• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.1-11
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• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.6
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• pp.19-29
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• 2007

This paper introduces a robot knowledge framework which is represented with multiple classes, levels and layers to implement robot intelligence at real environment for mobile robot. Our root knowledge framework consists of four classes of knowledge (KClass), axioms, rules, a hierarchy of three knowledge levels (KLevel) and three ontology layers (OLayer). Four KClasses including perception, model, activity and context class. One type of rules are used in a way of unidirectional reasoning. And, the other types of rules are used in a way of bi-directional reasoning. The robot knowledge framework enable a robot to integrate robot knowledge from levels of its own sensor data and primitive behaviors to levels of symbolic data and contextual information regardless of class of knowledge. With the integrated knowledge, a robot can have any queries not only through unidirectional reasoning between two adjacent layers but also through bidirectional reasoning among several layers even with uncertain and partial information. To verify our robot knowledge framework, several experiments are successfully performed for object recognition and navigation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.101-109
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• 2014

It is an essential element for the establishment of image processing related systems to find the exact contour from the image of an arbitrary object. In particular, if a vision system is established to inspect the products in the automated production process, it is very important to detect the contours for standardized shapes such lines and curves. In this paper, we propose a prototype adaptive dynamic contour model, p-Snake with improved contour extraction algorithms by adding the prototype energy. The proposed method is to find the initial contour by applying the existing Snake algorithm after Sobel operation is performed for prototype analysis. Next, the final contour of the object is detected by analyzing prototypes such as lines and circles, defining prototype energy and using it as an additional energy item in the existing Snake function on the basis of information on initial contour. We performed experiments on 340 images obtained by using an environment that duplicated the background of an industrial site. It was found that even if objects are not clearly distinguished from the background due to noise and lighting or the edges being insufficiently visible in the images, the contour can be extracted. In addition, in the case of similarity which is the measure representing how much it matches the prototype, the prototype similarity of contour extracted from the proposed p-ACM is superior to that of ACM by 9.85%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.366-373
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• 2017

In this paper, we propose a variable weighted average (VWA) learning method in order to improve the performance of the fuzzy ART neural network that has been developed by Grossberg. In a conventional method, the Fast Commit Slow Recode (FCSR), when an input pattern falls in a category, the representative pattern of the category is updated at a fixed learning rate regardless of the degree of similarity of the input pattern. To resolve this issue, a variable learning method proposes reflecting the distance between the input pattern and the representative pattern to reduce the FCSR's category proliferation issue and improve the pattern recognition rate. However, these methods still suffer from the category proliferation issue and limited pattern recognition rate due to inevitable excessive learning created by use of fuzzy AND. The proposed method applies a weighted average learning scheme that reflects the distance between the input pattern and the representative pattern when updating the representative pattern of a category suppressing excessive learning for a representative pattern. Our simulation results show that the newly proposed variable weighted average learning method (VWA) mitigates the category proliferation problem of a fuzzy ART neural network by suppressing excessive learning of a representative pattern in a noisy environment and significantly improves the pattern recognition rates.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.18-18
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• 2010

전기적 에너지를 기계적 에너지로 변환하고 또한, 기계적 에너지를 전기적 에너지로 변환할 수 있는 압전 세라믹스는 압전 변압기 (piezoelectric transformer), 초음파 모터, 센서 등과 같은 응용분야에 광범위하게 사용되고 있다. 특히, 전원장치에 있어서 현재 주요 전자제품에 사용되고 있는 권선형 변압기와 같은 전자 변환기의 대체품으로 압전 세라믹스 소재의 특성을 이용한 압전변압기의 개발과 응용연구는 국내외적으로 활발히 연구되어왔다. 압전변압기는 권선형 변압기와 비교 하였을 때 누설자속이 없어 노이즈 발생이 없고, 공진주파수만을 이용하므로 출력의 파형이 정현파에 가까워 고조파 잡음이 없으며, 불연성의 특징을 가지고 있다. 추가적으로 압전 변압기는 소형화, 슬림화, 경량화가 가능하며 90%이상의 높은 효율을 얻을 수 있다. 또한, 단판형 압전변압기의 출력한계를 개선하기 위해 높은 승압비와 고출력을 갖는 적층타입의 압전변압기가 제안되었다. 압전변압기용 조성 세라믹스는 높은 에너지 변환효율을 위해서 전기기계결합계수 ($k_p$)가 커야 하며, 발열에 의한 온도 상승을 억제하기 위하여 기계적 품질계수(Qm)가 큰 것이 바람직하다. 또한, 높은 전류를 발생하기 위해서는 유전상수가 커 압전변압기의 출력측 정전용량을 크게 하여야한다. 이러한 압전변압기의 제작 조건을 위해 우수한 압전 및 유전특성을 갖는 PZT계 세라믹스가 주로 사용 되어져 왔다. 그러나, PZT계 세라믹스의 우수한 압전 및 유전특성에도 불구하고 $1000^{\circ}C$에서 급격히 휘발하는 PbO의 성질 때문에 환경적으로나 인체의 건강문제로 인해 전세계적으로 그 사용량을 제한하고 있다. 또한 적층 압전변압기의 구조적 특성상 내부전극과 함께 소결하여야 하는데, 이때 소결온도가 높으면 값비싼 Pd합량이 높은 전극을 사용하여야 한다. Pd함량이 10%미만인 Ag/Pd 전극을 사용하기 위해서는 $950^{\circ}C$ 이하에서 저온소결이 가능한 세라믹스 제조가 필수적이라 할 수 있다. 소결온도를 낮추는 방법으로는 다른 물질들을 치환하여 소결온도를 낮추는 방법과 미세분말을 만들어 그레인사이즈를 미세화 하는 방법들이 있다. 많은 미세 분말 제조 방법 중에서 Attrition mill은 일반적인 ball mill에 비해 분말의 입도를 미세하게 할 수 있어 증가된 분말의 비표면적에 의하여 반응을 촉진시킴으로써 저온소결이 가능한 세라믹스를 만들 수 있다. 따라서 본 연구에서는 소결온도가 낮으면서도 유전 및 압전특성이 우수한 조성을 사용하여 적층 압전변압기를 제작하여 전기적 특성을 조사하였다.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.87.3-88
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• 2017

한국천문연구원은 차세대소형위성 1호의 근적외선 영상분광기 NISS (Near-infrared Imaging Spectrometer for Star formation history) 탑재체를 개발하여 2017년 6월 30일에 최종 비행모델을 납품하였고, 이 발표는 탑재체 NISS 구조체의 비행모델 개발 결과를 보고한다. NISS는 0.9 - 2.5um (R~20) 근적외선 파장에서 관측을 해야 하기 때문에, 구조체의 배경잡음을 없애기 위해서 200K까지 passive cooling으로 냉각되며, H2RG 검출기는 소형 냉동기에 의해 약 88K에서 운영된다. NISS 구조체의 passive cooling을 효율적으로 수행하기 위해서 방열판, Kevlar 지지대, MLI, 표면제어용 필름 등을 조립하였고, 실제 지상 시험을 통해서 그 성능을 확인하였다. NISS 구조체는 최종 시스템 조립 과정에서 전자부 하네스 조립을 함께 수행했으며, 온도 모니터링 센서를 부착하고 소형 냉동기 피드백 온도를 반복 시험을 통해서 결정하였다. NISS 구조체는 미러 및 렌즈를 지지하는 광기계부를 함께 포함하기 때문에 발사 및 우주환경에서 광학 성능을 유지하기 위한 설계를 거쳐서 제작 되었으며, 최종 시스템 검교정 시험, 진동 및 열진공 시험을 통해서 그 성능을 확인하였다. NISS를 탑재한 차세대소형위성 1호는 2018년 상반기에 미국의 Falcon 9 발사체에 실려서 발사될 예정이다.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.97-107
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• 2017

In the field of automatic door, the infrared rays and microwave sensor are much used as the important components in charge of the motor's operation control of open and close through the incoming signal of object recognition. In case of existing system that the sensor of the infrared rays and microwave are applied to the automatic door, there are many malfunctions by the infrared rays and visible rays of the sun. Because the automatic doors are usually installed outside of building in state of exposure. The environmental change by temperature difference occurs the noise of object recognition detection signal. With this problem, the hardware fault that the detection sensor is unable to follow the object moving rapidly within detection area makes the sensing blind spot. This fault should be improved as soon as possible. Because It influences safety of passengers who use the automatic doors. This paper conducted an experiment to improve the detection area by installing extra ultrasonic sensor besides existing detection sensor. So, this paper realize the computing circuit and detection algorithm which can correctly and rapidly process the access route of objects moving fast and the location area of fixed obstacles by applying detection and advantages of ultrasonic signal to the automatic doors. With this, It is proved that the automatic door applying ultrasonic sensor is improved detection area of blind spot sensing through field test and improvement plan is proposed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.77-84
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• 2012

Face classification has wide applications in intelligent video surveillance, content retrieval, robot vision, and human-machine interface. Pose and expression changes, and arbitrary illumination are typical problems for face recognition. When the face is captured at a distance, the image quality is often degraded by blurring and noise corruption. This paper investigates the efficacy of multi-classifier decision level fusion for face classification based on the photon-counting linear discriminant analysis with two different cost functions: Euclidean distance and negative normalized correlation. Decision level fusion comprises three stages: cost normalization, cost validation, and fusion rules. First, the costs are normalized into the uniform range and then, candidate costs are selected during validation. Three fusion rules are employed: minimum, average, and majority-voting rules. In the experiments, unfocusing and motion blurs are rendered to simulate the effects of the long distance environments. It will be shown that the decision-level fusion scheme provides better results than the single classifier.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.6-14
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• 2011

The fast growing of the number of users requires the development of reliable communication systems able to provide higher data rates. In order to meet those requirements, techniques such as Multiple Input Multiple Out (MIMO) and Orthogonal Frequency Division multiplexing (OFDM) have been developed in the recent years. In order to combine the benefits of both techniques, the research activity is currently focused on MIMO-OFDM systems. In addition, for a fast wireless channel environment, the data rate and reliability can be optimized by setting the modulation and coding adaptively according to the channel conditions; and using sub-carrier frequency, and power allocation techniques. Depending on how accurate the feedback-based system obtain the channel state information (CSI) and feed it back to the transmitter without delay, the overall system performance would be poor or optimal. In this paper, we propose a Signal to Noise Ratio (SNR) estimation algorithm where the preamble is known for both sides of the transciever. Through simulations made over several channel environments, we prove that our proposed SNR estimation algorithm is more accurate compared with the traditional SNR estimation. Also, We applied AMC on several channel environments using the parameters of IEEE 802.11n, and compared the Throughput performance when using each of the different SNR Estimation Algorithms. The results obtained in the simulation confirm that the proposed algorithm produces the highest Throughput performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.10-21
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• 2008

The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.