• Korean Chemical Engineering Research
• /
• v.46 no.2
• /
• pp.383-388
• /
• 2008

The safe and efficient operation of the chemical processes has become one of the primary concerns of chemical companies, and a variety of fault diagnosis methods have been developed to diagnose faults when abnormal situations arise. Recently, many research efforts have focused on fault diagnosis methods based on quantitative history data-based methods such as statistical models. However, when the history data-based models trained with the data obtained on an operation mode are applied to another operating condition, the models can make continuous wrong diagnosis, and have limits to be applied to real chemical processes with various operation modes. In order to classify operation modes of chemical processes, this study considers three multivariate models of Euclidean distance, FDA (Fisher's Discriminant Analysis), and PCA (principal component analysis), and integrates them with process dynamics to lead dynamic Euclidean distance, dynamic FDA, and dynamic PCA. A case study of the TE (Tennessee Eastman) process having six operation modes illustrates the conclusion that dynamic PCA model shows the best classification performance.

• 연구논문집
• /
• s.34
• /
• pp.47-58
• /
• 2004

Power plants rely on air operated valves for the proper operation of many plant system. Many significant problems arise in vital systems of power plants due to air operated valve failures. This paper deals with the diagnosis technique and data acquisition method of an AOV actuator peformance. We constructed AOV diagnosis system and performed some tests to find out whether an AOV actuator was properly designed.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.54
• /
• pp.155-165
• /
• 2000

The fault diagnosis is a systematic and unified method to find based on the observing data resulting in noises. This paper presents the fault prediction and diagnosis using fuzzy expert system technique to manipulate the uncertainties efficiently in predictive perspective. We apply a fuzzy event tree analysis to the computer system, and build up the fault prediction and diagnosis using fuzzy expert system that predicts and diagnoses the error of the system in the advance of error.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.3
• /
• pp.1047-1049
• /
• 2015

Background: This systematic analysis was conducted to investigate pathological diagnosis of vertebral tumor metastasis with unknown primaries. Methods: Clinical studies conducted to pathologically investigate vertebral tumor metastasis were identified using a predefined search strategy. Pooled diagnosis (PD) of each pathological confirmation was calculated. Results: For vertebral tumor metastasis, 5 clinical studies which included 762 patients were considered eligible for inclusion. Systematic analysis suggested that, for all patients with vertebral tumor metastasis, dominant PD was pathologically confirmed with lung cancer in 21.7% (165/762), with breast cancer in 26.6% (203/762) and with prostate cancer in 19.2% (146/762). Other diagnosis that could be confirmed included lymphoma, multiple myeloma, renal cancer, for example, in this cohort of patients. Conclusions: This systemic analysis suggested that breast, lung and prostate lesions could be the most common pathological types of cancer for vertebral tumor metastasis formunknown primaries, and other common diagnoses could include lymphoma, multiple myeloma, renal cancer.

• Journal of Korean Society for Quality Management
• /
• v.51 no.2
• /
• pp.223-245
• /
• 2023

Purpose: This study aim to identify the trends in AI-based PHM technology that can enhance reliability and minimize costs. Furthermore, this research provides valuable guidelines for future studies in various industries Methods: In this study, I collected and selected AI-based PHM studies, established classification criteria, and analyzed research trends based on classified fields and techniques. Results: Analysis of 125 domestic studies revealed a greater emphasis on machinery in both diagnosis and prognosis, with more papers dedicated to diagnosis. various algorithms were employed, including CNN for image diagnosis and frequency analysis for signal data. LSTM was commonly used in prognosis for predicting failures and remaining life. Different industries, data types, and objectives required diverse AI techniques, with GAN used for data augmentation and GA for feature extraction. Conclusion: As studies on AI-based PHM continue to grow, selecting appropriate algorithms for data types and analysis purposes is essential. Thus, analyzing research trends in AI-based PHM is crucial for its rapid development.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.3
• /
• pp.137-147
• /
• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2004.11a
• /
• pp.87-90
• /
• 2004

This paper describes development of power transformer maintenance system using intelligent dissolved gases in oil analysis. The used gases are acetylene(C2H2), hydrogen(H2), ethylene(C2H4), methane(CH4), ethane(C2H6), carbon monoxide(CO) and carbon dioxide(CO2). The rule and neural network based gas analysis methods are used for artificial intelligent diagnosis. It is indicated that this program is efficient for diagnosis of oil immersed transformers diagnosis from application of gas analysis data of serviced transformer which has local overheating

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.6
• /
• pp.2215-2217
• /
• 2015

Background: This systemic analysis was conducted to to evaluate the application value of positron emission tomography/computed tomography (PET/CT) in early diagnosis of lung cancer. Methods: Clinical studies evaluating the application value of PET/CT for patients underwent PET/CT imaging. The histological diagnosis served as the standard of truth. Results: Four clinical studies which including 1330 patients with pulmonary spaceoccupying lesions were considered eligible for inclusion. Systemic analysis suggested that, in all 1330 patients, pooled sensitivity was 98.7% (1313.2/1330) and specificity was 58.2%(276.85/476). Conclusion: This systemic analysis suggests that integrated PET/CT imaging provides high sensitivity, and reasonably high specificity, and could be applied for early diagnosis of lung cancer.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.11 no.2
• /
• pp.68-83
• /
• 2007

Background and Purpose: According to chaos theory, irregular signals of electroencephalogram can interpretated by nonlinear method. Chaotic nonlinear dynamics in EEG can be studied by calculating the correlation dimension. The aim of this study is to analyze EEG by correlation dimension and do Correlation Analysis of correlation dimension and K-POMS factors score. Method: EEG raw data were measured during 15 minutes and choosed 40 seconds. We calculated correlation dimension and used surrogate data method for checking nonlinear data. After then do correlation analysis. Result and Conclusion: Correlation dimension of channel 6, channel 7 and channel 8 are showed significant correlation with vigor factor.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.11
• /
• pp.82-90
• /
• 1995

This paper pressents a condition-based maintenance (CBM) method through bibration analysis. The well known frequency analysis is employed for performing machine fault diagnosis. The statistical control chart is also applied for analyzing the trend of the bearing wear. Vibration sensors are attached to prototype machine and signals are continuously monitored. The sampled data are utilized to evaluate how well the fast fourier transform(FFT) and the statistical control chart techniques could be used to identify defects of machine and to analyze the machine degradation. Experimental results show that the propowed approach could classify every mal-function and could be utilized for real machine diagnosis system.