• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.466-475
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• 2017

Various factors in daily life cause stress, which could stem from trivial or major events. Stress is not appeared at any time but is appeared over the lifetime. Therefore, it is necessary to conduct studies to identify ways to manage stress. In the present study, we developed an iris analysis-based system to diagnose and manage stress. By analyzing its correlation with the degree of stress measured using the iris diagnosis and a questionnaire, the user's degree of stress quantitates. The system proposed in this study can be used to measure the degree of stress experienced by the user, which can be an effective method for the early diagnosis and prevention of stress-related diseases.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.183-192
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• 2022

Function and performance degradation in tunnel structure can cause road's function loss, proactive maintenance is needed to prevent the initial damage from progressing to intensified damage. Inspection and diagnosis are implemented in accordance with regulations, but it does not fully reflect maintenance processes such as inspection and diagnosis, planning rehabilitation and carrying out. It was carried out for 5,540 cases inspection and diagnosis result in 1,728 tunnels was analyzed to suggest rational maintenance plan in this study.

• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• Journal of the Korean Institute of Gas
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• v.4 no.4 s.12
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• pp.42-49
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• 2000

Due to their size and complexity, it is very difficult to make diagnostic system for the whole chemical processes. Therefore, a systematic approach is required to decompose larpge-scale process into sub-processes and then diagnose them. This paper suggests a method for the minimization of knowledge base and flexible diagnosis to be used in qualitative fault diagnosis based on Fault-Effect Tree model. The system can be decomposed for flexible diagnosis, size reduction of knowledge base, and consistent construction of complex knowledge base. The new node, gate-variable, is introduced to connect the cause-effect relationships of each sub-process. For on-line diagnosis, off-line analysis is performed to construct Fault-Effect Trees of gate-variables as well as activation conditions of gate-variables. On-line diagnosis strategy is modified to get the same diagnosis result without system decomposition. The proposed method is illustrated with a fault diagnosis system for a large-scale boiler plant.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1232-1234
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• 2002

In this paper, as a basic study for the production of precisional diagnosis guidelines of the transformer used in railway vehicles, the stress and ageing cause of the transformer is analyzed, the international standards and diagnosis items relative to it are invested and rearanged.

• Journal of Mechanical Science and Technology
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• v.20 no.9
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• pp.1410-1417
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• 2006

The metal processing system usually consists of various components such like motors, work rolls, backup rolls, idle rolls, sensors, etc. Even a simple fault in a single component in the system may cause a serious damage on the final product. It is therefore necessary to diagnose the faults of the components to detect and prevent system failure. Especially, the defects in a work roll are critical to the quality of strip. It is especially difficult to detect faults of a roll by using the existing frequency analysis method if the speed of the roll is changing. In this study, a new diagnosis method for roll eccentricity under the roll speed changes was developed. The new method was induced from analyzing the rolling mechanism by using rolling force models, radius-speed relationship, and measured rolling force, etc. Simulation results by using the field data show that the proposed method is very useful.

• Journal of Yeungnam Medical Science
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• v.33 no.2
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• pp.77-84
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• 2016

Pancreatic cancer, the 4th leading cause of cancer-related death in the United States, has a very poor prognosis. Cholangiocarcinoma originates from either intrahepatic or extrahepatic bile duct, and its incidence is gradually increasing worldwide. Endoscopic retrograde cholangiopancreatography with brush cytology has a high false-negative rate for the diagnosis of biliary malignancy. Recently, endoscopic ultrasonography (EUS) has emerged as the potential modality to detect pancreatic cancer. EUS-guided fine needle aspiration for cytologic analysis made it possible to overcome the obstacle in differentiating between benign and malignant lesions in the pancreatobiliary lesion, and it has been well established as a safe and effective procedure. Herein, the clinical application of EUS in the diagnosis of pancreatobiliary cancer was reviewed.

• Proceedings of the Korea Concrete Institute Conference
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• 1994.04a
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• pp.243-248
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• 1994

The purpose of this study is to develop Deterioration Diagnosis System for the Reinforced Concrete Structure which can be used preliminary in determining the factors causing deterioration by simple inspection and mapping of the indicators of deterioration. Total 29 items compromising material, structural and constructional factors causing deterioration were considered in this system. Also the indicators of deterioration were subdivided into 54 items such as concrete crack pattern and steel corrosion etc. Each indicator of deterioration was quantatified by allocating and giving grade to each item which has extra weight according to its conscquence. Satisfactory results were obtatined by applying this Diagnosis system to the indicators of deterioration in ref. [3]. Further research was required on the indicators of deterioration in construction site to enhance the field applicability of this system.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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• pp.375-383
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• 2001

As many industrial systems become more complex, it becomes extremely difficult to diagnose the cause of failures. This paper presents a failure diagnosis approach based on discrete event system theory. In particular, the approach is a hybrid of event-based and state-based ones leading to a simpler failure diagnoser with supervisory control capability. The design procedure is presented along with a pump-valve system as an example.