• 전기학회논문지
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• 제66권7호
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• pp.1117-1122
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• 2017

Pedestrian detection is a very important and valuable part of artificial intelligence and computer vision. It can be used in various areas for example automatic drive, video analysis and others. Many works have been done for the pedestrian detection. The accuracy of pedestrian detection on multiple pedestrian image has reached high level. It is not easily get more progress now. This paper proposes a new structure based on the idea of HOG and convolutional filters to do the pedestrian detection in single pedestrian image. It can be a method to increase the accuracy depend on the high accuracy in single pedestrian detection. In this paper, we use Multiblock HOG and magnitude of the pixel as the feature and use convolutional filter to do the to extract the feature. And then use NEWFM to be the classifier for training and testing. We use single pedestrian image of the INRIA data set as the data set. The result shows that the Convolutional Multiblock HOG we proposed get better performance which is 0.015 miss rate at 10-4 false positive than the other detection methods for example HOGLBP which is 0.03 miss rate and ChnFtrs which is 0.075 miss rate.

• 한국안전학회지
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• 제17권4호
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• pp.94-100
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• 2002

As the use of automatic handling equipment for sensitive semiconductor devices is rapidly increased, manufacturers of electronic components and equipments need to be more alert to the problem of electrostatic discharges(ESD). In order to analyze damage characteristics of semiconductor device damaged by ESD, this study adopts a new charged-device model(CDM), field-induced charged model(FCDM) simulator that is suitable for rapid, routine testing of semiconductor devices and provides a fast and inexpensive test that faithfully represents ESD hazards in plants. High voltage applied to the device under test is raised by the field of non-contacting electrodes in the FCDM simulator, which avoids premature device stressing and permits a faster test cycle. Discharge current and time are measured and calculated. The characteristics of electrostatic attenuation of domestic semiconductor devices are investigated to evaluate the ESD phenomena in the semiconductors. Also, the field charging mechanism, the device thresholds and failure modes are investigated and analyzed. The damaged devices obtained in the simulator are analyzed and evaluated by SEM. The results obtained in this paper can be used to prevent semiconductor devices form ESD hazards and be a foundation of research area and industry relevant to ESD phenomena.

• Journal of Power Electronics
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• 제2권2호
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• pp.146-153
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• 2002

this paper presents an implementation of digital control system of speed sensorless for Reluctance Synchronous Motor (RSM) drives with direct torque control (DTC). The problem of DTC for high-dynamic performance RSM drive is generating a nonlinear torque due to a saturated nonlinear inductance curve with various load currents. The control system consists of stator flux observer, compensating inductance look-up table, rotor position/speed/torque estimator, two hysteresis band controllers, an optimal switching look-up table, IGBT voltage source unverter, and TMS320C31 DSP controller. The stator flux observer is based on the combined voltage and current model with stator flux feedback adapitve control that inputs are the compensated inductances, current and voltage sensing of motor terminal with estimated rotor angle for wide speed range. The rotor position is estimated rotor speed is determined by differentiation of the rotor position used only in the current model part of the flux observer for a low speed operation area. It does not requrie the knowledge of any montor paramenters, nor particular care for moter starting, In order to prove the suggested control algorithm, we have simulation and testing at actual experimental system. The developed sensorless control system is showing a good speed control response characterisitic result and high performance features in 20/1500 rpm with 1.0Kw RSM having 2.57 ratio of d/q reluctance.

• 정보처리학회논문지D
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• 제15D권1호
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• pp.107-112
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• 2008

레거시소프트웨어시스템을 유지 및 보수하는 작업은 소프트웨어 공학 분야에서 중요한 화두이다. 그리고 유지 및 보수 과정에 있어 회귀 테스트는 소프트웨어의 변경에 따른 기능적 동작이 올바른지 확인한다. 그러나 기존의 회귀 테스트는 대부분 시스템 레벨에서 접근이 되었으며 단위테스트 레벨에서는 준비된 테스트 케이스가 없어서 적용이 어려웠다. 본 논문에서는 단위테스트 케이스들을 구현하고 자산화하기 위해서 기존의 레거시소프트웨어시스템을 재구성하는 기법을 제안한다. 그리고 이를 실제 개발 과제의 특정 모듈에 적용하고 그 테스트 커버리지 결과를 분석하였다. 향후 본 논문에서 제시하는 방안을 기반으로 재구성 자동화 기법 및 테스트 케이스 자동화 생성에 대한 연구가 지속된다면 레거시소프트웨어시스템의 유지 및 보수에 큰 발전을 기대할 수 있을 것이다.

• 한국산업융합학회 논문집
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• 제21권5호
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• pp.219-225
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• 2018

In this paper, for the testing of tensile strength of dead-end clamp used in transmission line, resulting values were estimated by designing and producing the horizontal version of widely-used vertical tensile tester. Tensile strength test of dead-end clamp for transmission line is essential for quality test of products. Moreover, tensile tester is an equipment that requires high level of reliability which needs to be examined through sampling tests commensurate with total inspection. Frames of tensile tester were made up of H-beams so that it can endure more than 20 [tons] of load capability and the test was implemented for 60[seconds] applying five types of tension. In consequence, the tester could withstand up to 21,600[kg] of weight as well as all types of tension. This newly developed horizontal tensile tester can be utilized in figuring out properties of various materials by estimating tensile strength of materials such as metal, rubber and fiber.

• 소프트웨어공학소사이어티 논문지
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• 제28권1호
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• pp.23-28
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• 2019

최근 이미지 처리 응용을 위한 심층 신경망 모델의 효과적 테스팅을 위해 해당 모델이 올바르게 예측하지 못하는 코너 케이스에 해당하는 행동을 보이는 데이터를 자동 생성하는 연구가 활발히 진행되고 있다. 본 논문은 문장 분류 심층 신경망 모델에 기반하고 있는 버그 담당자 자동 배정 시스템의 테스트를 위해 입력 데이터인 버그 리포트의 내용에서 임의의 단어를 선택해 동의어로 변형하는 테스트 데이터 생성기법을 제안한다. 그리고 제안하는 테스트 데이터 생성 기법을 사용한 경우와 기존의 차이 유발 테스트 데이터 생성 기법을 사용했을 경우를 다양한 뉴런 기반 커버리지를 중심으로 비교 평가한다.

• 대한원격탐사학회지
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• 제35권1호
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• Journal of Information Science Theory and Practice
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• 제6권4호
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• pp.28-38
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• 2018

Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

• 한국멀티미디어학회논문지
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• 제24권3호
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.