K. Taniguchi

University of Hyogo, Kōbe, Hyōgo, Japan

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Publications (51)1.07 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes fuzzy damage extraction method for ultrasonic nondestructive testing images. In our experiment, we employ a piece of wind turbine blade as a specimen has artificial damages. We acquire ultrasonic waveforms from scanning lines on surface of the pecimen using an ultrasonic single probe. To extract the damages, we calculate fuzzy degrees of average difference data of all scanning lines, and make fuzzy images whose ntensities calculated by the fuzzy degrees. As the results, we found the line image with all damage portions, and we estimated depth of damage surface with high accuracy.
    Multiple-Valued Logic (ISMVL), 2013 IEEE 43rd International Symposium on; 01/2013
  • K. Tsukuda, T. Egawa, K. Taniguchi, Y. Hata
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes a method of average difference imaging and applying it to ultrasonic nondestructive evaluation of wind turbine blade. Average difference image is made from the means of intensity difference among several images. The image demonstrates the difference between an interested image and the other images. In performance test of this method, we employ a specimen of wind turbine blade. This specimen has artificial damages. We acquire ultrasonic waveforms from scanning lines on specimen using an ultrasonic single probe. To extract the damage, we make average difference images for every line. In the images, we could successfully find the line image with damage portions, and we estimated depth of damage portions with high accuracy. Thus, average difference imaging effectively led the line image with the damages. We then found the depth of damage portions from the line image.
    Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on; 01/2012
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    ABSTRACT: Recently, as the number of elderly people increases, much more caregivers are required for the support for them. However the number of caregivers and therapists is not enough in the current situation. In this paper, we propose a support system for the elderly introducing robot partners, sensor networks, and portable sensing devices in informationally structured space. In the system, human state estimation is one of the most important technologies. In order to realize the estimation suitable to the elderly, we should consider how to model the human states. Most of previous methods are based on off-line statistic approaches. In this paper, we discuss an on-line learning method for modeling human states. First of all, we explain the system for the elderly in informationally structured space. Next, we propose an on-line learning architecture based on spiking neurons. Finally, we show an example of experimental result for modeling the patterns of human states in a living room.
    World Automation Congress (WAC), 2012; 01/2012
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    ABSTRACT: Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.
    Proc SPIE 05/2011;
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    ABSTRACT: This paper proposes a human localization method in sensor networks for monitoring elderly people. First, we explain the proposed intelligent sensor networks. Next, we apply a spiking neural network to extract feature points for human localization from a measurement data by sensor networks. Furthermore, we propose a learning method using spiking neural network based on the time series of measurement data. Finally, we discuss the effectiveness of proposed method through experimental results in a living room.
    Robotic Intelligence In Informationally Structured Space (RiiSS), 2011 IEEE Workshop on; 01/2011
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    ABSTRACT: This paper propose a biometric personal identification method based on a pair of right and left sole pressure distribution change. We acquire the sole pressure distribution change by load distribution sensor and use it for a personal identification. We employ twelve features based on shape of footprint, and twenty seven features based on movement of weight during walking for each sole pressure data. We make these fuzzy if-then rules. We calculate a fuzzy degree of a pair of right and left sole pressure data for one person, and identify person by this fuzzy degree. We evaluated our method by five-hold cross validation method. The low false rejection and acceptance rates are evaluated from 20 to 90 persons.
    World Automation Congress (WAC), 2010; 10/2010
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    ABSTRACT: This paper proposes a biometric personal authentication method based on one step foot pressure distribution change. We acquire the foot pressure distribution change by mat type load distribution sensor and use it as a personal authentication. We employ twelve features based on shape of footprint, and twenty seven features based on movement of weight while walking. A classifier for each feature is developed on the basis of fuzzy inference. The classifier is trained by a clonal selection algorithm in artificial immune system. A personal authentication system for one step is made every classifier for all features. We employed 10 volunteers, and we took the step data five times. We evaluated our method by five-fold cross validation method. We obtained low false rejection and acceptance rates in identification and verification.
    FUZZ-IEEE 2010, IEEE International Conference on Fuzzy Systems, Barcelona, Spain, 18-23 July, 2010, Proceedings; 01/2010
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    ABSTRACT: The aim of this study was to clarify how the introduction of a 24-hour unconscious and unrestrained monitoring system in a nursing home affected the physical and mental functions of care staffs. This system was composed of an air pressure sensor system and an ultrasonic oscillosensor system. The intervened participants were ten care staffs (age 37.4+/-14.2) and nineteen elderly persons (age 85.6+/-8.9) in a nursing home, Osaka, Japan. We carried out a baseline survey at the end of September 2008, and the follow-up study was done at the end of October 2008. From the comparisons of the average scores of measurements between preand post-intervention using generalized linear model, the scores of "energy expenditure during diaper changing", "frequency of diaper changing", and "decreased work incentives" (P
    01/2010;
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    ABSTRACT: Personal Classification Based on Sole Pressure Changes
    IEEJ Transactions on Electronics Information and Systems 01/2010; 130:1953-1959.
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    ABSTRACT: This paper proposes a sensing system for a behavior detection system using an ultrasonic oscillosensor and an air pressure sensor. The ultrasonic oscillosensor sensor has a cylindrical tank filled with water. It detects the vibration of the target object from the signal reflected from the water surface. This sensor can detect a biological vibration by setting to the bottom bed frame. The air pressure sensor consists of a polypropylene sheet and an air pressure sensor, and detects the pressure information by setting under the bed's mattress. An increase (decrease) in the load placed on the bed is detected by the increase (decrease) in the pressure of the air held in the tube attached to the sheet. We propose a behavior detection system using both sensors, complementally. The system recognizes three states (nobody in bed, keeping quiet in bed, moving in bed) using both sensors, and we detect the behavior before getting out of bed by recognized these states. Fuzzy logic plays a primary role in the system. As the fundamental experiment, we applied the system to five healthy volunteers, the system successfully recognized three states, and detected the behavior before getting out of bed. As the clinical experiment, we applied the system to four elderly patients with dementia, the system exactly detected the behavior before getting out of the bed with enough time for medical care support.
    IEICE Transactions. 01/2010; 93-D:542-549.
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    ABSTRACT: This paper describes a method for distribution of people monitoring system using a 3D camera. This camera measures distance and intensity distribution based on time of flight (TOF). From the obtained distance data, we perform clustering processing to detect the distribution of people. In our method, fuzzy logic plays a primary role to decide the cluster, i.e., people number. Our proposed method applied for 120 sec in a room. We successfully detected distribution of people in a room for moving 3 people distribution. Consequently, our proposed method successfully detected distribution of people using automated cluster number determination.
    01/2010;
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    ABSTRACT: In this paper, we analyze human gait pattern and estimate her/his foot age. We acquire foot pressure distribution change as gait pattern by a mat type load distribution sensor. From the foot pressure distribution data, duration of gait cycle and center of foot pressure (CFP) changes are determined for each stride. We employ four estimation indexes such as step length, step CFP width, the time of double supporting period and distance of step CFP changes. We employ 87 volunteers, and divided them to young, middle age and elderly groups. By comparing of three groups, we found that elderly had shorter step length and larger step CFP width than young and middle age people. Besides, the double supporting time of the elderly was longer, and distance of step CFP changes was longer than those of young and middle age people. From these facts, sixteen fuzzy IF-THEN rules are made. We determine a fuzzy degree for her/his foot age by fuzzy MIN-MAX center-of-gravity method. In our experiment on 87 volunteers, we compared these results with regression method.
    Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 10-13 October 2010; 01/2010
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    ABSTRACT: Recently, the social role of the nursing health facilities increases more and more as Japan becomes an aged society. The health care should be done according to meals, daily exercise, sleep, and others. In this paper, we focus on the monitoring of sleep using optical oscillosensor and pneumatic s for the health care to elderly people. In these sensors, two thresholds are used for the state estimation of the human behavior on the bed. Therefore, we apply fuzzy membership functions to extract sensitive changes of sensory inputs, and spiking neural network for the state estimation of human behaviors on the bed. Next, we discuss the effectiveness of the proposed method through numerical experiments.
    01/2010;
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    ABSTRACT: This paper proposes a biometric personal authentication based on the pressure distribution while one step walking. We extract one step from a walk on a mat type load distribution sensor and use it to personal authentication. With this method, features which are based on weight movement and foot shape during walking are calculated, then a classifier is developed on the basis of fuzzy inference. We employed 30 volunteers. All volunteers are ranged from 20 to 85 years old. For each volunteer, we took walk data six times. Then, we evaluated this method by five training data and one test data. We obtained 6.1% EER (Equal Error Rate) and 13.9% FRR (False Rejection Rate) in verification (1:1 collation) and identification (1:N collation), respectively.
    Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on; 09/2009
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes a biometric personal authentication based on the pressure distribution while one step walking. We extract one step from a walk on a mat type load distribution sensor and use it to personal authentication. With this method, features which are based on weight movement and foot shape during walking are calculated, then a classifier is developed on the basis of fuzzy inference. We employed 30 volunteers. All volunteers are ranged from 20 to 85 years old. For each volunteer, we took walk data six times. Then, we evaluated this method by five training data and one test data. We obtained 6.1% EER (Equal Error Rate) and 13.9% FRR (False Rejection Rate) in verification (1:1 collation) and identification (1:N collation), respectively.
    Proceedings of the 18th international conference on Fuzzy Systems; 08/2009
  • Y. Hata, S. Kobashi, K. Taniguchi, H. Nakajima
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    ABSTRACT: This paper describes a human health monitoring system by an ultrasonic sensor and an mat sensor systems. The system is realized with constrain-free, low cost and bed-side usage applicable. In it the ultrasonic sensor system obtains the state of a patient in bed by placing it under a bed frame. The mat sensor system detects heart beats and respiration signals by placing it to the mattress on the bed. This means that we can measure autonomic nerve system by using the heart rate and contribute the diagnosis of sleep apnea. This system employs fuzzy logic techniques to detect them. Thus, the system of systems with fuzzy logic can noninvasively and unconsciously provide human health information with high accuracy.
    Robotic Intelligence in Informationally Structured Space, 2009. RIISS '09. IEEE Workshop on; 05/2009
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    ABSTRACT: In this paper, we propose an approach to extract features of center of foot pressure (COP) obtained by a load distribution sensor and apply this method to develop a biometrics personal identification system. Biometrics technology, as a method of personal identification, plays an important role in our daily lives. In our experiment, we have a user stand on load distribution sensor with slipper, and acquire pressure data during a simple motion, as touching a bell nearby by one hand but without movements of feet. We propose a biometrics personal identification system with less information, time and low space. First, we calculate the site of COP from the obtained pressure data. Features for identification are extracted from the position and the movement of COP. Second, we built a k-out-of-n system and a neural network (NN) model with the feature parameter. Third, we input test data to the two systems. Finally, we give a comparison of these two methods. We employ 11 volunteers. The experimental result reveals that the proposed identification method can achieve an accuracy of 12.0% in FRR (False Rejection Rate) and 1.0% in FAR (False Acceptance Rate).
    ISMVL 2009, 39th International Symposium on Multiple-Valued Logic, 21-23 May 2009, Naha, Okinawaw, Japan; 01/2009
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    ABSTRACT: This paper describes a fuzzy processing method for a respiratory rate monitoring system by an optical fiber sensor. This optical fiber sensor has a part of sensor sheet and a polarization detector. It noninvasively detects vital signal of human on the sheet by obtaining polarization variations. By using this sensor, we propose a fuzzy processing method of a respiratory rate for human in the bed. Our method was tested on five volunteers. We successfully detected a respiratory rate. In it, fuzzy logic plays a primary role in the detection. Our system determines fuzzy membership functions by using characteristic of respiration. Consequently, this system can noninvasively detect a respiratory rate by using a constraint-free device.
    Automation Congress, 2008. WAC 2008. World; 11/2008
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    ABSTRACT: In this paper, we propose a personal identification by sole pressure change. We obtain sole pressure change of multiple steps by using two pressure sensor sheets. Each pressure sensor sheet is inserted into each shoe as an inner sock. Then, we extract characteristics of sole pressure change from the obtained data. We make template data of both feet from the extracted characteristics. We propose a Euclidean distance based method for personal identification. As the experimental result, we have recognized one of ten volunteers with over 90% accuracy.
    Automation Congress, 2008. WAC 2008. World; 11/2008
  • N. Kubota, H. Kojima, K. Taniguchi, T. Sawayama
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    ABSTRACT: Recently, the social role of the nursing health facilities increases more and more as Japan becomes an aged society. There has been a problem on accidents such as ldquofallingrdquo and ldquomoving away from facilitiesrdquo in such facilities owing to the overload to the human monitoring and the privacy protection. This sensor can detect the state such as turning over in bed and getting up on the bed. The parameters should be updated automatically according to the monitoring results. In the ultrasonic oscillosensor, two thresholds are used for the state estimation of the human behavior on the bed. Therefore, these thresholds should be automatically updated according to some criteria. In this paper, we apply a fuzzy spiking neural network for the state estimation of the human behaviors on the bed, and a steady-state genetic algorithm for updating the thresholds based on the correctness of the state estimation. Next, we discuss the effectiveness of the proposed method through several numerical experiments.
    Automation Congress, 2008. WAC 2008. World; 11/2008

Publication Stats

118 Citations
1.07 Total Impact Points

Institutions

  • 2006–2010
    • University of Hyogo
      • Graduate School of Engineering
      Kōbe, Hyōgo, Japan
  • 2008
    • Osaka University
      • Immunology Frontier Research Center
      Ōsaka-shi, Osaka-fu, Japan
  • 2005–2008
    • Tokyo Metropolitan University
      • Department of Mechanical Engineering
      Tokyo, Tokyo-to, Japan
  • 2000–2003
    • Himeji Institute of Technology
      • Department of Computer Engineering
      Himezi, Hyōgo, Japan
    • Osaka Institute of Technology
      Ōsaka, Ōsaka, Japan
  • 2001
    • Fukui University
      Hukui, Fukui, Japan