• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.452-455
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• 2018

Wheel loader performs digging and dumping tasks using boom and bucket. The operation of the wheel loader equipment has a lot of repetitive tasks and the working environment is poor, but only by hand by man. Recently, demands for applying unmanned automated systems are increasing more and more in electrical components. For automated systems, accurate angle detection is indispensable for stable control. This paper proposes a signal processing system for precise angular control with noise robust features. As a result of implementing the proposed system and applying it to the wheel loader boom angle system, it was possible to detect an angle change of about 0.1 degree.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.177-180
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• 2001

영상에 존재하는 객체들을 인식하기 위해서는 먼저 영상의 영역분할이 필요하다. 통계적 모델을 이용한 영상의 영역분할은 미리서 분할하고자 하는 클러스터의 수를 결정한 후 이를 토대로 영상을 분할하게 된다. 그러나 영상마다 특성상 분할하고자 하는 클러스터 수가 다를 경우 이를 수동적으로 해주는 것은 비능률적이다. 따라서 본 논문은 영상의 영역분할에 통계적 모델에서 미리 결정해줘야 하는 클러스터의 수 문제를 자동으로 검출하고 퍼지 c-Means 글러스터링 알고리즘을 통한 영상의 영역분할 시 노이즈문제를 이웃한 픽셀들의 멤버쉽 값을 평균화합으로써 해결하는 방법을 제안하였다.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2501-2503
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• 2005

탐침형 정보 저장 장치는 원자 현미경의 원리를 이용한 차세대 고밀도 저장장치로 여겨진다. 정보의 크기가 수십 나노미터 이하이므로 정밀한 위치 제어 알고리즘이 필수적이다. 본 논문에서는 별도의 위치 센서없이 복수개인 탐침을 이용하여 원하는 데이터 위치로 이동하는 servo algorithm, 정보 추종을 위한 tracking algorithm 및 위치 검출용 탐침의 수를 줄이고 최적 제어기 설계를 위한 노이즈 특성 안정화를 위한 servo code 설계법을 제안하고 제어기 설계법을 제안한다.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2000.11a
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• pp.231-234
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• 2000

The corona in oil, the surface discharge in oil, the void discharge which could be in a transformer were imitated by using three electrodes with different shapes and the frequency bands of each ultrasonic signal were measured. After emitting artificial noises toward the transformer, only ultrasonic signals were extracted by frequency filtering, from the ultrasonic signals with noises at partial discharge. As a result, it was confirmed that the ultrasonic signals due to the corona in oil, the surface discharge in oil, the void discharge could be distinguished from the noises.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.598-600
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• 2000

영상에 존재하는 객체들을 인식하기 위해서는 먼저 영상의 영역 분할이 필요하다. 통계적 모델을 이용한 영상의 영역 분할은 미리서 분할하고자 하는 클러스터의 수를 결정한 후 이를 토대로 영상을 분할하게 된다. 그러나 영상마다 특성상 분할하고자 하는 클러스터 수가 다를 경우 이를 수동적으로 해주는 것은 비능률적이다. 따라서 본 논문은 영상의 영역 분할에 통계적 모델에서 미리 결정해줘야 하는 클러스터의 수 문제를 자동으로 검출하고 퍼지 c-Means 클러스터링 알고리즘을 통한 영상의 영역 분할 시 노이즈 문제를 이웃한 픽셀들의 멤버쉽 값을 평균화함으로써 해결하는 방법을 제안하였다.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.359-365
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• 2021

GEO-KOMPSAT-2A (GK2A) AMI (Advanced Meteorological Imager) Best Detector Select (BDS) map is pre-determined and uploaded before the satellite launch. After the launch, there is some possibility of a detector performance change driven by an abrupt temperature variation and thus the status of BDS map needs to be evaluated and updated if necessary. To investigate performance of entire elements of the detectors, AMI BDS analyses were conducted based on a technical note provided from the AMI vendor (L3HARRIS). The concept of the BDS analysis is to investigate the stability of signals from detectors while they are staring at targets (deep space and internal calibration target). For this purpose, Long Time Series (LTS) and Output Voltage vs. Bias Voltage (V-V) methods are used. The LTS for 30 secs and the V-V for two secs are spanned respectively for looking at the targets to compute noise components of detectors. To get the necessary data sets, these activities were conducted during the In-Orbit Test (IOT) period since a normal operation of AMI is stopped and special mission plans are commanded. With collected data sets during the GK2A IOT, AMI BDS map was intensively examined. It was found that about 1% of entire detector elements, which were evaluated at the ground test, showed characteristic changes and those degraded elements are replaced by alternative best ones. The stripping effects on AMI raw images due to the BDS problem were clearly removed when the new BDS map was applied.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.257-264
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• 2013

In this study, we have designed the variable amplitude threshold algorithm that can enhance the accuracy of step count using variable amplitude. This algorithm converts the x, y, z sensor values into a single energy value($E_t$) by using SVM(Signal Vector Magnitude) algorithm and can pick step count out over 99% of accuracy through the peak data detection algorithm and fixed peak threshold. To prove the results, We made the noise filtering with the fixed amplitude threshold from the amplitude of energy value that found out the detection error was increasing, and it's the key idea of the variable amplitude threshold that can be adapted on the continuous data evaluation. The experiment results shows that the variable amplitude threshold algorithm can improve the average step count accuracy up to 98.9% at 10 Hz sampling rate and 99.6% at 20Hz sampling rate.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.121-128
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• 2008

Accurate voice activity detection have a great impact on performance of speech applications including speech recognition, speech coding, and speech communication. In this paper, we propose methods for voice activity detection that can adapt to various car noise situations during driving. Existing voice activity detection used various method such as time energy, frequency energy, zero crossing rate, and spectral entropy that have a weak point of rapid. decline performance in noisy environments. In this paper, the approach is based on existing spectral entropy for VAD that we propose voice activity detection method using MFB(Met-frequency filter banks) spectral entropy, gradient FFT(Fast Fourier Transform) spectral entropy. and gradient MFB spectral entropy. FFT multiplied by Mel-scale is MFB and Mel-scale is non linear scale when human sound perception reflects characteristic of speech. Proposed MFB spectral entropy method clearly improve the ability to discriminate between speech and non-speech for various in noisy car environments that achieves 93.21% accuracy as a result of experiments. Compared to the spectral entropy method, the proposed voice activity detection gives an average improvement in the correct detection rate of more than 3.2%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.75-82
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• 2005

The robust feature vector selection method for the multiple frontal face detection is proposed in this paper. The proposed feature vector for the training and classification are integrated by means, amplitude projections, and its 1D Harr wavelet of the input image. And the statistical modeling is performed both for face and nonface classes. Finally, the estimated probability density functions (PDFs) are applied for the detection of multiple frontal faces in the still image. The proposed method can handle multiple faces, partially occluded faces, and slightly posed-angle faces. And also the proposed method is very effective for low quality face images. Experimental results show that detection rate of the propose method is $98.3\%$ with three false detections on the testing data, SET3 which have 227 faces in 80 images.