• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.316-321
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• 2005

We designed and implemented fish robots for various purposes such as autonomous navigation, maneuverability control, posture balancing and improvement of quick turns in a tank of 120 X 120 X 180cm size. Typically, fish robots have 30-50 X 15-25 X 10-20cm dimensions; length, width and height, respectively. It is essential to have the ability of quick and smooth turning to avoid collision with obstacles or walls of the water pool at a close distance. Infrared distance sensors are used to detect obstacles, magneto-resistive sensors are used to read direction information, and a two-axis accelerometer is mounted to compensate output of direction sensors. Because of the swing action of its head due to the tail fin movement, the outputs of an infrared distance sensor contain a huge amount of noise around true distances. With the information from accelerometers and e-compass, much improved distance data can be obtained by fuzzy logic based estimation. Successful swimming and smooth turns without collision demonstrated the effectiveness of the distance estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1986-1991
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• 2007

In this paper, we discussed, intelligent soft filter for MR(magnetoresistive) sensor. Most navigation systems today use some type of compass to determine heading direction. Using the earth's magnetic field, electronic compass based on MR(magnetoresistive) sensors can electrically resolve better then 0.1 degree rotation. Intelligent methode for soft building a one degree compass using MR(magnetoresistive) sensors will also be discussed. Compensation techniques are shown to correct for compass tilt angels and nearby ferrous material disturbances. we proved the fuzzy logic that based on the way the ham deals with inexact information is useful for MR sensors.

• Journal of the Korean Magnetics Society
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• v.5 no.4
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• pp.294-298
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• 1995

A shielded magnetoresistive (SMR) head which has double MR films and linearizes each other has been designed and studied by applying the transmission-line model. We have analyzed the yoke efficiency, bias efficiency and read efficiency of the SMR head. The read efficiency strongly depends on the height of the sensor and slightly on the other geometric parameters. The yoke and bias efficiencies vary with gap length, insulated layer thickness and relative permeability. A quasi-index reduction in the signal flux is observed when the displacement moves away from the medium.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.911-912
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• 2018

금속탐지 센서는 공항, 접경지역 같은 다양한 보안 분야 등에서 널리 사용하고 있는 센서이다. 이러한 금속탐지 센서는 홀 현상을 이용하는가, 맴돌이 전류를 색출하여 탐지하는가 등에 따라 방식이 나뉘게 된다. 본 논문에서는 이러한 방식들 중 미세한 저항의 변화를 탐지하는 이방성 자기저항을 이용하여 다른 방식의 센서들보다 전력소보가 적고 정교한 탐지가 가능하도록 하였다. 또한 센서의 축을 늘려 더욱 넓은 원형범위에서 금속을 탐지할 수 있게 하였다.

• Journal of Sensor Science and Technology
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• v.10 no.5
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• pp.329-336
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• 2001

This paper describes the steering control and geomagnetism cancellation for an autonomous vehicle using an MR sensor. The magneto-resistive (MR) sensor obtains the vector summation of the magnetic fields from embedded magnets and the Earth. The vehicle is controlled by the magnetic fields from embedded magnets. So, geomagnetism is the disturbance in the steering control system. In this paper, we propose a new method of the sensor arrangement in order to remove the geomagnetism and vehicle body interference. The proposed method uses two MR sensors located in a level plane and the steering controller has been developed. The controller has three input variables ($dB_x$, $dB_y$, $dB_z$) using the measured magnetic field difference, and an output variable (the steering angle). A simulation program was developed to acquire the data to teach the neural network, in order to test the ability of a neural network to learn the steering control process. Also, the computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. From the simulation and field test, good result was obtained and we confirmed the robustness of the neural network controller in a real autonomous vehicle.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.213-221
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• 2005

An insole type local shear force measurement system was developed and local shear stresses in the foot were measured during level walking. The shear force transducer based on the magneto-resistive principle, was a rigid 3-layer circular disc. Sensor calibrations with a specially designed calibration device showed that it provided relatively linear sensor outputs. Shear transducers were mounted on the locations of four metatarsal heads and heel in the insole. Sensor outputs were amplified, decorded in the bluetooth transmission part and then transferred to PC. In order to evaluate the developed system, both shear and plantar pressure measurements, synchronized with the three-dimensional motion analysis system, were performed on twelve young healthy male subjects, walking at their comfortable speeds. The maximum peak pressure during gait was 5.00kPa/B.W at the heel. The time when large local shear stresses were acted correlated well with the time of fast COP movements. The anteroposterior shear was dominant near the COP trajectory, but the mediolateral shear was noted away from the COP trajectory. The vector sum of shear stresses revealed a strong correlation with COP movement velocity. The present study will be helpful to select the material and to design of foot orthoses and orthopedic shoes for diabetic neuropathy or Hansen disease.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.281-286
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• 2000

This paper describes a lateral guidance system of an unmanned vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in the unmanned vehicle simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the learning pattern, learning itself, and the adequacy of the design controller. A computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. Good results were obtained. Also, the real unmanned electrical vehicle using neural network controller verified good results.

• Journal of Sensor Science and Technology
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• v.10 no.2
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• pp.134-141
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• 2001

This paper describes a method to steer an autonomous electric vehicle using magnetic fields. Magnets are embeded along the center of the road and a magneto-resistive sensor is mounted beneath the front bumper of the vehicle. As the vehicle moves along the road neural network controller controls the vehicle using measured magnetic field variation. Based on a single magnets modeling equation, we analyzed three dimensional magnetic field distributions of embeded magnets in series on the center of the road and performed a computer simulation using this results. In simulation study, straight and curved road was configured. The steering controller for the vehicle was designed using neural network and experiment was performed on the real embeded magnets using real autonomous electric vehicle. At the experiment we compensated the earth's magnetic fields and showed a good result driving an autonomous vehicle using proposed method.