• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.581-586
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• 1996

This paper presents a feature recognition system for recognizing and extracting feature information needed for machining from design data contained in the CAD database of AutoCAD system. The developed system carries out feature recognition from an orthographic view of a press mold containing not only atomic features such as holes, pockets, and slots, but also compound features. Based on the result of feature recognition, it generates a 3-D modeling of the press mold. Especially, The feature recognition part is designed for detecting feature styles according to feature definition and classification, extracting parameters for various atomic features, and constructing necessary data structures for the recognized features.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.1743-1746
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• 2003

C. elegans often used to study of function of gene, but it is difficult for human observation to distinguish the mutants of C. elegans. To solve this problem, the system, which can be classified automatically using the computer vision, is studying now. In the previous works , they described the auto-tracking system and the egg-laying timing modeling, which are used to automated-classily system. In this paper, we use three kinds of features, which are related to movement , size and posture of the worm, and each feature is described mathematically and normalized. In experimental result, we validated the features for the hierarchical clustering, And we used the Calinski and Harabasz's method to find the appropriate cluster number.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3790-3803
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• 2018

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.228-233
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• 1997

An efficient corner matching algorithm is developed to minimize the amount of calculation. To reduce the amount of calculation, all available information from a corner detector is used to make model. This information has uncertainties due to discretization noise and geometric distortion, and this is represented by fuzzy rule base which can represent and handle the uncertainties. Form fuzzy inference procedure, a matched segment list is extracted, and resulted segment list is used to calculate the transformation between object of model and scene. To reduce the false hypotheses, a vote and re-vote method is developed. Also an auto tuning scheme of the fuzzy rule base is developed to find out the uncertainties of features from recognized results automatically. To show the effectiveness of the developed algorithm, experiments are conducted for images of real electronic components.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2003년도 하계학술대회 논문집
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• pp.18-21
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• 2003

In general it has been considered to be the difficult problem that we divide continuous speech into short interval with having identical phoneme quality. In this paper we used Gaussian Mixture Model (GMM) related to probability density to divide speech into phonemes, an initial, medial, and final sound. From them we peformed continuous speech recognition. Decision boundary of phonemes is determined by algorithm with maximum frequency in a short interval. Recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme divided by eye-measurement. For the experiments result we confirmed that the method we presented is relatively superior in auto-segmentation in korean speech.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 C
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• pp.2208-2210
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• 2005

전문적인 지식이 없는 UHF 부분방전 측정장치 사용자를 위해 자동으로 측정된 신호로부터 GIS 내부의 결함을 추론할 수 있는 신경망회로 엔진을 연구하였다. 측정된 방전신호로부터 적절한 변수들을 계산하고 이를 신경망회로를 이용하여 미리 분류한 GIS 결함들 중 가장 유사한 결함을 자동으로 표현하는 기능을 엔진이 가지도록 하였다. 특히 본 엔진은 3상 일괄형 GIS나 GIS의 전압 위상에 동기되지 않은 부분방전 측정시스템에도 방전 원인을 잘 추론함을 실험을 통하여 확인하였다.

• Smart Structures and Systems
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• 제30권3호
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• pp.263-271
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• 2022

In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice.

• 대한임베디드공학회논문지
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• 제18권3호
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• pp.101-108
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• 2023

Optimizing the performance of deep neural networks on embedded systems is a challenging task that requires efficient compilers and runtime systems. We propose a TVM-based approach that consists of three steps: quantization, auto-scheduling, and ahead-of-time compilation. Our approach reduces the computational complexity of models without significant loss of accuracy, and generates optimized code for various hardware platforms. We evaluate our approach on three representative CNNs using ImageNet Dataset on the NVIDIA Jetson AGX Xavier board and show that it outperforms baseline methods in terms of processing speed.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2024년도 춘계학술발표대회
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• pp.656-657
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• 2024

현대 의료 진단 분야 중 하나인 수면다원 검사에서 수면 단계 분류는 평가에 많은 시간이 소요되고 평가자 간 일관성 문제가 대두되고 있다. 이러한 평가 문제를 해결하기 위하여 최근 급격하게 발전하고 있는 딥러닝 기술을 이용하여 자동화하려는 연구가 활발히 진행되고 있다. 본 논문에서는 오토 인코더 (autoencoder)와 대조 학습 (contrastive learning)을 통해 수면 시 측정된 생체 신호에서 보다 중요한 특징을 추출하는 방법을 제안하고 제안된 방법의 딥러닝 모델을 구성 및 평가한다.

• 한국통신학회논문지
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• 제39C권10호
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• pp.887-895
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• 2014

졸음운전은 전체 교통사고 원인 중 큰 비중을 차지하며 그 위험성이 음주운전보다도 크다고 알려져 있다. 따라서 운전자의 졸음을 판단하고 경고하는 시스템 개발에 대한 관심이 높아지고 있으며, 뇌파를 분석하는 것이 운전자의 피로와 졸음을 감지하는데 효과적이라는 연구결과들이 발표되었다. 본 논문은 짧은 시간에 높은 해상도를 가지는 auto-regressive 모델 기법 중 잡음에 강인한 errors-in-variables(EIV) 방법을 이용하여 특징벡터를 추출하고, 다층신경망(multilayer perceptron; MLP)에 적용하여 운전자의 상태를 각성, 천이, 졸음의 세 가지 상태로 분류하는 졸음 감지 시스템을 제안한다. 생체신호의 측정 환경에 따른 성능을 평가하기 위해 높은 진단률을 갖도록 하는 EIV차수를 결정하고, 잡음에 대한 강인성을 확인하기 위해 신호대 잡음비(signal-to-noise ratio; SNR)에 따른 성능을 선형 예측 부호화(linear predictive coding; LPC) 방법과 비교하였다. 이 결과로부터 제안한 EIV와 MLP를 결합한 졸음 감지 시스템은 기존의 LPC와 MLP를 이용한 시스템에 대해 우수한 성능을 얻을 수 있음을 확인하였다.