• 한국전산구조공학회논문집
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• 제36권1호
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• pp.1-7
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• 2023

본 논문에서는 구조물의 부분 변위값으로 전체 구조물의 변위 형상을 예측할 수 있는 인공지능 학습기법을 개발하였으며, 개발된 기술의 성능을 실험을 통해 평가하였다. 3차원 공간에서 변위 형상 및 노드 위치 좌표의 특성을 학습에 반영할 수 있는 Image-to-Image 변위 형상 학습과 위치 특징을 결합한 변위 상관 학습 방법을 제시하였다. 개발된 인공지능 학습방법의 성능을 평가하기 위해 목업 구조 실험을 진행하였고, 3D 스캔으로 측정한 변위값과 인공지능으로 예측한 결과를 비교하였다. 비교 결과 인공지능 예측 결과는 3D 스캔 측정 결과에 비해 5.6~5.9%의 오차율을 보여 적정 성능을 보였다.

• 경영과정보연구
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• 제36권4호
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• pp.33-51
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• 2017

제4차 산업혁명의 등장과 경제성장으로 인한 '국민 삶의 질 향상' 요구 증대로 인해 의료서비스의 질과 의료비용에 대한 국민들의 요구수준이 향상되고 있으며, 이로 인해 인공지능이 의료현장에 도입되고 있다. 하지만 인공지능이 의료분야에 활용된 사례를 살펴보면 '삶의 질'에 직접적인 영향을 끼치는 만성피부질환에 활용된 사례는 부족한 실정이며, 만성피부질환 중 대표적 질병인 아토피피부염은 정성적 진단 방법으로 인해 진단의 객관성을 확보할 수 없다는 한계가 존재한다. 본 연구에서는 아토피피부염의 객관적 중증도 평가 방법을 마련하여 아토피피부염 환자의 삶의 질을 향상시키고자 다음과 같은 연구를 수행하였다. 첫째, 가톨릭대학교 의과대학 성모병원의 데이터베이스로부터 아토피피부염 환자의 이미지 데이터를 수집했으며, 수집된 이미지 데이터에 대한 정제 및 라벨링 작업을 수행하여 모델 학습과 검증에 적합한 데이터를 확보했다. 둘째, 지능형 아토피피부염 중증도 진단 모형에 적합한 이미지 인식 알고리즘을 파악하기 위해 다양한 CNN 알고리즘들을 병변별 학습용 데이터로 학습시키고, 검증용 데이터를 활용하여 해당 모델의 이미지 인식 정확도를 측정했다. 실증분석 결과 홍반(Erythema)의 경우 'ResNet V1 101', 긁은 정도(Excoriation)의 경우 'ResNet V2 50'이 90% 이상의 정확도를 기록하였으며, 태선화(Lichenification)의 경우 학습용 데이터 부족의 한계로 인해 두 병변보다 낮은 89%의 정확도를 보였다. 해당 결과를 통해 이미지 인식 알고리즘이 단순한 사물 인식 분야뿐만 아니라 전문적 지식이 요구되는 분야에도 높은 성능을 나타낸다는 것을 실증적으로 입증했으며, 본 연구는 실제 아토피피부염 환자의 이미지 데이터를 활용했다는 측면에서 실제 임상환경에서 활용성이 높을 것으로 사료된다.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 1989년도 한글날기념 학술대회 발표논문집
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• pp.101-106
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• 1989

음성 및 문자를 통한 컴퓨터와의 정보 교환을 위한 통합 사용자 인터페이스 (Intelligent Man- Machine interface) 시스템의 일환으로 한국어 단모음의 인식을 위한 시스템을 인공 신경망 모델을 사용하여 구현하였으며 인식시스템의 상위 접속부에 필요한 단어 인식 모듈에 있어서의 인지 실험도 행하였다. 모음인식의 입력으로는 제1, 제2, 제3 포르만트가 사용되었으며 실험대상은 한국어의 [아, 어, 오, 우, 으, 이, 애, 에]의 8 개의 단모음으로 하였다. 사용한 인공 신경망 모델은 Multilayer Perceptron 이며, 학습 규칙은 Generalized Delta Rule 이다. 1 인의 남성 화자에 대하여 약 94%의 인식율을 나타내었다. 그리고 음성 인식시의 인지 현상 실험을 위하여 약 20개의 단어를 인공신경망의 어휘레벨에 저장하여 음성의 왜곡, 인지시의 lexical 영향, categorical percetion등을 실험하였다. 이때의 인공 신경망 모델은 Interactive Activation and Competition Model을 사용하였으며, 음성 입력으로는 가상의 음성 피쳐 데이타를 사용하였다.

• Nuclear Engineering and Technology
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• 제52권6호
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• pp.1188-1200
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• 2020

In this paper, we consider a SDMS (Self-Diagnostic Monitoring System) for a reciprocating pump for the purpose of not only diagnosis but also prognosis. We have replaced a multi class estimator that selects only the most probable one with a multi label estimator such that we are able to see the state of each of the components. We have introduced a measure called certainty so that we are able to represent the symptom and its state. We have built a flow loop for a reciprocating pump system and presented some results. With these changes, we are not only able to detect both the dominant symptom as well as others but also to monitor how the degree of severity of each component changes. About the dominant ones, we found that the overall recognition rate of our algorithm is about 99.7% which is slightly better than that of the former SDMS. Also, we are able to see the trend and to make a base to find prognostics to estimate the remaining useful life. With this we hope that we have gone one step closer to the final goal of prognosis of SDMS.

• Nuclear Engineering and Technology
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• 제46권1호
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• pp.101-108
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• 2014

About 40% of reactors in the world are being operated beyond design life or are approaching the end of their life cycle. During long-term operation, various degradation mechanisms occur. Fatigue caused by alternating operational stresses in terms of temperature or pressure change is an important damage mechanism in continued operation of nuclear power plants. To monitor the fatigue damage of components, Fatigue Monitoring System (FMS) has been installed. Most FMSs have used Green's Function Approach (GFA) to calculate the thermal stresses rapidly. However, if temperature-dependent material properties are used in a detailed FEM, there is a maximum peak stress discrepancy between a conventional GFA and a detailed FEM because constant material properties are used in a conventional method. Therefore, if a conventional method is used in the fatigue evaluation, thermal stresses for various operating cycles may be calculated incorrectly and it may lead to an unreliable estimation. So, in this paper, the modified GFA which can consider temperature-dependent material properties is proposed by using an artificial neural network and weight factor. To verify the proposed method, thermal stresses by the new method are compared with those by FEM. Finally, pros and cons of the new method as well as technical findings from the assessment are discussed.

• Nuclear Engineering and Technology
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• 제56권9호
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• pp.3925-3932
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• 2024

During outages at nuclear power plants, much more care for radiation workers against internal exposure should be ensured given that more hot particles exist relative to the amount during normal operation. If fuel-type hot particles (FTHP) are inhaled, they can cause more severe health risks compared to activation-type hot particles (ATHP), which contain ⁶⁰Co, due to the alpha-emitting nuclides within FTHPs. The activities of difficult-to-measure nuclides within FTHPs inhaled by workers are inferred by the age-dating technique using a¹⁴¹Ce/¹⁴⁴Ce ratio as measured by whole-body counters. However, this method may be limited to outages that last for only a few months due to the short half-life (32.5 days) of ¹⁴¹Ce. We studied the feasibility of utilizing ²⁴¹Am, a nuclide with a long half-life of 432.6 years, as an alternative to ¹⁴¹Ce. Additionally, we improved the performance of a stand-type whole-body counter for low-energy gamma spectroscopy to meet the criterion (RMSE ≤0.25) specified in ANSI/HPS N13.30-2011 by employing an artificial neural network (ANN). This study can contribute to more rapid and accurate internal dose assessments for workers who have inhaled FTHPs during long-term outages at nuclear power plants.

• Imaging Science in Dentistry
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• 제50권2호
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• pp.169-174
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• 2020

Purpose: Periodontal disease causes tooth loss and is associated with cardiovascular diseases, diabetes, and rheumatoid arthritis. The present study proposes using a deep learning-based object detection method to identify periodontally compromised teeth on digital panoramic radiographs. A faster regional convolutional neural network (faster R-CNN) which is a state-of-the-art deep detection network, was adapted from the natural image domain using a small annotated clinical data- set. Materials and Methods: In total, 100 digital panoramic radiographs of periodontally compromised patients were retrospectively collected from our hospital's information system and augmented. The periodontally compromised teeth found in each image were annotated by experts in periodontology to obtain the ground truth. The Keras library, which is written in Python, was used to train and test the model on a single NVidia 1080Ti GPU. The faster R-CNN model used a pretrained ResNet architecture. Results: The average precision rate of 0.81 demonstrated that there was a significant region of overlap between the predicted regions and the ground truth. The average recall rate of 0.80 showed that the periodontally compromised teeth regions generated by the detection method excluded healthiest teeth areas. In addition, the model achieved a sensitivity of 0.84, a specificity of 0.88 and an F-measure of 0.81. Conclusion: The faster R-CNN trained on a limited amount of labeled imaging data performed satisfactorily in detecting periodontally compromised teeth. The application of a faster R-CNN to assist in the detection of periodontally compromised teeth may reduce diagnostic effort by saving assessment time and allowing automated screening documentation.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• 융합보안논문지
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• 제24권3호
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• pp.95-104
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• 2024

미래 전장의 전쟁수행 역량은 AICBMS(AI, Cloud, Bigdata, Mobile, Security)라 일컫는 4차 산업혁명의 차세대 기술을 적용하여 혁신적인 국방력을 확보할 수 있는가에 달려 있다 해도 과언이 아니다. 또한, 미래의 군 작전환경은 네트워크를 기반으로 모든 무기체계가 하나의 통합된 정보통신망 내에서 실시간으로 전장정보를 상호공유하며 작전을 수행하게 되는 네트워크 중심전(NCW)으로 급변하고, 유·무인 복합전투체계 운용범위로 확대되고 있다. 특히, 초고속, 초연결성을 책임지는 통신 네트워크는 여러 전투 요소를 연결하고 정보의 원활한 유통을 위해 높은 생존성과 다계층(국방 모바일, 위성, M/W, 유선) 네트워크 기반의 전력 운용의 효율성을 요구한다. 이러한 관점에서 본 연구는 제원이 고정된 기존의 단일매체, 단일경로 전송과는 달리, 가용한 통신 유무선 인프라 다매체를 동시 사용하여 통신량 폭주시 부하분산과 RNN(Recurrent Neural Networks) 알고리즘을 이용한 인공지능 기반의 전송기술로 다매체다중경로(MMMP-Multi-Media Multi-Path) 적응적 네트워크 기술 개발하는 것이다.

• Imaging Science in Dentistry
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• 제52권4호
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• pp.351-357
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• 2022

Purpose: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising artificial intelligence methods in the field of medical and dental research. CNNs can provide an effective diagnostic methodology allowing for the detection of early-staged diseases. Therefore, this study aimed to evaluate the performance of a deep CNN algorithm for apical lesion segmentation from panoramic radiographs. Materials and Methods: A total of 1000 panoramic images showing apical lesions were separated into training (n=800, 80%), validation (n=100, 10%), and test (n=100, 10%) datasets. The performance of identifying apical lesions was evaluated by calculating the precision, recall, and F1-score. Results: In the test group of 180 apical lesions, 147 lesions were segmented from panoramic radiographs with an intersection over union (IoU) threshold of 0.3. The F1-score values, as a measure of performance, were 0.828, 0.815, and 0.742, respectively, with IoU thresholds of 0.3, 0.4, and 0.5. Conclusion: This study showed the potential utility of a deep learning-guided approach for the segmentation of apical lesions. The deep CNN algorithm using U-Net demonstrated considerably high performance in detecting apical lesions.