• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.5
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• pp.349-357
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• 2015

The piezoelectric paint sensor is a paint type sensor comprising of an epoxy and piezoelectric powder, which is the main component of a piezoelectric material. This sensor can be easily attached to any type of structure as compared to other sensors because it is viable to directly apply it on structures, as in the case with a typical paint. In this study, the capability of piezoelectric paint sensor for impact detection was evaluated. In Particular, the applications of the piezoelectric paint sensor for railroad vehicles were considered. There have been various cases reported about the damages caused by flying gravel to the under-cover of the railroad vehicle during operation. In order to prevent this, real-time monitoring of the large under-cover surface of the railroad vehicle is unavoidable. Under the assumption of vehicle application, sensor sensitivities were measured after multiple and prolonged exposure to thermal cycle environment $-20{\sim}60^{\circ}C$). Sensitivity evaluation of paint sensor under environmental conditions was conducted in an aluminum specimen. In results, despite the small variations in sensitivity, we could confirm the applicability of this paint sensor for impact detection even after a severe environmental exposure test.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.881-887
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• 2015

The hardware-in-the-loop simulation (HILS) of a railway vehicle is crucial for overcoming the limitation of field tests of a railway vehicle. A brake HILS system for a railway vehicle was previously not able to test the performance of a speed-sensing system of a railway vehicle, since wheelset speeds were generated only by computer simulations. In this paper, we present a novel wheelset speed implementation of a brake HILS system for a railway vehicle. Four wheelset speeds of a brake HILS system for a car of a railway vehicle are implemented using four small-sized servomotors, whereas the speed sensors and pole wheels used in the brake HILS system are the actual ones of the railway vehicle. According to the simulated speeds of four wheelsets in the dynamic equations of motion, four servomotors generate wheel speeds in real time, and then the measured wheelset speeds are fed back to the computer simulation model. Moreover, in this paper, we improve the performance of wheelset speed measurement via the T method instead of the M method presently used in the field. The performances of wheelset speed implementation and speed-sensor operation are demonstrated by experimental works using a HILS system.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.139-144
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• 2016

This paper provides an overview of recent trends in sensor fusion technologies for vehicle positioning. The GNSS by itself cannot satisfy precision and reliability required by autonomous driving. We survey sensor fusion techniques that combine the outputs from the GNSS and the inertial navigation sensors such as an odometer and a gyroscope. Moreover, we overview landmark-based positioning that matches landmarks detected by a lidar or a stereo vision to high-precision digital maps.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.58-62
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• 2008

The need for position information in agriculture is gradually increasing for precise control farm vehicle and effective manage farm land. Though geomagnetic sensor has a lot of merits in estimating heading angle of vehicle because of low costs and sensing ability of magnetic north, it is easy that sensor outputs are distorted in electro magnetic field environment. This study was conducted to develop geomagnetic compass which could be available in measuring relative position from reference point correcting output distorted by external electro magnetic field in a small scale field. Magnetic inducing sensor (PNI's Vector2X) which wound enamel coated copper coil on ferrite core in order to measure and correct earth magnetic field. Magnetic azimuth was corrected using the algorithm which estimated amount of magnetic distortion from the difference between each outputs of magnetic sensors that located on the cross shaped base. Developed auto-tuning magnetic sensor was showed less then 5% as bearing accuracy in the strong magnetic field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.915-916
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• 2015

The main sensor of AGV is the guide sensor in order to detect the path, and the sensor consists of 8 or 16-magneto resistance devices arranged by with 10mm. In controlling the AGV posture by using the sensor, AGV is occurred left/right shaking frequently. So, for driving stability of AGV, An accuracy of the sensor should be improved. Therefore, this study proposed sensor signal processing method to improve accuracy of guide sensor, and implemented. The accuracy of sensor in experimentation showed 2.84[mm]. In designing the sensor for controlling AGV posture, the proposed method will be effective.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.182-189
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• 1999

In this paper, we present an ultrasonic sensor based path planning method using fuzzy logic for obstacle avoidance of an intelligent vehicle in unknown environments. Generally, Robot navigation in unknown terrains is a very complex task difficult to control because of the great amount of imprecise and ambiguous sensor information that has to be considered. In this case, fuzzy logic can satisfactorily deal with such information in quite efficient manner. In this study, we propose two fuzzy logic controller which is composed of steering controller and velocity controller respectively. Our object is to develop a fuzzy controller that can enable a mobile robot to navigate from a start point to a goal point without collisions, in the least possible travel time. The ability and effectiveness for the proposed algorithm will be demonstrated by simulation and expeiment.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.215-225
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• 2020

Light detection and ranging (LiDAR) sensors detect the distance of the surrounding environment and objects. Conventional LiDAR sensors require a certain amount of a power because they detect objects by transmitting lasers at a regular interval depending on a constant resolution. The constant power consumption from operating multiple LiDAR sensors is detrimental to autonomous and electric vehicles using battery power. In this paper, we propose two algorithms that improve the inefficient power consumption during the constant operation of LiDAR sensors. LiDAR sensors with algorithms efficiently reduce the power consumption in two ways: (a) controlling the resolution to vary the laser transmission period (T_P) of a laser diode (LD) depending on the vehicle's speed and (b) reducing the static power consumption using a sleep mode depending on the surrounding environment. A proposed LiDAR sensor with a resolution control algorithm reduces the power consumption of the LD by 6.92% to 32.43% depending on the vehicle's speed, compared to the maximum number of laser transmissions (N_x·max). The sleep mode with a surrounding environment-sensing algorithm reduces the power consumption by 61.09%. The proposed LiDAR sensor has a risk factor for 4-cycles that does not detect objects in the sleep mode, but we consider it to be negligible because it immediately switches to an active mode when a change in surrounding conditions occurs. The proposed LiDAR sensor was tested on a commercial processor chip with the algorithm controlling the resolution according to the vehicle's speed and the surrounding environment.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.411-417
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• 2022

In this paper, we propose a sensor with a C-shaped load cell to detect force change when a person sitting on the chair in an electrical transport-convenience vehicle is pushing ground by both heels. The load cell built in the vehicle is mechanically deformed by the vertical force owing to the human weight and the horizontal force by ground-pushing feet. The deformation rate of the load cell and its distribution are simulated using finite element analysis. In the simulation, the applied loads are preset in the range of 10 kg - 100 kg with a step size of 10 kg, and the ground-pushing force by feet is increased to 40 N with a step size of 5 N with respect to each applied load level. The resistance change of the load cell was observed to be linear in simulation as well as in measurement. the maximum difference between simulation and measurement was 0.89 % when the strain gauge constant was 2.243. The constant has a large influence on the difference. The proposed sensor was fabricated by connecting an instrument amplifier and a microcontroller to a load cell and used to detect the force by ground-pushing feet. To detect foot driving, the reference signal was set to 130% of the load, and the duration of the sensor output signal exceeding the reference signal was set to 0.6 s. In a test of a vehicle built with the proposed sensor, the footpushing force by the worker could be successfully detected even when the worker was working.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.135-138
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• 2009

G-Sensor is being used for controlling motion of smart-phone and robot. G-Sensor can control motion to several direction, because it is composed of X, Y, and Z axis and also can be used on many mobile-phone by using Wi-Fi communication and RS-232C communication on the Bluetooth module. In this research, we suggest the application that realize and develop visual-vehicle-remote-control-system by using mobile-phone with G-Sensor so that drivers can more easily remote control and manage their vehicle with mobile-phone in real-time visual.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.121-129
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• 2015

This paper presents a multiple vehicle recognition algorithm based on radar and vision sensor fusion for lane change assistance. To determine whether the lane change is possible, it is necessary to recognize not only a primary vehicle which is located in-lane, but also other adjacent vehicles in the left and/or right lanes. With the given sensor configuration, two challenging problems are considered. One is that the guardrail detected by the front radar might be recognized as a left or right vehicle due to its genetic characteristics. This problem can be solved by a guardrail recognition algorithm based on motion and shape attributes. The other problem is that the recognition of rear vehicles in the left or right lanes might be wrong, especially on curved roads due to the low accuracy of the lateral position measured by rear radars, as well as due to a lack of knowledge of road curvature in the backward direction. In order to solve this problem, it is proposed that the road curvature measured by the front vision sensor is used to derive the road curvature toward the rear direction. Finally, the proposed algorithm for multiple vehicle recognition is validated via field test data on real roads.