• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.141-144
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• 2009

인터넷에 게시된 글(블로그, 포털 사이트, 게시판 등)에 대한 댓글들 중에서 중복, 광고 등의 스팸성 댓글을 추적하고 이를 필터링 하는 시스템을 개발.

• Journal of the Korea Computer Graphics Society
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• v.11 no.1
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• pp.8-12
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• 2005

본 연구에서는 영상에 간단한 공간 필터를 적용하여 연필 해칭 효과를 갖는 영상을 만들어 내는 방법을 제안한다. 해칭 스타일의 톤 생성을 위하여 모션 블러링을 이용해서 입력 영상에 방향성을 주고, 샤프닝과 블러링으로 연필 해칭 효과를 만들어 낸다. 이렇게 만들어진 영상은 영상 전체에 같은 방향으로 해칭한 것 같은 효과를 가진다. 모션 블러링을 각기 다른 방향으로 적용한 영상들을 합성하면 크로스 해칭의 효과를 만들 수 있다. 여기에 소벨 필터를 사용해서 원본 영상의 에지를 검출해서 함께 합성하여 해칭을 이용한 연필화를 생성한다.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1530-1537
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• 2007

In this paper, we implemented a speech emotion recognition system that can distinguish human emotional states from recorded speech captured by a single microphone and classify them into four categories: neutrality, happiness, sadness and anger. In general, a speech recorded with a microphone contains background noises due to the speaker environment and the microphone characteristic, which can result in serious system performance degradation. In order to minimize the effect of these noises and to improve the system performance, a MA(Moving Average) filter with a relatively simple structure and low computational complexity was adopted. Then a SFS(Sequential Forward Selection) feature optimization method was implemented to further improve and stabilize the system performance. For speech emotion classification, a SVM pattern classifier is used. The experimental results indicate the emotional classification performance around 65% in the computer simulation and 62% on the ARM platform.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.223-232
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• 2005

In this paper, we propose a novel post-filtering algorithm with low computational complexity that improves the visual quality of decoded images using block boundary classification and simple adaptive filter (SAF). At first, each block boundary is classified into smooth or complex sub-region. And for smooth-smooth sub-regions, the existence of blocking artifacts is determined using blocky strength. And simple adaptive filtering is processed in each block boundary area. The proposed method processes adaptively, that is, a nonlinear 1-D 8-tap filter is applied to smooth-smooth sub-regions with blocking artifacts, and for smooth-complex or complex-smooth sub-regions, a nonlinear 1-D variant filter is applied to block boundary pixels so as to reduce the blocking and ringing artifacts. And for complex-complex sub-regions, a nonlinear 1-D 2-tap filter is only applied to adjust two block boundary pixels so as to preserve the image details. Experimental results show that the proposed algorithm produced better results than those of conventional algorithms both subjective and objective viewpoints.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.31-32
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• 2015

태양광 발전 시스템에서 태양광 모듈 모니터링 시스템은 모듈의 전압, 전류, 온도 등의 정보를 얻는 것을 목적으로 하며 이를 통해 사업자로 하여금 태양광 발전시스템의 유지 및 관리에 큰 도움을 줄 수 있다. 태양광 모듈의 DC 전력선 이용하여 통신하는 DC-PLC(DC - Power Line Communication)는 추가적인 통신선이 필요하지 않아 보다 경제적으로 모니터링 시스템을 구성 할 수 있다. 본 논문에서는 디지털 변조 방식으로 ASK(Amplitude Shift Keying) 방식을 사용하고 디지털 대역통과필터를 통해 캐리어 신호의 검출하는 DC-PLC 기반 태양광 모듈 모니터링 시스템의 설계한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1207-1214
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• 2019

Technologies of the 4th industrial revolution have been unknowingly seeping into our lives. Many IoT based home security systems are using the convolutional neural network(CNN) as good biometrics to recognize a face and protect home and family from intruders since CNN has demonstrated its excellent ability in image recognition. In this paper, three layouts of CNN for 2D and 2.5D image of small dataset with various input image size and filter size are explored. The simulation results show that the layout of CNN with 50*50 input size of 2.5D image, 2 convolution and max pooling layer, and 3*3 filter size for small dataset of 2.5D image is optimal for a home security system with recognition accuracy of 0.966. In addition, the longest CPU time consumption for one input image is 0.057S. The proposed layout of CNN for a face recognition is suitable to control the actuators in the home security system because a home security system requires good face recognition and short recognition time.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.18-26
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• 2015

Measuring the heart rate during exercise is important to properly control the amount of exercise. With the recent advent of smart device usage, there is a dramatic increase in interest in devices for the real-time measurement of the heart rate during exercise. During intensive exercise, accurate heart rate estimation from wrist-type photoplethysmography (PPG) signals is a very difficult problem due to motion artifact (MA). In this study, we propose an efficient algorithm for an accurate estimation of the heart rate from wrist-type PPG signals. For the twelve data sets, the proposed algorithm achieves the average absolute error of 1.38 beat per minute (BPM) and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.9922. The proposed algorithm presents the strengths in an accurate estimation together with a fast computation speed, which is attractive in application to wearable devices.