D object detectors based on pure LIDAR sensors when it comes to detecting tiny targets like pedestrians.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"6 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":"126426205","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-ASIA57917.2022.9970906
Cuilian Huang, B. Ling, Xiaoyu Ding
Diabetes is a chronic metabolic disease. Due to insufficient insulin secretion to control blood glucose or the inability of the body to effectively use insulin, the blood glucose of patients will be higher than the normal value, resulting in various complications, which will seriously affect the health of patients. Real-time monitoring of blood glucose levels is crucial for early screening of high incidence of diabetes, as well as for diagnosis and treatment of patients with diabetes. Is proposed in this paper in the near-infrared (NIR) the application of noninvasive blood glucose level prediction, analyses the statistical characteristics and the relationship between the filter, proposed the concept of some new characteristics of filter, the filter is applied to the analysis of near infrared non-invasive blood glucose estimates, experimental results show that the new features in the machine learning model can improve the effect of the model.
{"title":"Relationship between statistics and filters in noninvasive blood glucose estimation analysis","authors":"Cuilian Huang, B. Ling, Xiaoyu Ding","doi":"10.1109/ISPCE-ASIA57917.2022.9970906","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970906","url":null,"abstract":"Diabetes is a chronic metabolic disease. Due to insufficient insulin secretion to control blood glucose or the inability of the body to effectively use insulin, the blood glucose of patients will be higher than the normal value, resulting in various complications, which will seriously affect the health of patients. Real-time monitoring of blood glucose levels is crucial for early screening of high incidence of diabetes, as well as for diagnosis and treatment of patients with diabetes. Is proposed in this paper in the near-infrared (NIR) the application of noninvasive blood glucose level prediction, analyses the statistical characteristics and the relationship between the filter, proposed the concept of some new characteristics of filter, the filter is applied to the analysis of near infrared non-invasive blood glucose estimates, experimental results show that the new features in the machine learning model can improve the effect of the model.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"91 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":"115854397","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-ASIA57917.2022.9970806
Danlei Wang, Cuili Yang, Yilong Liang
Dynamic multi-objective problems (DMOPs) have aroused extensive attention in recent years. Prediction-based methods have been proven to be effective. However, most existing methods assume the linear relationships between historical solutions. For real-life systems, ignoring the complex nonlinear relationships between historical environments may result in low prediction accuracy. To solve this problem, the echo state network (ESN) based prediction approach is proposed for DMOPs. First, the reservoir of ESN is used to express the input dynamics of the historical solutions to explore the linear or nonlinear relationships among historical solutions. Then, a fractal interpolation technique (FIT) is introduced to enrich the training data while preserving the original time series features as much as possible. The final experimental results show that the designed algorithm can solve the dynamic multi-objective optimization problems effectively.
{"title":"Dynamic Multiobjective Optimization Aided by ESN-based Prediction Approach","authors":"Danlei Wang, Cuili Yang, Yilong Liang","doi":"10.1109/ISPCE-ASIA57917.2022.9970806","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970806","url":null,"abstract":"Dynamic multi-objective problems (DMOPs) have aroused extensive attention in recent years. Prediction-based methods have been proven to be effective. However, most existing methods assume the linear relationships between historical solutions. For real-life systems, ignoring the complex nonlinear relationships between historical environments may result in low prediction accuracy. To solve this problem, the echo state network (ESN) based prediction approach is proposed for DMOPs. First, the reservoir of ESN is used to express the input dynamics of the historical solutions to explore the linear or nonlinear relationships among historical solutions. Then, a fractal interpolation technique (FIT) is introduced to enrich the training data while preserving the original time series features as much as possible. The final experimental results show that the designed algorithm can solve the dynamic multi-objective optimization problems effectively.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"32 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":"132196533","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-ASIA57917.2022.9970794
Hu Min, Fabing Lin, K. Wu, Junhui Lu, Z. Hou, Choujun Zhan
Global demand for electricity is increasing dramatically, because of population and electrical commodities growth. Therefore, accurate forecasting of electricity consumption is of great significance for formulating energy plans and ensuring the safe operation of power systems. However, due to the non-stationarity and non-linearity of electricity consumption time series, traditional forecasting methods can not capture the dynamic changes of load curves effectively. To solve this problem, we propose a novel Broad Learning System (BLS) based on Savitzky-Golay (SG) and Variational Mode Decomposition (VMD) for short-term load forecasting. First, we apply SG filter to eliminate the non-stationarity of the data. Then, VMD is used to decompose time series according to time frequency characteristics and extract the non-linear characteristics in the series. Finally, since BLS has a fast training process due to its single-layer network structure, we combine the developed filtering and decomposition algorithm with BLS for electricity forecasting. The study establishes empirical experiments with hourly electricity consumption data from the Los Angeles area. Experimental results show our framework achieves promising results and outperforms the state-of-the-art approaches on extensive public datasets.
{"title":"Broad learning system based on Savitzky—Golay filter and variational mode decomposition for short-term load forecasting","authors":"Hu Min, Fabing Lin, K. Wu, Junhui Lu, Z. Hou, Choujun Zhan","doi":"10.1109/ISPCE-ASIA57917.2022.9970794","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970794","url":null,"abstract":"Global demand for electricity is increasing dramatically, because of population and electrical commodities growth. Therefore, accurate forecasting of electricity consumption is of great significance for formulating energy plans and ensuring the safe operation of power systems. However, due to the non-stationarity and non-linearity of electricity consumption time series, traditional forecasting methods can not capture the dynamic changes of load curves effectively. To solve this problem, we propose a novel Broad Learning System (BLS) based on Savitzky-Golay (SG) and Variational Mode Decomposition (VMD) for short-term load forecasting. First, we apply SG filter to eliminate the non-stationarity of the data. Then, VMD is used to decompose time series according to time frequency characteristics and extract the non-linear characteristics in the series. Finally, since BLS has a fast training process due to its single-layer network structure, we combine the developed filtering and decomposition algorithm with BLS for electricity forecasting. The study establishes empirical experiments with hourly electricity consumption data from the Los Angeles area. Experimental results show our framework achieves promising results and outperforms the state-of-the-art approaches on extensive public datasets.","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":"131330041","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-ASIA57917.2022.9971117
Yilong Liang, Cuili Yang, Danlei Wang
Echo state network (ESN) is a kind of recurrent neural network without involving gradient problem. However, the reservoir of ESN often contains hundreds of neurons, whose corresponding high-dimensional state matrix may result in ill-conditioned solution problem. To solve it, the condition number-based evolving ESN (CNEESN) is proposed, whose sub-reservoir is generated by condition number analysis and differential evolution algorithm (DE). Firstly, the influence of condition number on output weight matrix is analyzed. Secondly, the randomly generated singular values are optimized by condition number and DE based optimize strategy. Finally, simulation result on a benchmark dataset has shown the superiority of the proposed CNEESN.
{"title":"Condition Number-based Evolving ESN","authors":"Yilong Liang, Cuili Yang, Danlei Wang","doi":"10.1109/ISPCE-ASIA57917.2022.9971117","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9971117","url":null,"abstract":"Echo state network (ESN) is a kind of recurrent neural network without involving gradient problem. However, the reservoir of ESN often contains hundreds of neurons, whose corresponding high-dimensional state matrix may result in ill-conditioned solution problem. To solve it, the condition number-based evolving ESN (CNEESN) is proposed, whose sub-reservoir is generated by condition number analysis and differential evolution algorithm (DE). Firstly, the influence of condition number on output weight matrix is analyzed. Secondly, the randomly generated singular values are optimized by condition number and DE based optimize strategy. Finally, simulation result on a benchmark dataset has shown the superiority of the proposed CNEESN.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"77 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":"115283029","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-ASIA57917.2022.9970814
Xiaoyu Ding, B. Ling, Cuilian Huang
In the past few decades, due to the increasing emphasis on health, blood glucose, a healthy reference value, has received more and more attention. Traditional invasive blood glucose testing methods require pricking a finger to take a drop of blood, and measuring blood glucose levels based on how the device reacts with the blood. Due to various shortcomings of traditional methods, and the semi-invasive or minimally invasive blood glucose monitoring systems that have been marketed in many countries and regions have high costs and some usage limitations, a new type of easy-to-use non-invasive blood glucose detection and prediction system is rapidly developing. This paper introduces a wearable non-invasive blood glucose detection device using near-infrared technology and its data processing technology, which includes extracting features from the obtained signals and using machine learning methods for blood glucose level prediction, and novel use of the solution The optimization problem of different norm values is used to obtain new statistical features to further improve the accuracy of non-invasive blood glucose prediction.
{"title":"Non-invasive Blood Glucose Estimation Using Statistical Features Defined via Convex Combination of One Norm and Infinity Norm Optimization Problems","authors":"Xiaoyu Ding, B. Ling, Cuilian Huang","doi":"10.1109/ISPCE-ASIA57917.2022.9970814","DOIUrl":"https://doi.org/10.1109/ISPCE-ASIA57917.2022.9970814","url":null,"abstract":"In the past few decades, due to the increasing emphasis on health, blood glucose, a healthy reference value, has received more and more attention. Traditional invasive blood glucose testing methods require pricking a finger to take a drop of blood, and measuring blood glucose levels based on how the device reacts with the blood. Due to various shortcomings of traditional methods, and the semi-invasive or minimally invasive blood glucose monitoring systems that have been marketed in many countries and regions have high costs and some usage limitations, a new type of easy-to-use non-invasive blood glucose detection and prediction system is rapidly developing. This paper introduces a wearable non-invasive blood glucose detection device using near-infrared technology and its data processing technology, which includes extracting features from the obtained signals and using machine learning methods for blood glucose level prediction, and novel use of the solution The optimization problem of different norm values is used to obtain new statistical features to further improve the accuracy of non-invasive blood glucose prediction.","PeriodicalId":197173,"journal":{"name":"2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)","volume":"98 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":"128000992","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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