• Proceedings of the Korean Statistical Society Conference
• /
• 2004.11a
• /
• pp.237-241
• /
• 2004

In standard time-to-event or survival analysis, the occurrence times of the event of interest are observed exactly or are right-censored. However in certain situations such as the AIDS data, the incubation period which is the time between HIV infection time and the diagnosis of AIDS is usually doubly censored. That is the HIV infection time Is interval censored and also the time of the diagnosis of AIDS is right censored. In this paper, we Impute the Interval censored infection time using the conditional mean imputation and estimate the coefficient factor of the regression analysis for the incubation period using Gibbs sampler. We applied parametric and semi-parametric methods for the analysis of the Incubation period and compared the results.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.14 no.1
• /
• pp.84-92
• /
• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.244-246
• /
• 2006

In this study, we search some parameters well-related to floating-sinking pulse by statistical analysis, because these pulses are frequently used in clinic. Pulse signals were acquired by 3D pulse analyzer and 30 subjects consist of 15 people diagnosed as floating pulse and 15 people diagnosed as sinking pulse by oriental doctors who have over 5 years clinical experience. Then, we made a representative beat template for each subject and, through a peak detection algorithm, we calculated several pulse parameters. To find the parameters related to floating-sinking pulse, we performed statistical testing with 17 parameters through the independence sampling, t-test. As a result, there is the biggest difference between pressure, the maximum pulse pressure, diastolic area(Ad) and float-sink data. (p < .05).

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.4
• /
• pp.105-110
• /
• 1998

In this study, statistical and neural network methods were used to recognize the cutting tool states. This system employed the tool dynamometer and cutting force signals which are processed from the tool dynamometer sensor using linear discriminent function. To learn the necessary input/output mapping for turning operation diagnosis, the weights and thresholds of the neural network were adjusted according to the error back propagation method during off-line training. The cutting conditions, cutting force ratios and statistical values(standard deviation, coefficient of variation) attained from the cutting force signals were used as the inputs to the neural network. Through the suggested neural network a cutting tool states may be successfully diagnosed.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.18 no.6
• /
• pp.619-623
• /
• 2008

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, it presents a study on the application of vibration signals to diagnose faults for a rotating machinery using the Mahalanobis distance-Taguchi system. RMS, crest factor and Kurtosis that is known as the statistical methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 1999.05a
• /
• pp.161-164
• /
• 1999

Estimation system of aging diagnosis using partial discharge(PD) is being highlighted as a research area for the residual lifetime pridiction of industrial equipment. But the application of PD requires complicated analysis method as expert system because the PD has complex progressing forms according to external stress. In this paper, it has been investigated the statistical distribution to express the 2D PD patterns of the diagnosis system using neural network(NN).

• Journal of Radiopharmaceuticals and Molecular Probes
• /
• v.7 no.1
• /
• pp.11-21
• /
• 2021

Radiopharmaceuticals are constantly being studied in the field of tumor diagnosis and therapy. As a result, many patents have been registered related to the development of radiopharmaceutical therapy. In this study, effective patents related to radiology and nuclear medicine filed during the past 10 years were collected from various countries like Korea, United States, Japan, Europe etc., and the application trends and growth stages were analyzed through statistical analysis. From the analysis results of 47,991 patents related to radiology and nuclear medicine, only 6,268 registered patents were found valid, and 80% of those were related to radiopharmaceutical development. In addition, we analyzed the patent of major competitors and used them to analyze the trends in radioisotopes and medicinal research. Among these, all the top 10 major applicants have found to be concentrating on radiopharmaceutical development.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.6
• /
• pp.503-510
• /
• 2002

This paper presents a fault diagnosis method using neural network-based multi-fault models and statistical method to detect and isolate faults in nonlinear systems. In the proposed method, faults are detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Journal of Applied Reliability
• /
• v.10 no.1
• /
• pp.39-56
• /
• 2010

Statistical process control (SPC) is a powerful technique for monitoring, managing, analysing and improving the process performance. However, its has limitations such as lack of engineering, statistical skill and training, and lesser importance of activity. To solve the problems, this study proposes an intelligent SPC system using specified agents which are derived through analysis and evaluation of the SPC activities. The activities investigated by the relevant researches are categorized as collection, process analysis, diagnosis, detection, cause analysis and rule generation. Also, the evaluation criteria are established as feasibility of automation, frequency, level and time. The requirements of the agent functions are derived by the evaluation, and the types of customized agents are as data collection, store, analysis, diagnosis, monitoring, alarm and reporting. A prototype SPC system represents that the functions of the proposed agents are successfully validated.

• Journal of Convergence Society for SMB
• /
• v.6 no.4
• /
• pp.79-84
• /
• 2016

A novel diagnosis scheme PCADP(personalized computer aided diagnosis probability) is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method based on ontology and it makes the bio-data analysis just a 'process' in the Smart healthcare service system. In addition, we offer a semantics modeling of the smart healthcare ontology framework in order to describe smart healthcare data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has real-time processing, characteristics of flexible structure, easy monitoring of decision process, and continuous improvement.