• International Journal of Precision Engineering and Manufacturing
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• v.4 no.1
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• pp.43-48
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• 2003

A linear laser-vision sensor called ‘Perception TriCam Contour' is mounted on an industrial robot and often used for various application of the robot such as the position correction and the inspection of a part. In this paper, a sensor center position calibration is presented for the most accurate use of the robot-Perceptron system. The obtained algorithm is suitable for on-site calibration in an industrial application environment. The calibration algorithm requires the joint sensor readings, and the Perceptron sensor measurements on a specially devised jig which is essential for this calibration process. The algorithm is implemented on the Hyundai 7602 AP robot, and Perceptron's measurement accuracy is increased up to less than 1.4mm.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.176-182
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• 1999

This paper presents a new autonomous kinematic calibration technique by using a laser-vision sensor called "Perceptron TriCam Contour". Because the sensor measures by capturing the image of a projected laser line on the surface of the object, we set up a long, straight line of a very fine string inside the robot workspace, and then allow the sensor mounted on a robot to measure the point intersection of the line of string and the projected laser line. The point data collected by changing robot configuration and sensor measuring are constrained to on a single straght line such that the closed-loop calibration method can be applied. The obtained calibration method is simple and accurate and also suitable for on-site calibration in an industrial environment. The method is implemented using Hyundai VORG-35 for its effectiveness.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.352-361
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• 2018

Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.639-645
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• 2006

FRP (Fiber Reinforced Polymer) is an excellent new constructional material in resistibility to corrosion, high intensity, resistibility to fatigue, and plasticity. FBG (Fiber Bragg Grating) sensor is widely used at present as a smart sensor due to lots of advantages such as electric resistance, small-sized material, and high durability. However, with insufficiency of measuring displacement, FBG sensor is used only as a sensor measuring physical properties like strain or temperature. In this study, FRP and FBG sensors are to be hybridized, which could lead to the development of a smart FRP rod. Moreover, developing the estimated model for deflection with neural network method, with the data measured through FBG sensor, could make conquest of a disadvantage of FBG sensor - uniquely used for sensing strain. Artificial neural network is MLP (Multi-layer perceptron), trained within error rate of 0.001. Nonlinear object function and back-propagation algorithm is applied to training and this model is verified with the measured axial displacement through UTM and the estimated numerical values.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.69-77
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• 1993

Automatic image recognition system is essential for a better man-to machine interaction. Because of the noise and deformation due to the sensor operation, it is not simple to build an image recognition system even for the fixed images. In this paper neural network which has been reported to be adequate for pattern recognition task is applied to the fixed and variational(rotation, size, position variation for the fixed image)recognition with a hope that the problems of conventional pattern recognition techniques are overcome. At fixed image recognition system. ART model is trained with face images obtained by camera. When recognizing an matching score. In the test when wigilance level 0.6 - 0.8 the system has achievel 100% correct face recognition rate. In the variational image recognition system, 65 invariant moment features sets are taken from thirteen persons. 39 data are taken to train multi-layer perceptron and other 26 data used for testing. The result shows 92.5% recognition rate.

• Journal of Sensor Science and Technology
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• v.11 no.4
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• pp.239-246
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• 2002

In this paper a new design of multi-layer perceptron(MLP) for camera calibration is proposed. Most existing techniques determine a transformation from 3D spatial points to their image points and camera parameters are tried to be estimated from the transformation. The technique proposed here, on the other hand, determines rays of sight uniquely from image points and parameters are estimated from the relationship using an MLP. By this approach projection and back-projection can be done more straightforwardly. Being based on a geometric model, a network design process becomes less ambiguous, which is a clear merit compared to other neural net based techniques. An MLP designed according to the technique proposed showed fast and stable learning in tests under various conditions.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.920-922
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• 2018

This paper describes a method to classify simple circular artificial markers on surfaces of a box on the back of hand to detect the pose of user's hand for VR/AR applications by using a Leap Motion camera and two IMU sensors. One IMU sensor is located in the box and the other IMU sensor is fixed with the camera. Multi-layer Perceptron (MLP) algorithm is adopted to classify artificial markers on each surface tracked by the camera using IMU sensor data. It is experimented successfully in real-time, 70Hz, under PC environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.23-29
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• 2012

Injured peoples usually care their body at medical institutions. But if they need some more rehabilitation to the affected area thus exist. These medical institutions according to the scale there are significant differences in rehabilitation programs, most of the small-scale rehabilitation program for medical doctors and patients to be progression of the conversation is an issue. In this paper, in a small medical facility rehabilitation to assist in the accuracy and reliability, physical contact and force sensors that can measure a combination of substitution and the tactile sensor and tactile sensor alternative with a similar function is proposed. Perceptron neural networks by applying the contact evaluation according to the algorithm to determine the pattern is applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.360-364
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• 2009

현대사회는 인구의 고령화에 따른 노인인구의 증가 및 만성질환자의 증가에 따른 의료수요 급증이 예상되고 있다. 하지만 현재의 의료서비스 인프라는 증가하는 의료수요를 충족하기에는 역부족이 따르며, 이러한 문제점을 해결하기위해 정보통신기술과 헬스케어기술이 결합된 유비쿼터스 헬스케어기술이 부각되고 있다. 본 연구에서는 일상생활 중 움직임에 따른 활동 상태를 판별하여 운동량의 모니터링을 통한 건강관리뿐만 아니라 낙상 등과 같은 응급상황의 모니터링이 가능한 시스템을 구현하고자 하였다. 이를 위하여 3축 가속도센서를 이용하여 인체의 움직임에 따른 활동 가속도 신호를 계측할 수 있는 센서 및 시스템을 구현하였다. 또한 계측된 센서신호를 PC또는 휴대용 단말기로 무선전송하기위하여 무선센서네트워크 기술을 적용한 데이터 전송시스템을 구현하였다. 계측된 가속도 신호로부터 활동 상태를 판별하기위해 다층 퍼셉트론 알고리즘을 적용한 분류알고리즘을 제안하였으며, 분류알고리즘의 성능평가를 통해 실제 활동상태 모니터링에 적용 가능함을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.210-210
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• 2000

In this paper, we have investigated the properties of multi-layer perceptron (MLP) for odour patterns classification and concentration estimation simultaneously. When the MLP may be has a fast convergence speed with small error and excellent mapping ability for classification, it can be possible to use for classification and concentration prediction of volatile chemicals simultaneously. However, the conventional MLP, which is back-Propagation of error based on the steepest descent method, was difficult to use for odour classification and concentration estimation simultaneously, because it is slow to converge and may fall into the local minimum. We adapted the Levenberg-Marquardt(LM) algorithm [4,5] having advantages both the steepest descent method and Gauss-Newton method instead of the conventional steepest descent method for the simultaneous classification and concentration estimation of odours. And, We designed the artificial odour sensing system(Electronic Nose) and applied LM-BP algorithm for classification and concentration prediction of VOC gases.