| Co-Investigator(Kenkyū-buntansha) |
IZUMI Takashi Nihon University, College of Science and Technology, Professor, 理工学部, 教授 (30120372)
FUKUDA Atsushi Nihon University, College of Science and Technology, Professor, 理工学部, 教授 (90208950)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2006: ¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 2005: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Research Abstract |
In recent years, development of the wireless communication technology is able to provide useful information and active safety for the drivers. Though many researchers try to monitor a traffic congestion, weather condition, road surface condition and car maintenance by the on-board TV camera or the probe car system, sensor signals of these have non-stationary and transitory characteristics while the vehicle is in motion. Structural health monitoring is used for this purpose. In structural health monitoring, the response signals from sensors built into a structure are used to monitor the condition of the structure. When strain, vibration, sound and infrared sensors are used for monitoring real structures, they are generally affected by environmental and load changes often resulting in a nonlinear relationship between phenomena and the response signals. Since the response signals from a vehicle-like structure that continually experiences vibration contain information about the vehicle's o
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perating conditions and environment, a highly accurate technique for monitoring and analyzing the vehicle's structure is required to interpret changes in the response signal accurately.
In order to predict the occurrence of phenomena we need to create a model that relates the response signals to the phenomena. Strong generalization capabilities are required for highly accurate predictions. General learning tools can be used for modeling. The learning processes employed by these techniques enable the creation of a complicated nonlinear model, but they require defining the various learning parameters appropriately. Even a model capable of classifying learning data correctly may yield only a local solution. In order to obtain a model that has great generalizability, the learning parameters must be determined by trial and error.
In this study, therefore, the Support Vector Machines method that uses a statistical technique for learning and estimating is applied to structural health diagnosis. Since the method for creating a model of great generalizability based on a theory of statistical learning is clearly an optimization problem, SVM is expected to give highly accurate prediction of phenomena. We investigate and evaluate SVM by applying it to the vibration response, which is dependent on various factors, and using it to diagnose the structural health of a vehicle. Less
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