• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.569-575
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• 2020

The serial arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet, and statistical features have been used, additional steps such as transformation and feature extraction are required. On the contrary, deep learning models directly use the raw data without any feature extraction processes. Therefore, the usage of time-domain data is preferred, but the performance is not satisfactory. To solve this problem, subsequent 1-D signals are transformed into 2-D data that can feed into a convolutional neural network (CNN). Experiments validated that CNN model outperforms deep neural network (DNN) by the classification accuracy of 8.6%. In addition, data augmentation is utilized, resulting in the accuracy improvement by 14%.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.577-579
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• 1998

본 논문에서는 디지털 영상 데이터에 워터마크를 삽입하는 알고리즘을 제시한다. 디지털 데이터를 주파수 공간으로 변환시켜 인간이 잘 감지 못하는 주파수 영역과 중요한 주파수를 선택하여 대역확산통신(Spread Spectrum Communication)에서 사용되는 유사한 방법으로 워터마크를 삽입하였다. Fourier 스펙트럼 공간에서 JPEG 압축의 다양한 양자화 단계를 거쳐도 변화가 덜 민감한 Phase에서 워터마크를 삽입할 주파수 영역을 찾았다. 원본과 워터마크가 삽입된 데이터를 가지고 워터마크는 자기상관관계 특성으로 추출하였다. 그리고 다양한 신호처리(손실 압축, 필터링, 양자화)에도 워터마크를 추출하였다.

• Information and Communications Magazine
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• v.31 no.11
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• pp.35-45
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• 2014

주성분 분석 기법(PCA)는 가장 널리 사용되는 데이터 차원 감소 (dimensionality reduction) 기법으로 알려져 있다. 하지만 데이터에 이상점 (outlier)가 존재하는 환경에서는 성능이 크게 저하된다는 단점을 가지고 있다. Rank-Sparsity(Robust PCA) 기법은 주어진 행렬을 low-rank 행렬과 저밀도(sparse)행렬의 합으로 분해하는 방식으로, 이상점이 많은 환경에서 PCA기법을 효과적으로 대체할 수 있는 알고리즘으로 알려져 있다. 본 고에서는 RPCA 기법을 간략히 소개하고, 그의 적용분야, 및 알고리즘에 관한 연구들을 대해서 알아본다.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.372-375
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• 2001

본 논문에서는 고속데이터 전송을 위한 CDMA 모뎀를 구현하였다. 데이터율 1Mbps의 트래픽 5채널에 직교부호를 곱하여 채널을 구분한 후 하나의 채널로 처리하였다. I,Q로 입력된 신호는 복소 곱셈기에서 칩 레이트 8Mcps로 OCQPSK(또는 HPSK) 변조하였다. 복조기는 I,Q의 신호를 역확산한 후 직교부호를 다시 곱하여 각 채널의 데이터를 분리한다. 변복조기의 구현은 클럭 속도 167MHz의 부동 소수점형 프로세서인 TI사의 TMS320C6701 DSP(Digital Signal Processor)를 사용하었고, long code 및 I,Q 채널 PN 코드는 IMT-2000 동기방식과 비동기방식의 규격에 정의된 2가지의 PN코드 발생기를 모두 구현하였다.

• Journal of Satellite, Information and Communications
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• v.4 no.1
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• pp.36-44
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• 2009

The key technologies of GNSS receiver for GNSS sensor station are under development as a part of a GNSS ground station in ETRI. This paper presents the GNSS receiver implementation and signal processing result which is implemented based on FPGA to process the Galileo E1 and E5 signal. To verify the working and performance for GNSS receiver which is implemented based on FPGA, live signal received from GIOVE-B which is second test satellite is used. We gather GIOVE-B signal by using prototyping antenna and RF/IF units including IF-component. To verify Galileo E1 and E5 signal processing function from GIOVE-B, FPGA based signal processing module is implemented as a prototyping hardware board.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.263-269
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• 2010

This paper describes real-time detection and tracking of moving objects for unmanned visual surveillance. Using images obtained from the fixed camera it detects moving objects within the image and tracks them with displaying rectangle boxes enclosing the objects. Tracking method is implemented on an embedded system which consists of TI DSK645.5 kit and the FPGA board connected on the DSP kit. The DSP kit processes image processing algorithms for detection and tracking of moving objects. The FPGA board designed for image acquisition and display reads the image line-by-line and sends the image data to DSP processor, and also sends the processed data to VGA monitor by DMA data transfer. Experimental results show that the tracking of moving objects is working satisfactorily. The tracking speed is 30 frames/sec with 320x240 image resolution.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.49-54
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• 2017

In this paper, we use face database to detect human emotion in real time. Although human emotions are defined globally, real emotional perception comes from the subjective thoughts of the judging person. Therefore, judging human emotions using computer image processing technology requires high technology. In order to recognize the emotion, basically the human face must be detected accurately and the emotion should be recognized based on the detected face. In this paper, based on the Cohn-Kanade Database, one of the face databases, faces are detected by combining the detected faces with the database.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.847-855
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• 2018

GMTI (Ground Moving Target Indicator) is a kind of airborne radar function that is used widely in military applications to detect the moving targets on the ground. In this paper, GMTI signal processing technique was presented and its performance was verified using sum and difference channels raw data obtained by the captive flight test.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.311-318
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• 2013

This paper deals with a noise reduction algorithm which uses the binary masking in the time-frequency domain. To improve speech intelligibility in noise, noise-masked speech is decomposed into time-frequency units and mask "0" is assigned to masker-dominant region removing time-frequency units where noise is dominant compared to speech. In the previous research, Gaussian mixture models were used to classify the speech-dominant region and noise-dominant region which correspond to mask "1" and mask "0", respectively. In each frequency band, data were collected and trained to build the Gaussian mixture models and detection procedure is performed to the test data where each time-frequency unit belongs to speech-dominant region or noise-dominant region. In this paper, we consider the correlation of masks in the frequency domain and propose a post-processing method which exploits the Viterbi algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.213-220
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• 2023

Falling occurs unexpectedly during daily activities, causing many difficulties in life. The purpose of this study was to establish a system for fall detection of high-risk occupations and to verify their effectiveness by collecting data and applying it to predictive models. To this end, a wearable device was configured to detect fall by calculating acceleration signals and azimuths through acceleration sensors and gyro sensors. In addition, the study participants wore the device on their abdomen and measured necessary data from falls-related movements in the process of performing predetermined activities and transmitted it to the computer through a Bluetooth device present in the device. The collected data was processed through filtering, applied to fall detection prediction models based on deep learning algorithms which are 1D CNN, LSTM and CNN-LSTM, and evaluate the results.