Pub Date : 2019-02-01DOI: 10.1109/icaiic.2019.8669001
{"title":"ICAIIC 2019 TOC","authors":"","doi":"10.1109/icaiic.2019.8669001","DOIUrl":"https://doi.org/10.1109/icaiic.2019.8669001","url":null,"abstract":"","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129694143","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668993
Takuro Ebuchi, Hiroshi Yamamoto
In recent years, the number of casualties and injuries at intersections and roads has been decreasing due to wide spread of safe driving support systems, but the number of casualties and injuries due to low-speed traffic accidents in parking lots has not decreased. In the parking lot, it is necessary to drive while looking for an empty slot, which may result in contact accidents with pedestrians. Therefore, in this research, we propose a new smart parking system that prevents low-speed contact accidents by estimating availability of slots in the parking lot and the position of pedestrians. The proposed system attempts to estimate positions of user’s smartphones by deploying a small number of beacon devices on the parking lot, and by analyzing the radio wave intensity measured by the smartphones. In addition, estimation accuracy of the position of the pedestrian / driver is evaluated by experimental evaluation in a parking lot. Through the performance evaluation, estimation accuracy of the vehicle’s position to higher than 98%, and estimation accuracy of the pedestrian’s position is about 70%.
{"title":"Vehicle/Pedestrian Localization System Using Multiple Radio Beacons and Machine Learning for Smart Parking","authors":"Takuro Ebuchi, Hiroshi Yamamoto","doi":"10.1109/ICAIIC.2019.8668993","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668993","url":null,"abstract":"In recent years, the number of casualties and injuries at intersections and roads has been decreasing due to wide spread of safe driving support systems, but the number of casualties and injuries due to low-speed traffic accidents in parking lots has not decreased. In the parking lot, it is necessary to drive while looking for an empty slot, which may result in contact accidents with pedestrians. Therefore, in this research, we propose a new smart parking system that prevents low-speed contact accidents by estimating availability of slots in the parking lot and the position of pedestrians. The proposed system attempts to estimate positions of user’s smartphones by deploying a small number of beacon devices on the parking lot, and by analyzing the radio wave intensity measured by the smartphones. In addition, estimation accuracy of the position of the pedestrian / driver is evaluated by experimental evaluation in a parking lot. Through the performance evaluation, estimation accuracy of the vehicle’s position to higher than 98%, and estimation accuracy of the pedestrian’s position is about 70%.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129316506","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 : 2019-02-01DOI: 10.1109/icaiic.2019.8668994
{"title":"ICAIIC 2019 Venue","authors":"","doi":"10.1109/icaiic.2019.8668994","DOIUrl":"https://doi.org/10.1109/icaiic.2019.8668994","url":null,"abstract":"","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127334812","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669070
Jongbae Kim
In this paper, we propose a method to detect a pedestrian in real time in a low illumination environment and estimate the distance from the camera using a smart phone based thermal camera. Thermal cameras use equipment that can be attached to low-cost smartphones and which use cameras for image processing in real-time. A pedestrian detector is created using a multi-stage cascade learning device to detect pedestrians in a low-illuminated environment, and the pedestrian area is detected using this detector. Then, the distance is estimated by calculating the position of the pedestrian detected in the real-world 3D environment in the 2D thermal image by calculating the parameters detected by the thermal imaging camera in advance. Experimental results show that the detection accuracy of pedestrians is about 91% and the accuracy of distance estimation is 95%. In this way, the proposed method can be applied to the image sensing system in real time in a low-illuminance environment such as nighttime.
{"title":"Pedestrian Detection and Distance Estimation Using Thermal Camera in Night Time","authors":"Jongbae Kim","doi":"10.1109/ICAIIC.2019.8669070","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669070","url":null,"abstract":"In this paper, we propose a method to detect a pedestrian in real time in a low illumination environment and estimate the distance from the camera using a smart phone based thermal camera. Thermal cameras use equipment that can be attached to low-cost smartphones and which use cameras for image processing in real-time. A pedestrian detector is created using a multi-stage cascade learning device to detect pedestrians in a low-illuminated environment, and the pedestrian area is detected using this detector. Then, the distance is estimated by calculating the position of the pedestrian detected in the real-world 3D environment in the 2D thermal image by calculating the parameters detected by the thermal imaging camera in advance. Experimental results show that the detection accuracy of pedestrians is about 91% and the accuracy of distance estimation is 95%. In this way, the proposed method can be applied to the image sensing system in real time in a low-illuminance environment such as nighttime.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132951183","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669082
Hyeyoung Park, Kwanyong Lee
Natural gradient learning, which is one of gradient descent learning methods, is known to have ideal convergence properties in the learning of hierarchical machines such as layered neural networks. However, there are a few limitations that degrades its practical usability: necessity of true probability density function of input variables and heavy computational cost due to matrix inversion. Though its adaptive approximation have been developed, it is basically derived for online learning mode, in which a single update is done for a single data sample. Noting that the on-line learning mode is not appropriate for the tasks with huge number of training data, this paper proposes a practical implementation of natural gradient for mini-batch learning mode, which is the most common setting in the real application with large data set. Computational experiments on benchmark datasets shows the efficiency of the proposed methods.
{"title":"Adaptive Natural Gradient Method for Learning Neural Networks with Large Data set in Mini-Batch Mode","authors":"Hyeyoung Park, Kwanyong Lee","doi":"10.1109/ICAIIC.2019.8669082","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669082","url":null,"abstract":"Natural gradient learning, which is one of gradient descent learning methods, is known to have ideal convergence properties in the learning of hierarchical machines such as layered neural networks. However, there are a few limitations that degrades its practical usability: necessity of true probability density function of input variables and heavy computational cost due to matrix inversion. Though its adaptive approximation have been developed, it is basically derived for online learning mode, in which a single update is done for a single data sample. Noting that the on-line learning mode is not appropriate for the tasks with huge number of training data, this paper proposes a practical implementation of natural gradient for mini-batch learning mode, which is the most common setting in the real application with large data set. Computational experiments on benchmark datasets shows the efficiency of the proposed methods.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121320131","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669071
Kyosuke Shibata, Hiroshi Yamamoto
In recent years, research and development of a people flow observation system is attracting attention in various fields (e.g., city area, shopping district) because the directional information of people flow is very useful for various objective (e.g., navigation, evacuation). However, existing studies of the observation system have mainly been utilizing cameras and image analysis techniques for specifying people flow, but the use of cameras is not preferable in actual fields because of the privacy issues.Therefore, in this study, we propose a new people crowd density observation system for people flow observation. In order to avoid privacy issues, the proposed system dmeasures only signal strength of radio waves of the cellular communication. Furthermore, the measurement results are analyzed by utilizing several machine learning techniques so as to estimate crowd density of many people who have a mobile phone or a smartphone.
{"title":"People Crowd Density Estimation System using Deep Learning for Radio Wave Sensing of Cellular Communication","authors":"Kyosuke Shibata, Hiroshi Yamamoto","doi":"10.1109/ICAIIC.2019.8669071","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669071","url":null,"abstract":"In recent years, research and development of a people flow observation system is attracting attention in various fields (e.g., city area, shopping district) because the directional information of people flow is very useful for various objective (e.g., navigation, evacuation). However, existing studies of the observation system have mainly been utilizing cameras and image analysis techniques for specifying people flow, but the use of cameras is not preferable in actual fields because of the privacy issues.Therefore, in this study, we propose a new people crowd density observation system for people flow observation. In order to avoid privacy issues, the proposed system dmeasures only signal strength of radio waves of the cellular communication. Furthermore, the measurement results are analyzed by utilizing several machine learning techniques so as to estimate crowd density of many people who have a mobile phone or a smartphone.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116670582","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669018
Changhun Hyun, Hyeyoung Park
Automatic report generation is a technology that automatically generates documents in the form of report by summarizing various materials according to a specific topic in time sequence or subject. Although the main content of the report is text, insertion of appropriate images can improve the completeness of the report. In this paper, we propose an image recommendation method for automatically selecting and inserting appropriate images corresponding to a specific part of a report. In our proposed method, reevaluation of the candidate images is performed based on the semantic similarity between query and the contents of the images. In order to transform semantic information of text query and image into one vector space, we extracted semantic information from image as a set of tags form using deep learning based object detection module. Also, we extracted tags from the given title of the image so that the proposed system can evaluate the candidate images even in the case that the given query includes specific keywords or proper nouns which were not learned by object detection and recognition module in advance. In this paper, we conducted experiments on eight queries related to recent events to verify the applicability of our proposed image recommendation system and evaluate the image selection accuracy.
{"title":"Image Recommendation for Automatic Report Generation using Semantic Similarity","authors":"Changhun Hyun, Hyeyoung Park","doi":"10.1109/ICAIIC.2019.8669018","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669018","url":null,"abstract":"Automatic report generation is a technology that automatically generates documents in the form of report by summarizing various materials according to a specific topic in time sequence or subject. Although the main content of the report is text, insertion of appropriate images can improve the completeness of the report. In this paper, we propose an image recommendation method for automatically selecting and inserting appropriate images corresponding to a specific part of a report. In our proposed method, reevaluation of the candidate images is performed based on the semantic similarity between query and the contents of the images. In order to transform semantic information of text query and image into one vector space, we extracted semantic information from image as a set of tags form using deep learning based object detection module. Also, we extracted tags from the given title of the image so that the proposed system can evaluate the candidate images even in the case that the given query includes specific keywords or proper nouns which were not learned by object detection and recognition module in advance. In this paper, we conducted experiments on eight queries related to recent events to verify the applicability of our proposed image recommendation system and evaluate the image selection accuracy.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132677326","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668985
M. Sato, Y. Fukuyama
This paper proposes operation planning of energy plants by swarm reinforcement learning in order to realize successful BEMS for small and mid-sized buildings. It usually takes many man-hours to develop an evolutionary computation based program and develop a model considering facility characteristics and so on for an energy management system, while engineering man-hours can be reduced and appropriate operational planning can be expected to be realized by a versatile program of swarm reinforcement learning without consideration of facility characteristics and so on. Moreover, the results of the proposed methods are compared with those of a basic Q learning based method and a basic particle swarm optimization (PSO) based method. It is verified that energy cost can be more reduced by one of the proposed methods (PSO-Q based method) than those by the original Q-learning based method. Since the rates to the whole cost are large in case of small and mid-sized buildings, the proposed swarm reinforcement learning based methods can contribute to successful BEMS for small and mid-sized buildings.
{"title":"Swarm Reinforcement Learning for Operational Planning of Energy Plants for Small and Mid-Sized Building Energy Management Systems","authors":"M. Sato, Y. Fukuyama","doi":"10.1109/ICAIIC.2019.8668985","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668985","url":null,"abstract":"This paper proposes operation planning of energy plants by swarm reinforcement learning in order to realize successful BEMS for small and mid-sized buildings. It usually takes many man-hours to develop an evolutionary computation based program and develop a model considering facility characteristics and so on for an energy management system, while engineering man-hours can be reduced and appropriate operational planning can be expected to be realized by a versatile program of swarm reinforcement learning without consideration of facility characteristics and so on. Moreover, the results of the proposed methods are compared with those of a basic Q learning based method and a basic particle swarm optimization (PSO) based method. It is verified that energy cost can be more reduced by one of the proposed methods (PSO-Q based method) than those by the original Q-learning based method. Since the rates to the whole cost are large in case of small and mid-sized buildings, the proposed swarm reinforcement learning based methods can contribute to successful BEMS for small and mid-sized buildings.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132280001","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8669000
Seong-Hoon Kim, Gi-Tae Han
The respiration status of a person is one of the vital signs that can be used to check the health condition of the person. The respiration status has been measured in various ways in the medical and healthcare sectors. Contact type sensors were conventionally used to measure respiration. The contact type sensors have been used primarily in the medical sector, because they can be only used in a limited environment. Recent studies have evaluated the ways of detecting human respiration patterns using Ultra-Wideband (UWB) Radar, which relies on non-contact type sensors. Previous studies evaluated the apnea pattern during sleep by analyzing the respiration signals acquired by UWB Radar using a principal component analysis (PCA). However, it is necessary to measure various respiration patterns in addition to apnea in order to accurately analyze the health condition of an individual in the healthcare sector. Therefore, this study proposed a method to recognize four respiration patterns based on the 1D convolutional neural network from the respiration signals acquired from UWB Radar. The proposed method extracts the eupnea, bradypnea, tachypnea, and apnea respiration patterns from UWB Radar and composes a learning dataset. The proposed method learned data through 1D CNN and the recognition accuracy was measured. The results of this study revealed that the accuracy of the proposed method was up to 15% higher than that of the conventional classification algorithms (i.e., PCA and Support Vector Machine (SVM)).
{"title":"1D CNN Based Human Respiration Pattern Recognition using Ultra Wideband Radar","authors":"Seong-Hoon Kim, Gi-Tae Han","doi":"10.1109/ICAIIC.2019.8669000","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669000","url":null,"abstract":"The respiration status of a person is one of the vital signs that can be used to check the health condition of the person. The respiration status has been measured in various ways in the medical and healthcare sectors. Contact type sensors were conventionally used to measure respiration. The contact type sensors have been used primarily in the medical sector, because they can be only used in a limited environment. Recent studies have evaluated the ways of detecting human respiration patterns using Ultra-Wideband (UWB) Radar, which relies on non-contact type sensors. Previous studies evaluated the apnea pattern during sleep by analyzing the respiration signals acquired by UWB Radar using a principal component analysis (PCA). However, it is necessary to measure various respiration patterns in addition to apnea in order to accurately analyze the health condition of an individual in the healthcare sector. Therefore, this study proposed a method to recognize four respiration patterns based on the 1D convolutional neural network from the respiration signals acquired from UWB Radar. The proposed method extracts the eupnea, bradypnea, tachypnea, and apnea respiration patterns from UWB Radar and composes a learning dataset. The proposed method learned data through 1D CNN and the recognition accuracy was measured. The results of this study revealed that the accuracy of the proposed method was up to 15% higher than that of the conventional classification algorithms (i.e., PCA and Support Vector Machine (SVM)).","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123001595","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 : 2019-02-01DOI: 10.1109/ICAIIC.2019.8668975
K. Yano, Naoto Egashira, Julian Webber, M. Usui, Yoshinori Suzuki
The authors have studied a multiband wireless local area network (MB-WLAN) which can effectively detect and exploit unused radio resources scattered in time and frequency domains. The MB-WLAN sets one or more primary channels (PCHs) in multiple frequency bands, and each station (STA) carries out random back-off process on the multiple primary channels to obtain a transmission opportunity (TXOP). Once a STA obtains a TXOP on any PCH, it checks whether or not another TXOP can be obtained on any other PCH in near future. If the STA judges that it can obtain another TXOP, it pends its transmission until another TXOP is obtained on any other PCH, and then a channel-bonded frame is transmitted. A suitable pending duration depends on the level of congestion on each PCH because the STA lose its TXOP more frequently to other STA’s frame transmission as the PCH gets more crowded. This paper, therefore, proposes a method to control the maximum pending duration with the aid of idle length prediction based on probabilistic neural network (PNN). This paper also proposes a method to control the timing to invoke learning of channel usage for PNN in order to get rid of the impact of self-transmission on the characteristics of channel usage. In order to validate the effectiveness of the proposals, this paper evaluates the achievable throughput of the MB-WLAN by computer simulation assuming IEEE 802.11n/ac-based WLAN operated in the 2.4GHz and 5GHz bands and 4-antenna STA. It is confirmed that the MBWLAN with two proposals can achieve almost best performance regardless the level of congestion on PCHs.
{"title":"Achievable Throughput of Multiband Wireless LAN using Simultaneous Transmission over Multiple Primary Channels Assisted by Idle Length Prediction Based on PNN","authors":"K. Yano, Naoto Egashira, Julian Webber, M. Usui, Yoshinori Suzuki","doi":"10.1109/ICAIIC.2019.8668975","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668975","url":null,"abstract":"The authors have studied a multiband wireless local area network (MB-WLAN) which can effectively detect and exploit unused radio resources scattered in time and frequency domains. The MB-WLAN sets one or more primary channels (PCHs) in multiple frequency bands, and each station (STA) carries out random back-off process on the multiple primary channels to obtain a transmission opportunity (TXOP). Once a STA obtains a TXOP on any PCH, it checks whether or not another TXOP can be obtained on any other PCH in near future. If the STA judges that it can obtain another TXOP, it pends its transmission until another TXOP is obtained on any other PCH, and then a channel-bonded frame is transmitted. A suitable pending duration depends on the level of congestion on each PCH because the STA lose its TXOP more frequently to other STA’s frame transmission as the PCH gets more crowded. This paper, therefore, proposes a method to control the maximum pending duration with the aid of idle length prediction based on probabilistic neural network (PNN). This paper also proposes a method to control the timing to invoke learning of channel usage for PNN in order to get rid of the impact of self-transmission on the characteristics of channel usage. In order to validate the effectiveness of the proposals, this paper evaluates the achievable throughput of the MB-WLAN by computer simulation assuming IEEE 802.11n/ac-based WLAN operated in the 2.4GHz and 5GHz bands and 4-antenna STA. It is confirmed that the MBWLAN with two proposals can achieve almost best performance regardless the level of congestion on PCHs.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125197554","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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