• Progress in Medical Physics
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• v.21 no.1
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• pp.22-28
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• 2010

To evaluate the potential value of $^1H$ Magnetic resonance spectroscopy (MRS) for detecting and characterizing invasive ductal carcinoma of breast. We conducted $^1H$ Magnetic resonance spectroscopy (MRS), using a 3.0T MR scanner, on 40 patients who were histologically diagnosed to have invasive ductal carcinoma (IDC); tumor areas of the patients were designated as experimental samples, and non-tumor areas as control samples. The peak at 3.2 ppm is characteristically intense and observed in 34 cases of the total 40 invasive ductal carcinoma (sensitivity 86.2%; specificity 100%; positive predictive value 100%; negative predictive value 60%). In constrast peak at 1.3 ppm is characteristically intense and observed in normal breast (sensitivity 86.2%; specificity 100%; positive predictive value 100%; negative predictive value 60%). The study shows that $^1H$ MRS can effectively discriminate invasive ductal carcinoma from normal breast in most cases. It also demonstrates the feasibility of localized in vivo $^1H$ MRS technique as a new diagnostic modality in the detection of breast tumor.

• Journal of the Korean Institute of Gas
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• v.26 no.4
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• pp.41-57
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• 2022

The global demand for carbon neutrality in response to climate change is in a situation where it is necessary to prepare countermeasures for carbon trade barriers for some countries, including Korea, which is classified as an export-led economic structure and greenhouse gas exporter. Therefore, digital transformation, which is one of the predictable ways for the carbon-neutral transition model to be applied, should be introduced early. By applying digital technology to industrial gas manufacturing facilities used in one of the major industries, high-tech manufacturing industry, and hydrogen gas facilities, which are emerging as eco-friendly energy, abnormal detection, and diagnosis services are provided with cloud-based predictive diagnosis monitoring technology including operating knowledge. Here are the trends. Small and medium-sized companies that are in the blind spot of carbon-neutral implementation by confirming the direction of abnormal diagnosis predictive monitoring through optimization, augmented reality technology, IoT and AI knowledge inference, etc., rather than simply monitoring real-time facility status It can be seen that it is possible to disseminate technologies such as consensus knowledge in the engineering domain and predictive diagnostic monitoring that match the economic feasibility and efficiency of the technology. It is hoped that it will be used as a way to seek countermeasures against carbon emission trade barriers based on the highest level of ICT technology.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.5
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• pp.20-29
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• 2024

The chloride diffusion coefficient is a critical indicator for assessing the durability of concrete marine substructures. This study develops a prediction model for the chloride diffusion coefficient using data from concrete bridges located in marine exposure zones (atmospheric, splash, tidal), an aspect that has not been considered in previous studies. Chloride profile data obtained from these bridge substructures were utilized. After data preprocessing, machine learning models, including Random Forest (RF), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN), were optimized through hyperparameter tuning. The performance of these models was developed and compared under three different variable sets. The first model uses six variables: water-to-binder (W/B) ratio, cement type, coarse aggregate volume ratio, service life, strength, and exposure environment. The second model excludes the exposure environment, using only the remaining five variables. The third model relies on just three variables: service life, strength, and exposure environment factors that can be obtained from precision safety diagnostics. The results indicate that including the exposure environment significantly enhances model performance for predicting the chloride diffusion coefficient in concrete bridges in marine environments. Additionally, the three variable model demonstrates that effective predictions can be made using only data from precision safety diagnostics.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.5
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• pp.436-442
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• 2009

Purpose: We underwent this study to evaluate the diagnostic potential of I-123/I-131 metaiodobenzylguanidine (MIBG) scintigraphy alone in the initial diagnosis of pheochromocytoma, compared with biochemical test and anatomic imaging. Materials & Methods: Twenty two patients (M:F=13:9, Age: $44.3{\pm}\;19.3$ years) having the clinical evaluation due to suspicious pheochromocytoma received the biochemical test, anatomic imaging modality (CT and/or MRI) and I-123/I-131 MIBG scan for diagnosis of pheochromocytoma, prior to histopathological confirmation. MIBG scans were independently reviewed by 2 nuclear medicine physicians. Results: All patients were confirmed histopathologically by operation or biopsy (incisional or excisonal). In comparison of final diagnosis and findings of each diagnostic modality, the sensitivities of the biochemical test, anatomic imaging, and MIBG scan were 88.9%, 55.6%, and 88.9%, respectively. And the specificities of the biochemical test, anatomic imaging, and MIBG scan also were 69.2%, 69.2%, and 92.3%, respectively. MIBG scan showed one false positive (neuroblastoma) and one false negative finding. There was one patient with positive MIBG scan and negative findings of the biochemical test, anatomic imaging. Conclusion: Our data suggest that I-123/I-131 MIBG scan has higher sensitivity, specificity, positive predictive value, negative predictive value and accuracy than those of biochemical test and anatomic imaging. Thus, we expect that MIBG scan is e tectively used for initial diagnosis of pheochromocytoma alone as well as biochemical test and anatomic imaging.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.6
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• pp.113-119
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• 2016

In this study, the method to predict the lateral response by using strain data is presented on the steel moment frame. For this, the reliability of the proposed method by applying the example of five-story frame structure were verified. Using the strain value of columns, it predicted the lateral response of structure. It is assumed that all of four strain sensors for one column set up and the strain responses of both end of the column are utilized. The lateral response of member is calculated by using the slope deflection method. Also, using the acceleration response of the one layer, the stiffness of the rotation spring located in the supporting point is predicted. As a result, it was effective to understand the lateral displacement and acceleration responses and to predict local damage and location.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.4
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• pp.87-96
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• 2017

It is important to estimate the future maintenance budget of all SOC infrastructure at the national strategic level. In this study, Based on a currently available statistics data, we predicted future maintenance investment for all SOC infrastructure in Korea. We have studied the applicable prediction models, and we developed the prediction models that can calculated the future maintenance cost by a real expenditure date. The subjects of facilities are bridges, tunnels, pavements, harbors, dams, airports, water supply, rivers and port. As a result of total estimated cost, eight types of SOC infrastructures are about 23 trillion won for the next 10years, and the most expensive facilities are road pavements and bridges.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.3
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• pp.122-129
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• 2015

In this study, a prediction model for the effective compressive strength of corner columns with intervening normal strength concrete slabs was developed. A structural analogy between high-strength concrete column-normal strength concrete slab joint and brick masonry was used to develop the prediction model. In addition, the aspect ratio of slab thickness to column dimension was considered in the models. The reliability of the new prediction model was evaluated by comparison with experimental results and its superiority was demonstrated by comparison with previous models proposed by design codes and other researchers. As a result, with average test-to-predicted ratios of 1.09, a standard deviation of 0.15, the newly developed equation provided superior predictions in terms of accuracy and consistency over all of the existing effective strength prediction approaches including KCI structural concrete design code (2012).

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.1
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• pp.125-132
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• 2020

The causes of concrete shrinkage are very diverse, in particular, aggregates impact the characteristics of shrinkage in concrete by constraining the shrinkage of cement paste. Meanwhile, owing to the lack of natural aggregate, various alternative aggregates are being developed, and their application in concrete also becomes more diverse. This study aimed to experimentally evaluate the drying and autogenous shrinkage in concrete that was composed of electric arc furnace slag as coarse aggregates. And, the results were compared with prediction models. From the results, the application of electric arc furnace slag can reduce the drying and autogenous shrinkage. In particular, autogenous shrinkage is greatly decreased. The predictions using GL2000 for drying shrinkage and Tazawa model for autogenous shrinkage were similar to the experimental results. However, the most prediction models do not consider the impact of aggregates, hence, the new prediction model should be developed or improved.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.301-302
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• 2011

2007년 12월 7일 발생한 허베이스피리트호 원유 유출사고를 계기로 국가 해양유류오염사고 대응체제 문제점이 부각되었고 이에 따라 해양 유류사고에 대한 대응 및 오염 진단, 복원을 지원할 수 있는 과학기술지원 체계 구축이 제기되었다. 본 논문에서는 실시간 해양오염 예측을 위해 필요한 해양 및 기상 예측 정보 통합 시스템을 소개한다. 본 시스템에서는 그리드 기술을 통한 해양 및 기상 예측 모델 수행에 필요한 사용자 환경 및 고성능 컴퓨팅 자원을 제공하고, 이를 통해 생성된 예측 자료를 통해 실시간 해양 오염 예측 정보를 생성하여 제공한다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.76-78
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• 2006

현재 심혈관 질환은 암 다음으로 높은 사망 원인으로 기록되고 있어 심혈관 질환에 대한 초기 진단은 질환의 치료에 매우 중요한 문제로 대두되고 있다. 본 논문에서는 SVM을 이용하여 심혈관질환 환자의 질환 단계를 예측하였다. 일반적으로 이진분류에 사용되는 SVM을 이용하여 정상 및 질환 $1{\sim}3$기의 총 4가지 분류가 필요한 다분류 분류문제를 처리하기 위해서 논문에서는 독립적 학습된 단일 SVM 분류기들을 결합하여 분류를 수행하는 SVM 앙상블 방법을 사용하였다. 단일 분류기의 결합은 Majority voting, 최소자승에러기반 가중치 부여, 2단계층 결합 등의 방법으로 수행하여 심혈관 질환 분류에 적합한 앙상블의 구성을 시도하였다. 실험 데이터는 (주)제노프라의 압타머 칩 데이터를 사용하였다. 서로 다른 데이터를 이용하여 학습된 이종의 SVM들을 결합한 결과 질환단계 예측에 있어서 단일 SVM을 이용하여 질환 단계를 예측하는 경우 보다 향상된 질환단계 예측 성능을 관찰할 수 있었으며, 심혈관 질환의 예측에 대해서는 단일 SVM 분류기의 2단 계층 결합법이 가장 좋은 성능을 보임을 확인하였다.