• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.292-297
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• 2004

The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) which is independent of the CNC vendor. The OAC and machine tools with OAC led the convenient environment where it is possible to implement user-defined application programs efficiently within CNC. Tis paper proposes a method of operational fault cause diagnosis which is based on the status of programmable logic controller (PLC) in machine tools with OAC. The operational fault is defined as a disability state occurring during normal operation of machine tools. The faults are occupied by over 70% of all faults and are also unpredictable as most of them occur without any warning. Two diagnosis models, the switching function (SF) and the step switching function (SSF), are propose in order to diagnose the fault cause quickly and exactly. The cause of an occurring fault is logically diagnosed through a fault diagnosis system (FDS) using the diagnosis models. A suitable interface environment between CNC and develope application modules is constructed in order to implement the diagnostic functions in the CNC domain. The diagnosed results were displayed on a CNC monitor for machine operators and provided to a remote site through a web browser. The result of his research could be a model of the fault cause diagnosis and the remote monitoring for machine tools with OAC.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.417-419
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• 2023

Hypertension is one of the leading causes of mortality worldwide. In recent years, the incidence of hypertension has increased dramatically, not only among the elderly but also among young people. In this regard, the use of machine-learning methods to diagnose the causes of hypertension has increased in recent years. In this study, we improved the prediction of hypertension detection using Mahalanobis distance-based multivariate outlier removal using the KNHANES database from the Korean national health data and the COVID-19 dataset from Kaggle. This study was divided into two modules. Initially, the data preprocessing step used merged datasets and decision-tree classifier-based feature selection. The next module applies a predictive analysis step to remove multivariate outliers using the Mahalanobis distance from the experimental dataset and makes a prediction of hypertension. In this study, we compared the accuracy of each classification model. The best results showed that the proposed MAH_RF algorithm had an accuracy of 82.66%. The proposed method can be used not only for hypertension but also for the detection of various diseases such as stroke and cardiovascular disease.

• East Asian Journal of Business Economics (EAJBE)
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• v.8 no.1
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• pp.21-40
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• 2020

Purpose - The labor management department is an important area in charge of the labor-management relations that affects the competitiveness of the company. This study seeks to diagnose labor management education focusing on labor manager competency strengthening curriculum that is currently being conducted domestically and propose an educational model that can contribute to the labor manager competency development by researching improvement measures. Research design, data, and methodology - In this study, the first phase is a Delphi open form survey and 15 expert panels participated. The second phase had 31 expert panels participating and in the final IPA analysis, targeting 111 on-site subjects, it conducted a survey regarding desired level of current educational level and future education requirement. Results - A final 57 subjects regarding 11 items to increase the competency of the labor managers through the first and second Delphi survey was deduced through this study. To add, regarding the current education level and desired level that the current workers are thinking with respect to analysis results of the 57 subjects through the IPA analysis, an educational model could be deduced to increase competency of the labor managers based on the result. Conclusions - Thus far, research regarding labor management has been insufficient as it was defined as a subordinate role to human resources. This study reviews the role and competency of labor managers and presented an educational model to strengthen the capabilities of internal labor managers to re-illuminate the labor manager. However, this study is limited in terms of the diversity of the types of companies participating and the small number of panels. Better data can be produced if such parts are supplemented in the future.

• Journal of Astronomy and Space Sciences
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• v.14 no.1
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• pp.166-178
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• 1997

Many research works are on going to monitor and diagnose diverse malfunctions of satellite systems as the complexity and number of satellites increase. Currently, many works on monitoring and diagnosis are carried out by human experts but there are needs to automate much of the routine works of them. Hence, it is necessary to study on using expert systems which can assist human experts routine work by doing automatically, thereby allow human experts devote their expertise more critical and important areas of monitoring and diagnosis. In this paper, we are employing artificial intelligence techniques to model human expert's knowledge and inference the constructed knowledge. Especially, case-based approaches are used to construct a knowledge base to model human expert capabilities which use previous typical exemplars. We have designed and implemented a prototype case-based system for diagnosing satellite malfunctions using cases. Our system remembers typical failure cases and diagnoses a current malfunction by indexing the case base. Diverse methods are used to build a more user friendly interface which allows human experts can build a knowledge base in an easy way.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.87-92
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• 2007

Recently, developments of process monitoring system in order to detect and diagnose process abnormalities has got the spotlight in process systems engineering. Normal data obtained from processes provide available information of process characteristics to be used for modeling, monitoring, and control. Since modern chemical and environmental processes have high dimensionality, strong correlation, severe dynamics and nonlinearity, it is not easy to analyze a process through model-based approach. To overcome limitations of model-based approach, lots of system engineers and academic researchers have focused on statistical approach combined with multivariable analysis such as principal component analysis (PCA), partial least squares (PLS), and so on. Several multivariate analysis methods have been modified to apply it to a chemical process with specific characteristics such as dynamics, nonlinearity, and so on.This paper discusses about missing value estimation and sensor fault identification based on process variable reconstruction using dynamic PCA and canonical variate analysis.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Journal of Hydrogen and New Energy
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• v.34 no.6
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• pp.555-561
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• 2023

In this study, we proposed a standard model for the design, construction and demonstration of the technology development and substantiation of small hydrogen powered vessel in order to respond to the alternative fuel-using vessel market that requires the use of low-carbon/carbon-free fuel as a greenhouse gas reduction measure. The hydrogen fuel cell-based electric propulsion system developed through this is optimized through performance and durability tests on the land-based test site (LBTS), and the electric propulsion system applied to this result is mounted on a small hydrogen propulsion vessel and operated. Simultaneously, through the digital twin technology between the LBTS and the hydrogen-propelled vessel on the sea, the technology that can predict and diagnose the problems that can occur in the electric propulsion system of the vessel is applied to carry out the empirical study of the hydrogen-propelled vessel. In addition, we propose a commercialization model by analyzing the economic feasibility of the demonstration vessel.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.812-818
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• 2020

Cervical cancer is the second most common female cancer in the world. In Korea, cervical cancer accounts for 13 percent of female cancers and 4,200 cases occur annually[1]. The purpose of this study is to use a deep learning model to identify the possibility of lesions in the cervix and to evaluate the efficient image preprocessing in order to diagnose diverse types of cervix in form. The study used 4,107 normal photographs of uterine cervix and 6,285 abnormal photographs of uterine cervix. Two types of image preprocessing were resized to square. The methods are cropping based on height and filling the space up and down with black images. In addition, all images were resampled to 256×256. The average accuracy of cropped cases is 94.15%. The average accuracy of the filled cases is 93.41%. According to the study, the model performance of cropped data was slightly better. But there were several images that were not accurately classified. Therefore, the additional experiment with pre-treatment process based on cropping is needed to cover images of the cervix in more detail.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.2
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• pp.181-200
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• 2013

The test instrument utilized to diagnose and evaluate a trainee's ability are necessary for an effective information literacy education. Nevertheless, there is a lack of a standardized test instrument to comprehensively measure students' information literacy. The purpose of this study is to develop a standardized test instrument to evaluate the information literacy of middle school students, and to verify the reliability and validity of the test instrument. For this purpose, this study selected factors that can show the information literacy and developed an information literacy test framework that was designed based on Guilford's SOI model and Meeker's SOI-LA test. The test instrument that was developed through this study is a 30-item Web-based multiple-choice test. This study administrated tests in middle school students (794 students joined), and analyzed difficulty, reliability, discrimination index, validity of tests, and reviewed tests items to qualify the standardized test. The cutoff score was also decided when using these tests as a diagnostic information literacy assessment.

• Journal of Hydrogen and New Energy
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• v.29 no.4
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• pp.339-356
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• 2018

Although fuel cell systems have advantages in terms of electric efficiency and environmental impact compared with conventional power systems, fuel cell systems have not been deployed widely due to their low reliability and high price. In order to guarantee the lifetime of 10 years, which is the commercialization goal of Polymer electrolyte fuel cells (PEFCs), it is necessary to improve durability and reliability through optimized operation and maintenance technologies. Due to the complexity of components and their degradation phenomena, it's not easy to develop and apply the diagnose and prognostic methodologies for PEFCs. The purpose of the paper is to show the current state on PEFC prognostic technology for condition based maintenance. For the prognostic of PEFCs, the model driven method, the data-driven, and the hybrid method can be applied. The methods reviewed in this paper can contribute to the development of technologies to reduce the life cycle cost of fuel cells and increase the reliability through prognostics-based health management system.