• 한국마이크로전자및패키징학회:학술대회논문집
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• 한국마이크로전자및패키징학회 2002년도 춘계 기술심포지움 논문집
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• pp.214-218
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• 2002

So far, many kinds of researches on the ceramic chip components and MCM-C RF module especially on the 3-dimensional ceramic module using embedded passives have been performed. LTCC system has many kinds of advantages, like low lass, low cost of process, stability of process etc.. But it's so hard to adjust the characteristics of passives in ceramic module after fabrication. So the exact prediction of behavior of components in high frequency region upper than 2 GHz must be made. In this procedure, the exact measurement is need. In this study, many kinds of measurement Jigs are compared and optimized, and measurement methods of each parameter are designed.

• 대한임베디드공학회논문지
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• 제18권5호
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• pp.259-265
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• 2023

Pedestrian detection is used to effectively detect pedestrians in various situations based on deep learning. Pedestrian detection has difficulty detecting pedestrians due to problems such as camera performance, pedestrian description, height, and occlusion. Even in the same pedestrian, performance in detecting them can differ according to the height of the pedestrian. The height of general pedestrians encompasses various scales, such as those of infants, adolescents, and adults, so when the model is applied to one group, the extraction of data becomes inaccurate. Therefore, this study proposed a pedestrian detection method that fine-tunes the pedestrian area by Refining Layer and Feature Concatenation to consider various heights of pedestrians. Through this, the score and location value for the pedestrian area were finely adjusted. Experiments on four types of test data demonstrate that the proposed model achieves 2-5% higher average precision (AP) compared to Faster R-CNN and DRPN.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.86-94
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• 2003

Electronically available biological literature has been accumulated exponentially in the course of time. So, researches on automatically acquiring knowledge from these tremendous data by text mining technology become more and more prosperous. However, most of the previous researches are technology oriented and are not well focused in practical extraction target, hence result in low performance and inconvenience for the bio-researchers to actually use. In this paper, we propose a more biology oriented target domain specific text mining system, that is, POSTECH bio-text mining system (POSBIOTM), for signal transduction pathway extraction, especially for G protein-coupled receptor (GPCR) pathway. To reflect more domain knowledge, we specify the concrete target for pathway extraction and define the minimal pathway domain ontology. Under this conceptual model, POSBIOTM extracts interactions and entities of pathways from the full biological articles using a machine learning oriented extraction method and visualizes the pathways using JDesigner module provided in the system biology workbench (SBW) [14]

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• Ocean Systems Engineering
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• 제7권2호
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• pp.157-177
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• 2017

This research is based on the concept of safety airbag to design a self-rescue system for the autonomous underwater vehicle (AUV) using micro inertial sensing module. To reduce the possibility of losing the underwater vehicle and the difficulty of searching and rescuing, when the AUV self-rescue system (ASRS) detects that the AUV is crashing or encountering a serious collision, it can pump carbon dioxide into the airbag immediately to make the vehicle surface. ASRS consists of 10-DOF sensing module, sensing attitude algorithm and air-pumping mechanism. The attitude sensing modules are a nine-axis micro-inertial sensor and a barometer. The sensing attitude algorithm is designed to estimate failure attitude of AUV properly using sensor calibration and extended Kalman filter (SCEKF), feature extraction and backpropagation network (BPN) classify. SCEKF is proposed to be used subsequently to calibrate and fuse the data from the micro-inertial sensors. Feature extraction and BPN training algorithms for classification are used to determine the activity malfunction of AUV. When the accident of AUV occurred, the ASRS will immediately be initiated; the airbag is soon filled, and the AUV will surface due to the buoyancy. In the future, ASRS will be developed successfully to solve the problems such as the high losing rate and the high difficulty of the rescuing mission of AUV.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.215-218
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• 2005

This paper reports the current status of development of the Korean speech recognition platform (ECHOS). We implement new modules including ETSI feature extraction, backward search with trigram, and utterance verification. The ETSI feature extraction module is implemented by converting the public software to an object-oriented program. We show that trigram language modeling in the backward search pass reduces the word error rate from 23.5% to 22% on a large vocabulary continuous speech recognition task. We confirm the utterance verification module by examining word graphs with confidence score.

• 한국멀티미디어학회논문지
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• 제6권2호
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• pp.309-318
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• 2003

분산된 환경에서 정보를 교환하기 위한 수단으로의 XML문서는, 그 자료의 구성을 정의하는 DTD를 통해서만 정확한 의미가 파악될 수 있다. 하지만 인터넷에서 수집된 XML 문서에 항상 DTD가 제공되리라는 보장은 없으며, 이러한 경우에는 수집 된 XML 문서의 구조를 파악한 후 정보를 추출해야 한다. 본 연구에서는 DTD가 알려지지 않은 XML 문서를 바탕으로 적합한 DTD를 구성하고, 이를 이용해 XML 정보를 구조적인 형태로 하부 DB에 저장할 수 있는 방법에 대해 설명하고자 한다. 특히, 본 연구를 통해 개발된 DTD 추출기는 XML 파일을 1-Path로 스캔하기에 기존에 나와있는 다른 방식보다 더 효율적으로 DTD를 구축할 수 있다.

• 대한토목학회논문집
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• 제31권6D호
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• pp.849-856
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• 2011

본 연구에서는 교량의 구조계산서와 같이 여러 종류의 머리기호를 사용하며 제목의 계층구조가 복잡한 형식을 띄는 엔지니어링 문서의 비구조화된 텍스트 정보를 제목의 계층 구조에 따른 준구조화된 XML 문서로 변환시키는 방법을 제시한다. 텍스트 정보로부터 제목의 계층구조를 자동으로 추출하기 위해 문서구조분석 방법의 하나인 문서구조추출 기법을 이용하는 방법을 개발하였으며, 특히 개조식 구문의 식별방법을 개발하여 구조계산서 문서 계층구조의 제목추출과정 및 계층구분의 전체 정확도를 향상시킬 수 있는 방법을 제시하였다. 제시된 방법에 따른 응용모듈을 개발하였으며, 총 40개의 교량 구조계산서를 대상으로 그 성능을 평가하였다. 먼저, 20개의 강거더 상부 구조계산서를 대상으로 선행 연구결과와 비교하여 본 연구에서 개발된 응용모듈의 정확성과 신뢰도가 향상됨을 보였다. 또한, 다른 구조형식에 대한 구조계산서 20개에 대하여 개발된 모듈의 적용성을 평가하였다. 그 결과 본 연구에서 제안한 방법에 의한 문서 계층구조 분석의 최종 정확도는 평균 99% 수준 이상을 나타내고, 표준편차는 1.52로 나타나 본 연구에서 제시된 방법이 다양한 형식의 머리기호를 사용하여 제목을 구분하는 여러 엔지니어링 문서에도 적용이 가능함을 보였다.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1899-1909
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• 2015

Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians’ effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10- fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent- Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집A
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• pp.212-218
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• 2000

The importance and role of pre-/in-service inspection(PSI/ISI) for nuclear power plant(NPP) components are intimately related to plant design, safety, reliability, operation, etc. In this paper, for an effective and efficient management of large amounts of PSI/ISI data in NPPs, an intelligent database program(WS-IDPIN) for PSI/ISI data management of NPP was developed. WS-IDPIN program enables the prompt extraction of previously conducted PSI/ISI conditions and results so that the time-consuming data management, painstaking data processing and analysis in the past are avoided. Furthermore, development of ISI UT auto flaw evaluation and acceptance module based on ASME Code Sec. XI were presented. This module can be used for any angle beam examination from flat plate to spherical shapes as selected by the proper azimuthal angle. This program can be further developed as a unique PSI/ISI data management expert system.