• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.370-375
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• 2007

인터넷과 디지털기기의 발달로 인해 이미지 데이터가 기하급수적으로 증가함에 따라 이미지 데이터의 의미적인 자동분류를 위한 연구가 활발히 진행되고 있다. 기존의 이미지 분류방법은 내용기반 분류와 주석자에 의한 직접 분류 방법이 있다. 하지만 분류 기준이 명확하지 않고, 이미지가 내포하고 있는 정확한 의미 별로 분류가 이루어져 있지 않았다. 이에 본 논문에서는 이미지의 주석간 개념적인 관계를 분석하고 이미지에 의미를 대표할 수 있는 키워드를 추출하여 의미적이고 효율적인 분류 방법을 제안한다.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.720-725
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• 1991

This paper is concerned with the program of the automatic mesh generation for 2-dimensional domain which contains the curved boundaries and holes. This program treats a new vertical - line drawing method. This method starts with 4-subdivisions of problem domain and the classification of the cross points of grid lines and boundaries. And the new node is generated by the vertical line to the line connecting the two intersections of a boundary and two grid lines in general. The boundaries are piecewise-curves composed of lines, circles, arcs, and free curves. The free curves are generated by B-Spline form. Although there were some bad elements for the complex boundary, it was possible to obtain the acceptable elements for the given boundaries. The results of automatic mesh generation can be verified directly by drawing on the computer monitor in executing the program. And it is possible to add the processes - that is, editing, hard copying, etc - using the script file in Auto-CAD.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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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.

• Proceedings of the IEEK Conference
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• 2003.07e
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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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• v.12 no.8
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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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.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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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.

• Proceedings of the KIEE Conference
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• 2005.07c
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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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• v.30 no.3
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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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.