Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00017
Junaidul Islam, Elvin Nur Furqon, Isack Farady, Chi-Wen Lung, Chih-Yang Lin
Magnetic Resonance Imaging (MRI) is currently one of the most promising tools for detecting Alzheimer’s disease (AD), as it allows for the analysis of brain regions affected by the disease, such as the hippocampus. However, the availability of labeled datasets for hippocampus regions in MRI images is limited, and manually annotating such images can be expensive and time-consuming task, particularly for large datasets. To overcome this challenge, we propose a deep learning approach that leverages object detection models to automatically identify the hippocampus region in MRI images. In our study, we employed various YOLO-based models to detect and classify the AD classes based on the hippocampus region in MRI images. We specifically selected the latest state-of-the-art YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv6, and YOLOv7. Our approach shows potential for improving the early detection of Alzheimer’s disease using deep learning and object detection and may be useful for developing automated diagnostic tools for clinical applications. We conducted experiments in two scenarios to validate our proposed idea: one-class detection and two-class detection. One-class detection detects a specific class based on the appearance of the hippocampus region, while two-class detection aims to detect and classify the AD level based on the hippocampus. Our preliminary results demonstrate that YOLO variants are viable for accurately detecting the hippocampus region in MRI images, with potential applications in hippocampus detection.
{"title":"Early Alzheimer’s Disease Detection Through YOLO-Based Detection of Hippocampus Region in MRI Images","authors":"Junaidul Islam, Elvin Nur Furqon, Isack Farady, Chi-Wen Lung, Chih-Yang Lin","doi":"10.1109/IS3C57901.2023.00017","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00017","url":null,"abstract":"Magnetic Resonance Imaging (MRI) is currently one of the most promising tools for detecting Alzheimer’s disease (AD), as it allows for the analysis of brain regions affected by the disease, such as the hippocampus. However, the availability of labeled datasets for hippocampus regions in MRI images is limited, and manually annotating such images can be expensive and time-consuming task, particularly for large datasets. To overcome this challenge, we propose a deep learning approach that leverages object detection models to automatically identify the hippocampus region in MRI images. In our study, we employed various YOLO-based models to detect and classify the AD classes based on the hippocampus region in MRI images. We specifically selected the latest state-of-the-art YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv6, and YOLOv7. Our approach shows potential for improving the early detection of Alzheimer’s disease using deep learning and object detection and may be useful for developing automated diagnostic tools for clinical applications. We conducted experiments in two scenarios to validate our proposed idea: one-class detection and two-class detection. One-class detection detects a specific class based on the appearance of the hippocampus region, while two-class detection aims to detect and classify the AD level based on the hippocampus. Our preliminary results demonstrate that YOLO variants are viable for accurately detecting the hippocampus region in MRI images, with potential applications in hippocampus detection.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130913016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00076
Wen-Zhuang Jiang, C. Liao, Y. Hsu
Recently, due to the environmental reasons, the renewable energy is getting attention. Therefore, the use of inverter-based resources (IBR), such as PV and wind turbines, has been increasing. However, the number of the traditional synchronous generators (SG) is decreasing, which results in lack of power system inertia. Therefore, the concept of virtual synchronous generator (VSG) is presented, which mimics the dynamic behavior of the traditional synchronous generator. Thus, VSG control has the characteristics of damping and inertia, which is suitable for the power system applications. In this paper, the concept of VSG control is discussed. Furthermore, the comparison of inverter using droop control and inverter using VSG control is given. The results show that VSG control has slower rate of change of frequency (RoCoF), which is more suitable for the control strategy of inverter-based resources in power systems.
{"title":"Application of Virtual Synchronous Generator in Power Systems","authors":"Wen-Zhuang Jiang, C. Liao, Y. Hsu","doi":"10.1109/IS3C57901.2023.00076","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00076","url":null,"abstract":"Recently, due to the environmental reasons, the renewable energy is getting attention. Therefore, the use of inverter-based resources (IBR), such as PV and wind turbines, has been increasing. However, the number of the traditional synchronous generators (SG) is decreasing, which results in lack of power system inertia. Therefore, the concept of virtual synchronous generator (VSG) is presented, which mimics the dynamic behavior of the traditional synchronous generator. Thus, VSG control has the characteristics of damping and inertia, which is suitable for the power system applications. In this paper, the concept of VSG control is discussed. Furthermore, the comparison of inverter using droop control and inverter using VSG control is given. The results show that VSG control has slower rate of change of frequency (RoCoF), which is more suitable for the control strategy of inverter-based resources in power systems.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125367288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00078
K. Hwu, Pei-Ching Tseng
This paper presents a new high-boost converter based on the dual boost inductor converter with the charge pumping capacitor. This structure is constructed by adding an additional voltage doubler circuit and a set of coils coupled together to the existing circuit to increase the voltage conversion ratio so the overall circuit size can be reduced and the leakage energy of the coupling inductor can be recovered.
{"title":"High Step-Up Converter","authors":"K. Hwu, Pei-Ching Tseng","doi":"10.1109/IS3C57901.2023.00078","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00078","url":null,"abstract":"This paper presents a new high-boost converter based on the dual boost inductor converter with the charge pumping capacitor. This structure is constructed by adding an additional voltage doubler circuit and a set of coils coupled together to the existing circuit to increase the voltage conversion ratio so the overall circuit size can be reduced and the leakage energy of the coupling inductor can be recovered.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123079487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00014
Jia-Ming Yeh, Garnett Chang, Jason P Lee, Wei-Yang Lin
Although there has been a lot of research on deep learning, most of them use GPU platform to run deep network models. However, it is less desirable to utilize GPU in real-world scenarios due its relatively high cost and high power consumption. In this paper, we propose a two-stage pipelined algorithm (TSPA) suitable for the FPGA platform to avoid the above-mentioned issues. We also combine OpenCV and GStreamer so that the FPGA platform can achieve real-time performance while maintaining satisfactory accuracy. We choose license plate recognition as an example to demonstrate the feasibility of our proposed approach. We have conducted experiments using the AOLP dataset and the self-collected videos. Our proposed method achieves promising results on these videos.
{"title":"A Two-Stage Pipelined Algorithm for Recognition Tasks: Using License Plate Recognition as an Example","authors":"Jia-Ming Yeh, Garnett Chang, Jason P Lee, Wei-Yang Lin","doi":"10.1109/IS3C57901.2023.00014","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00014","url":null,"abstract":"Although there has been a lot of research on deep learning, most of them use GPU platform to run deep network models. However, it is less desirable to utilize GPU in real-world scenarios due its relatively high cost and high power consumption. In this paper, we propose a two-stage pipelined algorithm (TSPA) suitable for the FPGA platform to avoid the above-mentioned issues. We also combine OpenCV and GStreamer so that the FPGA platform can achieve real-time performance while maintaining satisfactory accuracy. We choose license plate recognition as an example to demonstrate the feasibility of our proposed approach. We have conducted experiments using the AOLP dataset and the self-collected videos. Our proposed method achieves promising results on these videos.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122245228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00016
Irawati Nurmala Sari, Kei Masaoka, Jun’Nosuke Takarabe, Weiwei Du
Image completion has made impressive advancements based on deep learning approaches. However, even with advanced deep learning such as Generative Adversarial Networks (GAN), the restored area is not always optimal due to small-scale texture synthesis in high resolution and inferring missing information about image content from distant contexts, resulting in distorted lines and unnatural colors, especially in art painting completion with complicated structures and textures. Although several precious art paintings have been well-preserved by curators in museums, some frequent damages such as scratches, torn-out areas, and holes are still visible and require challenging physical repairs. Therefore, for practical refinement, some researchers convert them into high-resolution digital paintings to generate crisp brush strokes, textures, shapes, and tones by assuming similarities with the original physical ones. Based on these observations, we propose proceeding with a high-resolution art painting completion by applying a superior traditional method, named Multi-Region Laplacian Fusion. We attempt to recover irregular missing regions expected as the damages of ordinary paintings that often occur. To address high-resolution inpainting, we integrate two completions using the Laplacian pyramid and patch-based propagation. We then apply Alpha blending among both results to yield the fused reaction completion. Our experiments firmly validate the effectiveness of our proposed method to complete art paintings with random irregular missing regions.
{"title":"High-Resolution Art Painting Completion using Multi-Region Laplacian Fusion","authors":"Irawati Nurmala Sari, Kei Masaoka, Jun’Nosuke Takarabe, Weiwei Du","doi":"10.1109/IS3C57901.2023.00016","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00016","url":null,"abstract":"Image completion has made impressive advancements based on deep learning approaches. However, even with advanced deep learning such as Generative Adversarial Networks (GAN), the restored area is not always optimal due to small-scale texture synthesis in high resolution and inferring missing information about image content from distant contexts, resulting in distorted lines and unnatural colors, especially in art painting completion with complicated structures and textures. Although several precious art paintings have been well-preserved by curators in museums, some frequent damages such as scratches, torn-out areas, and holes are still visible and require challenging physical repairs. Therefore, for practical refinement, some researchers convert them into high-resolution digital paintings to generate crisp brush strokes, textures, shapes, and tones by assuming similarities with the original physical ones. Based on these observations, we propose proceeding with a high-resolution art painting completion by applying a superior traditional method, named Multi-Region Laplacian Fusion. We attempt to recover irregular missing regions expected as the damages of ordinary paintings that often occur. To address high-resolution inpainting, we integrate two completions using the Laplacian pyramid and patch-based propagation. We then apply Alpha blending among both results to yield the fused reaction completion. Our experiments firmly validate the effectiveness of our proposed method to complete art paintings with random irregular missing regions.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128126429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/is3c57901.2023.00100
I. Wahyono, Shih-Chung Chen
Epileptic seizures can range from short periods to violent shaking over long periods. Epilepsy is a disease that tends to occur repeatedly and cannot be cured. However, the occurrence of epileptic seizures can be controlled through medication. This study tried to detect epilepsy features via electroencephalogram (EEG) data analysis. The EEG data is divided into several windows using segmentation or decomposition. EEG Data will be extracted from each window using a modified Weighted Permutation Entropy (WPE) to produce one feature per window where the number of features of each EEG data will equal the number of windows obtained in the EEG data recording process. This research used k-fold cross-validation by dividing the data into training and testing data, classified using a Support Vector Machine. The EEG database used in this study came from Temple University Hospital EEG (TUH EEG), obtained online with as much as 1500 data. This EEG database consists of epileptic seizures (set 1) and non-epileptic seizures (sets 2, 3, 4, 5), each with 300 data. Based on testing, using this method produces an average accuracy of 81.29%.
{"title":"Optimizing Weighted Permutation in Support Vector Machine for the Detection of Epilepsy via EEG Data Analysis","authors":"I. Wahyono, Shih-Chung Chen","doi":"10.1109/is3c57901.2023.00100","DOIUrl":"https://doi.org/10.1109/is3c57901.2023.00100","url":null,"abstract":"Epileptic seizures can range from short periods to violent shaking over long periods. Epilepsy is a disease that tends to occur repeatedly and cannot be cured. However, the occurrence of epileptic seizures can be controlled through medication. This study tried to detect epilepsy features via electroencephalogram (EEG) data analysis. The EEG data is divided into several windows using segmentation or decomposition. EEG Data will be extracted from each window using a modified Weighted Permutation Entropy (WPE) to produce one feature per window where the number of features of each EEG data will equal the number of windows obtained in the EEG data recording process. This research used k-fold cross-validation by dividing the data into training and testing data, classified using a Support Vector Machine. The EEG database used in this study came from Temple University Hospital EEG (TUH EEG), obtained online with as much as 1500 data. This EEG database consists of epileptic seizures (set 1) and non-epileptic seizures (sets 2, 3, 4, 5), each with 300 data. Based on testing, using this method produces an average accuracy of 81.29%.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125108388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00065
Sheng-Cong You, Cheng-Yu Peng
This paper proposes the depth from focus method to analyze the 3D image characteristics in order to sorting hex nuts from piles. A fixed-focus lens with moving displacements produces multiple images with varying depths of focal field. 3D image characteristics of hex nuts are extracted as center coordinates, sizes, and tilt angles. Multiple nut images are convolved using a band-pass filter to evaluate the highest score from different displacements, and the three-dimensional height can be obtained by the depth gray-scale images. The individual nut regions can be discriminated by gray difference values, meaning less values classified as the same region. To improve the characteristic qualities, the region growing algorithm is used to segment the nuts and remove noise according to set area size groups. Comparing pixel coordinate and real-world coordinate leads to the tolerance of center coordinates, sizes and tilt angles for the precision analysis.
{"title":"Analyzing the 3D Image Characteristics for a Pile of Hex Nuts using the Depth from Focus Method","authors":"Sheng-Cong You, Cheng-Yu Peng","doi":"10.1109/IS3C57901.2023.00065","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00065","url":null,"abstract":"This paper proposes the depth from focus method to analyze the 3D image characteristics in order to sorting hex nuts from piles. A fixed-focus lens with moving displacements produces multiple images with varying depths of focal field. 3D image characteristics of hex nuts are extracted as center coordinates, sizes, and tilt angles. Multiple nut images are convolved using a band-pass filter to evaluate the highest score from different displacements, and the three-dimensional height can be obtained by the depth gray-scale images. The individual nut regions can be discriminated by gray difference values, meaning less values classified as the same region. To improve the characteristic qualities, the region growing algorithm is used to segment the nuts and remove noise according to set area size groups. Comparing pixel coordinate and real-world coordinate leads to the tolerance of center coordinates, sizes and tilt angles for the precision analysis.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133637123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/is3c57901.2023.00102
You-Cheng Chen, Yih-Shyh Chiou, Mu-Jan Shih
Due to rapid developments in aerial photography techniques, drones are now capable of providing essential, full-color images for rice paddy field applications. In this article, a technique is introduced that employs an unsupervised model based on generative adversarial networks and an image super-resolution approach to increase the resolution of full-color images acquired by drones. These improved images are then utilized to detect and interpret the locations of transplanted rice paddies. The process involves the use of advanced image processing techniques to enhance the clarity and detail of drone images. Validation was conducted using an 80/20 training and testing data ratio, and a set of established rice paddy seedling coordinates was used to assess the effectiveness of the model. Based on the obtained results, the accuracy rate for identifying and interpreting the transplanted positions in rice paddies is demonstrated to be above 93%, as measured by the F1-measure value.
{"title":"Interpretation of Transplanted Positions Based on Image Super-Resolution Approaches for Rice Paddies","authors":"You-Cheng Chen, Yih-Shyh Chiou, Mu-Jan Shih","doi":"10.1109/is3c57901.2023.00102","DOIUrl":"https://doi.org/10.1109/is3c57901.2023.00102","url":null,"abstract":"Due to rapid developments in aerial photography techniques, drones are now capable of providing essential, full-color images for rice paddy field applications. In this article, a technique is introduced that employs an unsupervised model based on generative adversarial networks and an image super-resolution approach to increase the resolution of full-color images acquired by drones. These improved images are then utilized to detect and interpret the locations of transplanted rice paddies. The process involves the use of advanced image processing techniques to enhance the clarity and detail of drone images. Validation was conducted using an 80/20 training and testing data ratio, and a set of established rice paddy seedling coordinates was used to assess the effectiveness of the model. Based on the obtained results, the accuracy rate for identifying and interpreting the transplanted positions in rice paddies is demonstrated to be above 93%, as measured by the F1-measure value.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122780430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00037
Pai-Hsun Chen, Yin-Nan Wang, Lu-Han Chen
This paper presents a prototype of a virtual reality exercise game that uses machine learning to control content generation and game personalization. The game aims to provide a personalized workout experience for users by generating content that is tailored to their individual grip training level, interests and preferences. Genetic algorithms and artificial intelligence neural network algorithms are used to analyze user data such as their biometrics, workout history and feedback to generate challenging but achievable personalized workout routines. The game also incorporates gamification designs to promote engagement and motivation, such as NPC, score, rewards and so on. The prototype was evaluated through user research, which showed that participants found the content motivating and enjoyable. The results suggest that using machine learning for content generation and personalization can improve the user experience and encourage adherence to the training application in a virtual reality environment.
{"title":"A Pilot Study of Applying Machine Learning to Adjust the Content Generation and Personalization in Developing a Virtual Reality Hand Grip Strength Exergame Prototype","authors":"Pai-Hsun Chen, Yin-Nan Wang, Lu-Han Chen","doi":"10.1109/IS3C57901.2023.00037","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00037","url":null,"abstract":"This paper presents a prototype of a virtual reality exercise game that uses machine learning to control content generation and game personalization. The game aims to provide a personalized workout experience for users by generating content that is tailored to their individual grip training level, interests and preferences. Genetic algorithms and artificial intelligence neural network algorithms are used to analyze user data such as their biometrics, workout history and feedback to generate challenging but achievable personalized workout routines. The game also incorporates gamification designs to promote engagement and motivation, such as NPC, score, rewards and so on. The prototype was evaluated through user research, which showed that participants found the content motivating and enjoyable. The results suggest that using machine learning for content generation and personalization can improve the user experience and encourage adherence to the training application in a virtual reality environment.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125092270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00040
Yu-Huei Cheng, Cheng-Yao Kang
With the accelerated process of urbanization, traffic congestion and parking difficulties have gradually become key factors affecting the quality of life of urban residents. To address this challenge, this study proposes an intelligent parking lot navigation and optimal parking spot path planning method based on genetic algorithm. This method fully utilizes the superior adaptability of genetic algorithm, can flexibly adapt to changes in the parking lot environment, search for the optimal parking spot, thereby shortening the distance of vehicle driving in the parking lot, reducing traffic congestion, and saving time for finding parking spots. In this study, we first constructed a comprehensive parking lot model, including parking spaces, occupied parking spaces, entrances and exits, and other relevant parameters. Next, we designed and implemented a genetic algorithm, including individual generation, fitness function, crossover operation, mutation operation, and genetic optimization process. To demonstrate the practicality of the algorithm, we used a Tkinter graphical user interface to simulate the parking lot environment and present the path planning results. After experimental verification, the proposed intelligent parking lot navigation and optimal parking spot path planning method based on genetic algorithm in this study performed well in the driving performance of the parking lot, effectively solving the problem of parking difficulties and improving the efficiency of urban traffic operation.
{"title":"Application of Genetic Algorithm to Path Planning Problem of Automatic Navigation Parking Spaces in Parking Lots","authors":"Yu-Huei Cheng, Cheng-Yao Kang","doi":"10.1109/IS3C57901.2023.00040","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00040","url":null,"abstract":"With the accelerated process of urbanization, traffic congestion and parking difficulties have gradually become key factors affecting the quality of life of urban residents. To address this challenge, this study proposes an intelligent parking lot navigation and optimal parking spot path planning method based on genetic algorithm. This method fully utilizes the superior adaptability of genetic algorithm, can flexibly adapt to changes in the parking lot environment, search for the optimal parking spot, thereby shortening the distance of vehicle driving in the parking lot, reducing traffic congestion, and saving time for finding parking spots. In this study, we first constructed a comprehensive parking lot model, including parking spaces, occupied parking spaces, entrances and exits, and other relevant parameters. Next, we designed and implemented a genetic algorithm, including individual generation, fitness function, crossover operation, mutation operation, and genetic optimization process. To demonstrate the practicality of the algorithm, we used a Tkinter graphical user interface to simulate the parking lot environment and present the path planning results. After experimental verification, the proposed intelligent parking lot navigation and optimal parking spot path planning method based on genetic algorithm in this study performed well in the driving performance of the parking lot, effectively solving the problem of parking difficulties and improving the efficiency of urban traffic operation.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123735314","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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