Pub Date : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279697
Shido Sato, T. Anezaki
Our laboratory is developing a “GPS-non-GPS integrated navigation” system for automatic inspection of transmission power lines using drones. However, if the number of flying drones continue to increase, traffic rules must be enforced. Therefore, we formulated UTM rules for collision avoidance. We studied methods for collision avoidance based on three scenarios; namely, front collision, takeoff and landing, and two-way traffic.
{"title":"Autonomous flight drone for infrastructure (transmission line) inspection (2)","authors":"Shido Sato, T. Anezaki","doi":"10.1109/ICIIBMS.2017.8279697","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279697","url":null,"abstract":"Our laboratory is developing a “GPS-non-GPS integrated navigation” system for automatic inspection of transmission power lines using drones. However, if the number of flying drones continue to increase, traffic rules must be enforced. Therefore, we formulated UTM rules for collision avoidance. We studied methods for collision avoidance based on three scenarios; namely, front collision, takeoff and landing, and two-way traffic.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125611460","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279719
Swe Swe Aung, I. Nagayama, S. Tamaki
k-Nearest Neighbor (k-NN) is very simple and powerful approach to conceptually approximate real-valued or discrete-valued target function. Many researchers have recently approved that k-NN is a high prediction accuracy for variety of real world systems using many different types of datasets. However, as we know, k-NN is a type of lazy learning algorithms as it has to compare to each of stored training examples for each observed instance. Besides, the prediction accuracy of k-NN is under the influence of K values. Mostly, the higher K values make the algorithm yield lower prediction accuracy according to our experiments. For these issues, this paper focuses on two properties that are to upgrade the classification accuracy by introducing Regional Distance-based k-NN (RD-kNN) and to speed up the processing time performance of k-NN by applying multi-threading approach. For the experiments, we used the real data sets, wine, iris, heart stalog, breast cancer, and breast tissue, from UCI machine learning repository. According to our test cases and simulations carried out, it was also experimentally confirmed that the new approach, RD-kNN, has a better performance than classical k-NN.
{"title":"Regional distance-based k-NN classification","authors":"Swe Swe Aung, I. Nagayama, S. Tamaki","doi":"10.1109/ICIIBMS.2017.8279719","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279719","url":null,"abstract":"k-Nearest Neighbor (k-NN) is very simple and powerful approach to conceptually approximate real-valued or discrete-valued target function. Many researchers have recently approved that k-NN is a high prediction accuracy for variety of real world systems using many different types of datasets. However, as we know, k-NN is a type of lazy learning algorithms as it has to compare to each of stored training examples for each observed instance. Besides, the prediction accuracy of k-NN is under the influence of K values. Mostly, the higher K values make the algorithm yield lower prediction accuracy according to our experiments. For these issues, this paper focuses on two properties that are to upgrade the classification accuracy by introducing Regional Distance-based k-NN (RD-kNN) and to speed up the processing time performance of k-NN by applying multi-threading approach. For the experiments, we used the real data sets, wine, iris, heart stalog, breast cancer, and breast tissue, from UCI machine learning repository. According to our test cases and simulations carried out, it was also experimentally confirmed that the new approach, RD-kNN, has a better performance than classical k-NN.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125682534","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279740
Shotaro Gushi, H. Higa, H. Uehara, T. Soken
This paper describes a mobile robotic arm for people with severe disabilities. Its user interface using eye movements consists of a web camera, computer, and display unit. Using the robotic arm system, we performed two experiments: (1) transferring water from a bowl to the other and (2) eating soup experiments. It is found from the experimental results that the robotic arm system can transfer more than 82 % of water and soup to the respective positions.
{"title":"A mobile robotic arm for people with severe disabilities: Evaluation of scooping foods","authors":"Shotaro Gushi, H. Higa, H. Uehara, T. Soken","doi":"10.1109/ICIIBMS.2017.8279740","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279740","url":null,"abstract":"This paper describes a mobile robotic arm for people with severe disabilities. Its user interface using eye movements consists of a web camera, computer, and display unit. Using the robotic arm system, we performed two experiments: (1) transferring water from a bowl to the other and (2) eating soup experiments. It is found from the experimental results that the robotic arm system can transfer more than 82 % of water and soup to the respective positions.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133976065","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279730
Yue-Dar Jou, Zhan-Pei Lin, Fu-Kun Chen
Least-squares design of two-channel quadrature mirror filter banks can be efficiently solved using infinite impulse response all-pass filters without yielding magnitude distortion. This paper exploits a neural network-based Lyapunov energy function to relate the phase objective function of the all-pass-based quadrature mirror filter banks. Applying the neural network architecture and suitable Hopfield-related parameters, the optimal all-pass filter coefficients can be obtained. By further using the parallel combination of the all-pass filters, the two-channel quadrature mirror filter banks can be efficiently designed. Simulation results demonstrate that the proposed approach achieves accurate performance in both reconstruction error and group delay.
{"title":"Design of all-pass-based quadrature mirror filter banks using a Lyapunov error criterion","authors":"Yue-Dar Jou, Zhan-Pei Lin, Fu-Kun Chen","doi":"10.1109/ICIIBMS.2017.8279730","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279730","url":null,"abstract":"Least-squares design of two-channel quadrature mirror filter banks can be efficiently solved using infinite impulse response all-pass filters without yielding magnitude distortion. This paper exploits a neural network-based Lyapunov energy function to relate the phase objective function of the all-pass-based quadrature mirror filter banks. Applying the neural network architecture and suitable Hopfield-related parameters, the optimal all-pass filter coefficients can be obtained. By further using the parallel combination of the all-pass filters, the two-channel quadrature mirror filter banks can be efficiently designed. Simulation results demonstrate that the proposed approach achieves accurate performance in both reconstruction error and group delay.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134490996","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279725
Mohammad Aldibaja, N. Suganuma, Keisuke Yoneda, R. Yanase, Akisue Kuramoto
This paper highlights the importance of incorporating holistic and feature based localization systems in autonomous driving. The intensity based localization system is represented by calculating the matching score between LIDAR and map images whereas the feature based system is integrated by extracting the lateral edges with respect to the vehicle heading angle. An edge matching technique is then applied to estimate the lateral position based on the common features between the map and LIDAR images. The experimental results have verified that the estimation of the lateral and longitudinal poses has become more robust by combining the image and edge matching results against the changes of weather and environmental conditions.
{"title":"On autonomous driving: Why holistic and feature matching must be used in localization?","authors":"Mohammad Aldibaja, N. Suganuma, Keisuke Yoneda, R. Yanase, Akisue Kuramoto","doi":"10.1109/ICIIBMS.2017.8279725","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279725","url":null,"abstract":"This paper highlights the importance of incorporating holistic and feature based localization systems in autonomous driving. The intensity based localization system is represented by calculating the matching score between LIDAR and map images whereas the feature based system is integrated by extracting the lateral edges with respect to the vehicle heading angle. An edge matching technique is then applied to estimate the lateral position based on the common features between the map and LIDAR images. The experimental results have verified that the estimation of the lateral and longitudinal poses has become more robust by combining the image and edge matching results against the changes of weather and environmental conditions.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132283612","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279708
Somchok Kimpan, Noppadol Maneerat, C. Kimpan
This research article discusses the process of increasing the efficiency of image retrieval based on details from the database of the retinal image of diabetic retinopathy patients. The image retrieval uses Radial Inverse Force Histograms which can improve the performance of the image retrieval process in using the details of the retinal image. The value of Radial Inverse Force Histograms can be used to retrieve the similar image. The experimental results indicated that using Radial Inverse Force Histograms can detect the diabetic eyes. Moreover, the image retrieval system is useful in diagnosis the retinal disorders for effectively screen or separate the diabetic retinopathy patients.
{"title":"Diabetic retinopathy image analysis using radial inverse force histograms","authors":"Somchok Kimpan, Noppadol Maneerat, C. Kimpan","doi":"10.1109/ICIIBMS.2017.8279708","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279708","url":null,"abstract":"This research article discusses the process of increasing the efficiency of image retrieval based on details from the database of the retinal image of diabetic retinopathy patients. The image retrieval uses Radial Inverse Force Histograms which can improve the performance of the image retrieval process in using the details of the retinal image. The value of Radial Inverse Force Histograms can be used to retrieve the similar image. The experimental results indicated that using Radial Inverse Force Histograms can detect the diabetic eyes. Moreover, the image retrieval system is useful in diagnosis the retinal disorders for effectively screen or separate the diabetic retinopathy patients.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133932389","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279704
Ryusei Shima, He Yunan, O. Fukuda, H. Okumura, K. Arai, N. Bu
This paper proposes a prosthetic control method which incorporates a novel object classifier with a conventional EMG-based motion classifier. The proposed method uses not only color information but spatial information to reduce the misclassification in previous research. The depth images are created based on spatial information which is acquired by Kinect. The deep convolutional neural network is adopted for the object classification, and the posture of the prosthetic hand is controlled based on the classification result of the object. To verify the validity of the proposed control method, the experiments have been carried out with 6 target objects. The 300 images for each target object were acquired in various directions. Their shapes resemble each other in particular perspective. We trained the deep convolutional neural network using the hybrid images which involve gray scale and depth information. In the experiments, the depth information improved the learning performance with high classification accuracy. These results revealed that the proposed method has high potential to improve object classification ability.
{"title":"Object classification with deep convolutional neural network using spatial information","authors":"Ryusei Shima, He Yunan, O. Fukuda, H. Okumura, K. Arai, N. Bu","doi":"10.1109/ICIIBMS.2017.8279704","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279704","url":null,"abstract":"This paper proposes a prosthetic control method which incorporates a novel object classifier with a conventional EMG-based motion classifier. The proposed method uses not only color information but spatial information to reduce the misclassification in previous research. The depth images are created based on spatial information which is acquired by Kinect. The deep convolutional neural network is adopted for the object classification, and the posture of the prosthetic hand is controlled based on the classification result of the object. To verify the validity of the proposed control method, the experiments have been carried out with 6 target objects. The 300 images for each target object were acquired in various directions. Their shapes resemble each other in particular perspective. We trained the deep convolutional neural network using the hybrid images which involve gray scale and depth information. In the experiments, the depth information improved the learning performance with high classification accuracy. These results revealed that the proposed method has high potential to improve object classification ability.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125891503","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279729
K. Thakulsukanant, V. Patanavijit
Theoretically, the conventional image enlarging technique is a mathematical method for building a superior enriched resolution image, which is normally insisted for modern computer vision and image processing application by utilizing only one poor resolution image, which is normally captured from any commercial embedded camera systems. Due to the fast computation, the Single-Image Super-Resolution (SISR) is one of the well-known Super Resolution-Reconstruction (SRR) techniques and the SISR is desired for applying on only one poor resolution image. Therefore, this article aims to present the image enlarged technique founded on SISR algorithm utilizing Hampel stochastic function and high-band frequency synthesizing. Unfortunately, the efficacy of the SISR technique is relied upon three parameters (b, h, k) and it is difficult task for estimating these suitable values of these three parameters for reconstructing the superior enriched resolution image with the optimum Peak Signal-to-Noise Ratio (PSNR). In consideration of deciphering to this problem, the Hampel stochastic function, which is relied upon wholly one parameter (J), instead of three parameters like the conventional function, is comprised into SISR technique. By studying on 14 classic images, which are corrupted by different noise models, in the statically exploratory section, the efficacy of the fast SISR technique closely equal to the conventional SISR technique but the parameter adjustment process of the proposed fast SISR technique (with one parameter) is more simple and fasert than the conventional SISR technique (with three parameters). Because of fast computation in the parameter adjustment process, the proposed fast SISR technique is more suitable for real implementation.
{"title":"A fast SISR technique founded on synthesize high-band frequency and an adaptive hampel stochastic function","authors":"K. Thakulsukanant, V. Patanavijit","doi":"10.1109/ICIIBMS.2017.8279729","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279729","url":null,"abstract":"Theoretically, the conventional image enlarging technique is a mathematical method for building a superior enriched resolution image, which is normally insisted for modern computer vision and image processing application by utilizing only one poor resolution image, which is normally captured from any commercial embedded camera systems. Due to the fast computation, the Single-Image Super-Resolution (SISR) is one of the well-known Super Resolution-Reconstruction (SRR) techniques and the SISR is desired for applying on only one poor resolution image. Therefore, this article aims to present the image enlarged technique founded on SISR algorithm utilizing Hampel stochastic function and high-band frequency synthesizing. Unfortunately, the efficacy of the SISR technique is relied upon three parameters (b, h, k) and it is difficult task for estimating these suitable values of these three parameters for reconstructing the superior enriched resolution image with the optimum Peak Signal-to-Noise Ratio (PSNR). In consideration of deciphering to this problem, the Hampel stochastic function, which is relied upon wholly one parameter (J), instead of three parameters like the conventional function, is comprised into SISR technique. By studying on 14 classic images, which are corrupted by different noise models, in the statically exploratory section, the efficacy of the fast SISR technique closely equal to the conventional SISR technique but the parameter adjustment process of the proposed fast SISR technique (with one parameter) is more simple and fasert than the conventional SISR technique (with three parameters). Because of fast computation in the parameter adjustment process, the proposed fast SISR technique is more suitable for real implementation.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"364 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121383344","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279721
Jae-Hwan Ryu, Byeong-Hyeon Lee, Miran Lee, Jeongpil Choi, Hyunil Cho
Owing to advances in information technology, some studies have been conducted on work environment monitoring system using various sensors in order to prevention of accidents, information gathering, and optimal management of work environment. It was found that a factory environment monitoring system using climate sensors such as O2, Co2, Nh3, and Pm2 has recently become essential because it can help protect the safety and health of workers. Climate sensors are a mesh-type arrangement placed at certain distances apart to acquire and anlyze exact environmental information. Sensitivity, specificity, and accuracy increase as the number of sensors are increased. However, as the number of sensors increases, it becomes more difficult to detect faulty sensors, and in the worst case, false information can lead to accidents. It is necessary, therefore, for environment monitoring systems using a large number of climate sensors to have a function that will automatically detect the failure of a sensor. The value of an individual climate sensor is organically realted to the value of neighbor sensors, unless they are located in enclosed spaces. If the sensor value at a specific position changes, the neighboring sensor's values are also changed. In the past, much research has studied algorithms to improve the sensing accuracy of specific location using neighbor sensor data based on these principle. If these algorithms are used inversely, it is possible to infer or predict the environmental information in the area where the sensor has failed, by using the values from neighboring sensors. Even with these systems in operaton, a major concern on many industrial sites is that work does not stop even if a sensor failure is detected. In other words, when workers are in areas where a sensor has failed, they may become exposed to hazardous conditions. Therefore, even if a sensor fails, for the sake of the workers, it is necessary to continuously provide environmental information to those in the affected area. This paper presents the automatic sensor fault detection and sensor reconstruction algorithm for emergency recovery relative to the production of continuous and reliable environmental data. The principle of automatic sensor fault detection and the sensor reconstruction algorithm are the same. The proposed algorithms consist of four steps. In the first step, a total of nine sensors consisting of 3∗3 are configured as one set. In the second step, the three sensors, including the central sensor, are grouped into one group. One set becomes a total of four groups. In the third step, reference curve maps (RCM) are created to record changes in sensor values according to the amount of ambient gas. The RCM records the sensor's values as the gas changes in density. Four RCMs are created per set. A total of 32 RCMs are created because one sensor is included in a total of eight sets. In the fourth step, the automatic sensor fault detection and sensor reconstruction algorithms ar
{"title":"Automatic sensor fault detection and sensor reconstruction algorithm for emergency recovery in industrial fields","authors":"Jae-Hwan Ryu, Byeong-Hyeon Lee, Miran Lee, Jeongpil Choi, Hyunil Cho","doi":"10.1109/ICIIBMS.2017.8279721","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279721","url":null,"abstract":"Owing to advances in information technology, some studies have been conducted on work environment monitoring system using various sensors in order to prevention of accidents, information gathering, and optimal management of work environment. It was found that a factory environment monitoring system using climate sensors such as O2, Co2, Nh3, and Pm2 has recently become essential because it can help protect the safety and health of workers. Climate sensors are a mesh-type arrangement placed at certain distances apart to acquire and anlyze exact environmental information. Sensitivity, specificity, and accuracy increase as the number of sensors are increased. However, as the number of sensors increases, it becomes more difficult to detect faulty sensors, and in the worst case, false information can lead to accidents. It is necessary, therefore, for environment monitoring systems using a large number of climate sensors to have a function that will automatically detect the failure of a sensor. The value of an individual climate sensor is organically realted to the value of neighbor sensors, unless they are located in enclosed spaces. If the sensor value at a specific position changes, the neighboring sensor's values are also changed. In the past, much research has studied algorithms to improve the sensing accuracy of specific location using neighbor sensor data based on these principle. If these algorithms are used inversely, it is possible to infer or predict the environmental information in the area where the sensor has failed, by using the values from neighboring sensors. Even with these systems in operaton, a major concern on many industrial sites is that work does not stop even if a sensor failure is detected. In other words, when workers are in areas where a sensor has failed, they may become exposed to hazardous conditions. Therefore, even if a sensor fails, for the sake of the workers, it is necessary to continuously provide environmental information to those in the affected area. This paper presents the automatic sensor fault detection and sensor reconstruction algorithm for emergency recovery relative to the production of continuous and reliable environmental data. The principle of automatic sensor fault detection and the sensor reconstruction algorithm are the same. The proposed algorithms consist of four steps. In the first step, a total of nine sensors consisting of 3∗3 are configured as one set. In the second step, the three sensors, including the central sensor, are grouped into one group. One set becomes a total of four groups. In the third step, reference curve maps (RCM) are created to record changes in sensor values according to the amount of ambient gas. The RCM records the sensor's values as the gas changes in density. Four RCMs are created per set. A total of 32 RCMs are created because one sensor is included in a total of eight sets. In the fourth step, the automatic sensor fault detection and sensor reconstruction algorithms ar","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122612175","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 : 2017-11-01DOI: 10.1109/ICIIBMS.2017.8279684
S. Fujii, Kohei Hiranaka, Seichio Miyagi, Motoki Kyan, Shouichi Tanifuji, T. Suriyon, Zacharie Mbaitiga, N. Yoshikawa, K. Kinoshita
This paper presents the concept of a highly-sophisticated network system using unmanned aerial vehicles, further named drones, and wireless networks for enhancing safety against natural disasters. The system consists of several drones and ground vehicles. Each vehicle has a wireless network unit, which employs the dual-mode Wireless Access in Vehicular Environment (WAVE) in the 700 MHz or 5 GHz band, with the goal of creating a multi-hop (more than three hops) ad hoc network. These features enable the system to facilitate rescue and relief operations in the event of a serious disaster. The proposed system represents an innovation in the field of mesh network systems, being based on a combination of ubiquitous drones and ground vehicles.
{"title":"Integration of drones' communication into an ITS network","authors":"S. Fujii, Kohei Hiranaka, Seichio Miyagi, Motoki Kyan, Shouichi Tanifuji, T. Suriyon, Zacharie Mbaitiga, N. Yoshikawa, K. Kinoshita","doi":"10.1109/ICIIBMS.2017.8279684","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2017.8279684","url":null,"abstract":"This paper presents the concept of a highly-sophisticated network system using unmanned aerial vehicles, further named drones, and wireless networks for enhancing safety against natural disasters. The system consists of several drones and ground vehicles. Each vehicle has a wireless network unit, which employs the dual-mode Wireless Access in Vehicular Environment (WAVE) in the 700 MHz or 5 GHz band, with the goal of creating a multi-hop (more than three hops) ad hoc network. These features enable the system to facilitate rescue and relief operations in the event of a serious disaster. The proposed system represents an innovation in the field of mesh network systems, being based on a combination of ubiquitous drones and ground vehicles.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126986167","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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