• 한국측량학회지
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• 제24권1호
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• pp.1-8
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• 2006

효율적인 3차원 모델링은 건설, 기계 그리고 디자인과 같은 폭넓은 설계분야에서 필수적으로 되고 있다. 특히, 역설계를 가능케 하는 툴로써 발전하고 있다. 3차원 모델링은 신속성, 정확성 그리고 명확성이 요구된다. 모델링을 위한 데이터 획득은 접촉식 좌표측정기, 레이져스캐너, 패턴스캐너 그리고 수치사진측량방법을 이용한다. 본 연구에서는 모델링 기법을 분석하고 패턴스캐너를 이용한 3차원 모델링기법을 소개하고자 한다. 또한, 본 연구는 OPTO-Top 패턴스캐너를 이용하여 3차원 모델링을 시도하고 신속성과 효율성을 수치사진측량기법과 비교분석하였다. 아울러 3차원으로 사용자가 웹환경에서 시뮬레이션 할 수 있는 환경구축을 시도하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3268-3283
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• 2018

With the advent of the Information Age, the source identification of digital images, as a part of digital image forensics, has attracted increasing attention. Therefore, an effective technique to identify the source of digital images is urgently needed at this stage. In this paper, first, we study and implement some previous work on image source identification based on sensor pattern noise, such as the Lukas method, principal component analysis method and the random subspace method. Second, to extract a purer sensor pattern noise, we propose a sample selection method to improve the random subspace method. By analyzing the image texture feature, we select a patch with less complexity to extract more reliable sensor pattern noise, which improves the accuracy of identification. Finally, experiment results reveal that the proposed sample selection method can extract a purer sensor pattern noise, which further improves the accuracy of image source identification. At the same time, this approach is less complicated than the deep learning models and is close to the most advanced performance.

• 한국한의학연구원논문집
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• 제14권2호
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• pp.41-46
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• 2008

The study was to investigate the agreement rate between the statistical diagnosis of pattern identification by discriminant analysis and the clinical diagnosis of pattern identification by medical specialist in obese patients with BMI$\geqq$23. The agreement rate of deficiency of the spleen, phlegm-retention, deficiency of Yang, retention of undigested food, stagnation of liver Gi, and blood stagnation are 0.40, 0.33, 0.52, 0.76, 0.71, and 0.66, respectively and accuracy rate and prediction rate using linear discriminant function are 0.59 and 0.61, respectively. Therefore, the complementary management in CRF questionnaires and/or consultation from experts will improve the accuracy and prediction rate, which will be helpful for pattern identification of obesity by clinical experts.

• 로봇학회논문지
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• 제14권3호
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• 한국군사과학기술학회지
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• 제21권4호
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• pp.529-540
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• 2018

As the equipment of the military has recently become more sophisticated and expensive, the cost of purchasing spare parts is also steadily increasing. Therefore, demand forecast accuracy is also becoming an issue for the effective execution of the spare parts budget. This study predicts the demand by using the data of spare parts consumption of the KF-16C fighter which is being operated in the Republic of Korea Air Force. In this paper, SARIMA(Seasonal Autoregressive Integrated Moving Average) is applied to seasonal data after dividing the spare parts consumptions into seasonal data and non-seasonal data. Proposing new methods, Majority Voting and Hybrid Method, to the non-seasonal data which consists of spare parts of low consumption with unclear pattern, We want to prove that the demand forecast accuracy of spare parts improves.

• 한국도로학회논문집
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• 제20권2호
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.133-136
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• 1997

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room or improving the accuracy of chromosome classification. In this paper, We propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of multi-step multi-layer neural network(MMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted three morphological features parameters such as centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.). This Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other classification methods.

• 한국정보통신학회논문지
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• 제12권7호
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• pp.1291-1300
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• 2008

본 논문에서는 패턴인식의 정확성을 향상시키기 위한 지능시스템을 제안한다. 제안한 지능시스템은 신경회로망의 무감독학습 방법인 SOPM(Self Organizing Feature Map), LVQ(Learning Vector Quantization), 그리고 퍼지이론의 FCM(Fuzzy C-means)을 이용하여 구성한다. 제안한 지능시스템의 유용성은 실험을 통해 확인한다. 실험은 Fisher의 Iris 데이터 분류, Cambridge 대학의 Olivetti 연구실(ORL; Olivetti Research Laboratory)에서 제공하는 얼굴 데이터베이스를 이용한 얼굴 영상 데이터 분류, 그리고 근전도(EMG, Electromyogram) 데이터를 분류하는 것이다. 제안한 지능시스템은 일반적인 LVQ와 비교한다. 실험을 통해 제안한 지능시스템이 일반적인 LVQ보다 패턴 인식의 정확성이 더 우수함을 알 수가 있었다.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3943-3948
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• 2022

Single-photon emission computed tomography is one of the reliable pin-by-pin verification techniques for spent-fuel assemblies. One of the challenges with this technique is to increase the total fuel assembly verification speed while maintaining high verification accuracy. The aim of the present study, therefore, was to develop an artificial intelligence (AI) algorithm-based tomographic image analysis technique for partial-defect verification of fuel assemblies. With the Monte Carlo (MC) simulation technique, a tomographic image dataset consisting of 511 fuel-rod patterns of a 3 × 3 fuel assembly was generated, and with these images, the VGG16, GoogLeNet, and ResNet models were trained. According to an evaluation of these models for different training dataset sizes, the ResNet model showed 100% pattern estimation accuracy. And, based on the different tomographic image qualities, all of the models showed almost 100% pattern estimation accuracy, even for low-quality images with unrecognizable fuel patterns. This study verified that an AI model can be effectively employed for accurate and fast partial-defect verification of fuel assemblies.

• 한국측량학회지
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• 제40권2호
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• pp.109-118
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• 2022

싱크홀 탐사 등 지반조사를 위한 기술로 개발된 GPR (Ground Penetrating Radar)은 지하시설물 탐사에서 불탐구간을 해소하기 위한 방법으로 한정되어 사용하고 있었다. 정부는 지하시설물 데이터의 정확도 개선을 위하여 2022년 7월부터 비금속 관로 탐사기를 이용한 지하시설물 탐사가 가능하도록 하였다. 그러나 GPR은 점토층 등과 같이 연약지반 같은 수분함량이 높은 지반에서 탐사율도 낮아지고, 정확도에 많은 변동이 발생하는 문제점을 가지고 있다. 본 연구에서는 GPR의 특성과 지하시설물의 환경을 고려한 탐사정확도 향상방안으로 유전률 상수 보정과 GPR 영상자료의 패턴분석을 이용한 지하시설물 GPR탐사 방안을 제시하고자 한다. 본 연구를 통하여 GPR 주파수 대역과 이기종 GPR을 적용한 현장검증 결과 지하시설물 탐사의 정확도 향상 및 높은 재현성 결과를 도출하였다.