• 대한융합한의학회지
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• 제6권1호
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• pp.37-42
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• 2024

Objectives: This study aims to evaluate the clinical validity of the system by conducting a clinical study to assess the diagnostic agreement between the system and Korean medical doctors. Methods: This study was conducted from September 7, 2023, to December 7, 2023, across five Korean medicine institutions, involving 100 adult participants aged 20-64 who consented to participate. Participants first entered their symptoms into a web-based program, which utilized an AI-based algorithm to diagnose 36 types of pattern differentiation. Subsequently, Korean medical doctors conducted face-to-face diagnoses using the same 36 types. The diagnostic agreement between the system and the doctors' diagnoses was analyzed using descriptive statistical analysis, and the results were expressed as a percentage agreement. Results: Analysis of the diagnostic data from 100 participants revealed that the web-based diagnosis support system identified an average of 7.76±0.79 patterns per patient, while Korean medical doctors identified an average of 7.99±0.10 patterns per patient. The diagnostic agreement between the system and the doctors showed an average of 7.08±1.08 patterns per patient, with an overall diagnostic agreement rate of 88.57±13.31%. Conclusion: This study developed a web-based diagnosis support system for traditional Korean medicine and evaluated its clinical validity by assessing diagnostic agreement. Comparing the diagnoses of the system with those of Korean medical doctors for 100 patients, the system showed an approximately 89% agreement rate with the clinical diagnoses. The system holds potential for aiding Korean medical doctors in pattern differentiation diagnosis in clinical practice.

• 대한중풍순환신경학회지
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• 제13권1호
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• pp.63-70
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• 2012

Object : Traditional Korean Medicine Diagnostic Support System(TKMDSS) is the diagnostic prescribing system based on ontology developed by Korea Institute of Oriental Medicine. We monitored and assessed its usefulness and searched for improvements. Methods : We collected 10 cases of stroke inpatients of Dongguk University Ilsan Oriental Hospital. They were diagnosed by primary care physician and another researcher who monitored using "TKMDSS" respectively. We compared the process and results of two diagnosis. Results : The diagnostic concordance rate between primary care physician and researcher were pretty high. Most of the problems were caused by expressions on symptoms inappropriate use of terminology. The severity of symptoms and vague symptoms which is hard to be diagnosed should be reflected and measured in this system. Conclusions : The problems were about terminology and definition. The terminology should be defined accurately and in-depth detail so that anyone can get the right information. If the problems were modified, "TKMMSS" could be utilized as supportive measures for oriental medicine doctors and students.

• Nuclear Engineering and Technology
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• 제47권5호
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• pp.624-632
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• 2015

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 C
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• pp.1885-1887
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• 2000

In this paper we introduce preventative and diagnostic systems developed to prevent substations from accidental fault of electric power transmitting apparatus. We propose monitoring and diagnostic system for ultra high voltage GIS and main transformer of 765kV substations as an example of preventative and diagnostic techniques being applied in Korea. We also present a guideline to construct and manage an expert system for this purpose. Finally, an engineering solution as a substation management support system is proposed.

• Asian Pacific Journal of Cancer Prevention
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• 제14권1호
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• pp.97-102
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• 2013

Objective: The aim of this study was to develop an automated computer-aided diagnostic system for diagnosis of thyroid cancer pattern in fine needle aspiration cytology (FNAC) microscopic images with high degree of sensitivity and specificity using statistical texture features and a Support Vector Machine classifier (SVM). Materials and Methods: A training set of 40 benign and 40 malignant FNAC images and a testing set of 10 benign and 20 malignant FNAC images were used to perform the diagnosis of thyroid cancer. Initially, segmentation of region of interest (ROI) was performed by region-based morphology segmentation. The developed diagnostic system utilized statistical texture features derived from the segmented images using a Gabor filter bank at various wavelengths and angles. Finally, the SVM was used as a machine learning algorithm to identify benign and malignant states of thyroid nodules. Results: The SVMachieved a diagnostic accuracy of 96.7% with sensitivity and specificity of 95% and 100%, respectively, at a wavelength of 4 and an angle of 45. Conclusion: The results show that the diagnosis of thyroid cancer in FNAC images can be effectively performed using statistical texture information derived with Gabor filters in association with an SVM.

• 한국컴퓨터정보학회논문지
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• 제15권1호
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• pp.23-30
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• 2010

의료 분야의 정보화와 다양화로 인해 한의학 분야에서도 지능화된 서비스를 제공해주는 온톨로지 기반의 지능형 의료 시스템에 관한 연구가 진행되고 있다. 지능형 의료 시스템은 온톨로지를 이용하여 복잡한 의료지식 및 개인의 의료정보등을구조화함으로써진단을과학화시키고보다나은의료서비스를제공하게해준다. 본 논문에서는 온톨로지를 이용하여 기본적인 의학 데이터, 진단 시 발생되는 임상데이터, 개인의 신체정보와 같은 세 가지 지식을 표현하여 온톨로지로 구축함으로써 개인 맞춤형 진단을 내리는데 중요한 데이터로 활용한다. 특히, 한의학진단에서는 환자 개인의 병증과 체질 등에 따라 상이한 진단 및 처방이 내려질 수 있기 때문에 개개인의 신체정보 및 질병 정보를 이용하여 사용자의 상황에 맞는 맞춤형의 진단 및 처방 서비스를 제공 해주는 지능형 진단보조시스템이 유용하다. 따라서 본 논문에서는 환자 개개인에게 맞춤형의 진단 서비스를 제공하기 위한 방법으로 개인의 신체정보 및 질병정보를 이용하여 한의학 온톨로지를 구축하고, 추론을 통해 진단을 내리는 한의학 진단보조시스템을 구현하였다.

• Nuclear Engineering and Technology
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• 제46권6호
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• pp.725-736
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• 2014

It is very important to detect and identify small anomalies and component failures for the safe operation of complex and large-scale artifacts such as nuclear power plants. Each diagnostic technique has its own advantages and limitations. These facts inspire us not only to enhance the capability of diagnostic techniques but also to integrate the results of diagnostic subsystems in order to obtain more accurate diagnostic results. The article describes the outline of four diagnostic techniques developed for the condition monitoring of the fast breeder reactor "Monju". The techniques are (1) estimation technique of important state variables based on a physical model of the component, (2) a state identification technique by non-linear discrimination function applying SVM (Support Vector Machine), (3) a diagnostic technique applying WT (Wavelet Transformation) to detect changes in the characteristics of measurement signals, and (4) a state identification technique effectively using past cases. In addition, a hybrid diagnostic system in which a final diagnostic result is given by integrating the results from subsystems is introduced, where two sets of values called confidence values and trust values are used. A technique to determine the trust value is investigated under the condition that the confidence value is determined by each subsystem.

• Nuclear Engineering and Technology
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• 제54권7호
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• pp.2453-2466
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• 2022

Nuclear power plant operators in the main control room are exposed to stressful conditions in emergency situations as immediate and appropriate mitigations are required. While emergency operating procedures (EOPs) provide operators with the appropriate tasks and diagnostic guidelines, EOPs have static properties that make it difficult to reflect the dynamic changes of the plant. Due to this static nature, operator workloads increase because unrelated information must be screened out and numerous displays must be checked to obtain the plant status. Generally, excessive workloads should be reduced because they can lead to human errors that may adversely affect nuclear power plant safety. This paper presents a framework for an operator support system that can substitute the initial responses of the EOPs, or in other words the immediate actions and diagnostic procedures, in the early stages of an emergency. The system assists operators in emergency operations as follows: performing the monitoring tasks in parallel, identifying current risk and latent risk causality, diagnosing the accident, and displaying all information intuitively with a master logic diagram. The risk causalities are analyzed with a functional modeling methodology called multilevel flow modeling. This system is expected to reduce workloads and the time for performing initial emergency response procedures.

• 대한물리치료과학회지
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• 제16권2호
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• pp.45-55
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• 2009

Background: This thesis aims at suggesting the direction for the introduction of public free treatment support system according to the establishment of [Special Education Law] and the right settlement of therapeutic support service. Method: It introduced the characteristics and the contents of school based PT & OT, diagnosis and evaluation, and operation method. It set up question items and presented intervention plan, and substantial intervention, beginning from the request of whole process. The diagnostic evaluation was described from 4 aspects, which are consideration matters in the time of document drawing and diagnostic evaluation, chiefly centering around SOAP. The flow of overall treatment support service, the allocation of 16 handicapped children for 1 therapist, and the weekly treatment frequency according to the treatment support location and environment were suggested in the concrete operation method. Result: The concrete method should be explored in order to provide handicapped students with requisite services, which are offered by various experts in the amended 'Special Education Law'. In addition, work condition and social welfare, which are equal to school teachers, should be provided for all experts. Conclusion: Along with these things, special education support center should establish the road-map for the education rehabilitation of the handicapped children from the evaluation of early diagnosis of the handicapped children to treatment support and lifelong education.

• 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.