• Korean Management Science Review
• /
• v.32 no.2
• /
• pp.1-14
• /
• 2015

The importance of technical innovation is increasing gradually in accordance with the life cycle of technology being shortened and the convergence being accelerate. This thesis aims to present the Framework of Competency Model of Techno Leadership capable of utilizing in promoting R&D (Research and Development) Leaders systematically by the technology based enterprise. The characteristic of core behavior necessary for Techno Leaders has been sorted out through the literature investigation and the analysis course of meaning, etc., the competency model of Techno Leadership has been deducted by analyzing the relation with MOT (Management of Technology) capacity being common skill of task necessary for Techno Leaders and the characteristic of behavior based on this through the questionnaire survey. The possibility of application in the field as well has been verified by applying the developed competency model in the actual field of enterprise. The competency model of techno Leadership developed in this research may be utilized in the direction establishment for the promotion of Techno Leadership in the enterprise or laboratory, especially, if would be effective to self-diagnose the core competency necessary for R&D manpower to be grown as the Techno leader in the IT oriented enterprise and to establish the improvement direction.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.4
• /
• pp.173-180
• /
• 2023

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.10
• /
• pp.112-122
• /
• 1996

In order to monitior machine tool condition and diagnose alarm states due to electrical and mechanical faults, and expert system using diagnostic parameters of NC machine tools was developed. A model-based knowledge base was constructed via searching and comparing procedures of diagnostic parameters and state parameters of the machine tool. Diagnostic monitoring results generate through a successive type inference engine were graphically displayed on the screen of the console. The validity and reliability of the expert system was rcrified on a vertical machining center equipped with FANUC OMC through a series of experiments.

• Reproductive and Developmental Biology
• /
• v.37 no.1
• /
• pp.41-49
• /
• 2013

Ovarian cancer is the most lethal world-wide gynecological disease among women due to the lack of molecular biomarkers to diagnose the disease at an early stage. In addition, there are few well established relevant animal models for research on human ovarian cancer. For instance, rodent models have been established through highly specialized genetic manipulations, but they are not an excellent model for human ovarian cancer because histological features are not comparable to those of women, mice have a low incidence of tumorigenesis, and they experience a protracted period of tumor development. However, the laying hen is a unique and highly relevant animal model for research on human ovarian cancer because they spontaneously develop epithelial cell-derived ovarian cancer (EOC) as occurs in women. Our research group has identified common histological and physiological aspects of ovarian tumors from women and laying hens, and we have provided evidence for several potential biomarkers to detect, monitor and target for treatment of human ovarian cancers based on the use of both genetic and epigenetic factors. Therefore, this review focuses on ovarian cancer of laying hens and relevant regulatory mechanisms, based on genetic and epigenetic aspects of the disease in order to provide new information and to highlight the advantages of the laying hen model for research in ovarian carcinogenesis.

• Smart Structures and Systems
• /
• v.5 no.2
• /
• pp.153-171
• /
• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• Journal of Aerospace System Engineering
• /
• v.7 no.1
• /
• pp.8-18
• /
• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• Journal of Multimedia Information System
• /
• v.3 no.2
• /
• pp.35-42
• /
• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• Journal of Korea Multimedia Society
• /
• v.25 no.11
• /
• pp.1547-1556
• /
• 2022

As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

• The Journal of Bigdata
• /
• v.2 no.2
• /
• pp.75-86
• /
• 2017

In this study, I propose a method to automate the method to diagnose the quality of big data. The reason for automating the quality diagnosis of Big Data is that as the Fourth Industrial Revolution becomes a issue, there is a growing demand for more volumes of data to be generated and utilized. Data is growing rapidly. However, if it takes a lot of time to diagnose the quality of the data, it can take a long time to utilize the data or the quality of the data may be lowered. If you make decisions or predictions from these low-quality data, then the results will also give you the wrong direction. To solve this problem, I have developed a model that can automate diagnosis for improving the quality of Big Data using machine learning which can quickly diagnose and improve the data. Machine learning is used to automate domain classification tasks to prevent errors that may occur during domain classification and reduce work time. Based on the results of the research, I can contribute to the improvement of data quality to utilize big data by continuing research on the importance of data conversion, learning methods for unlearned data, and development of classification models for each domain.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.1
• /
• pp.203-210
• /
• 2017

In this paper, we propose a electrical/mechanical method to effectively diagnose the local deterioration of a 10m long power shielded twist pair cable defined by the American Wire Gauge (AWG) 14 specification using electrical/mechanical methods. The rapid deterioration of the cable proceeded by using the heating furnace, which is based on the Arrhenius equations proceeds from 0 to 35 years with the deteriorated equivalent model. In this paper, we introduce a method to diagnose the characteristics of locally deteriorated cable by using $S_{21}$ phase and frequency change rate measured by vector network analyzer which is the electrical diagnostic method. The measured $S_{21}$ phase and rate of change of frequency show a constant correlation with the number of years of locally deteriorated cable, thus it can be useful for diagnosing deteriorated cables. The change of modulus due to deterioration was measured by a modulus measuring device, which is defined by the ratio of deformation from the force externally applied to the cable, and the rate of modulus change also shows a constant correlation with the number of years of locally deteriorated cable. Finally, By combining the advantages of electrical/mechanical diagnostic methods, we can efficiently diagnose the local deterioration in the power shielded cable.