• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.33-40
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• 2003

Criticality prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development costs because the problems in early phases largely affect the quality of the late products. Real-time systems such as telecommunication systems are so large that criticality prediction is mere important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing causes of the prediction results and low extendability. This paper builds a new prediction model, GAM, based on Genetic Algorithm. GAM is different from other models because it produces a criticality function. So GAM can be used for comparison between entities by criticality. GAM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering Internal characteristics and accuracy of prediction.

• Journal of Korean Society of Rural Planning
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• v.15 no.4
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• pp.213-220
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• 2009

Farmland spatial data are needed as a basic information in conducting rational use of farmlands in regional scale. This study develops a method that can be used to make up such farmland spatial data in a simple way and to develop a technique to manage them in a unitary way, and examines the effectiveness of the technique by applying it to the case area. A method that Web-Service Raster Image and Digital Cadastal Map can be utilized as a base map was devised. It was designed applying the vector system, in which one lot of farmland is area unit. Raster image and field survey data were combined to increase the accuracy of data. The lot boundaries of the existing boundary map were adjusted to the shapes of actual farmlands using GIS edition function. A proper farmland use classification system to the area characteristics was established and data obtained from the field survey were coded. Usually it is very difficult to identify the size of one lot of actual farmland in the existing space data, based on the results of the case study, the result map showed actual topography very realistically. Also the frequently occurring lot divisions and the serious topographical modifications by natural disasters frequently have made it impossible to survey farmlands on the catastral map in the field. But the final map had a great usefulness in that it may solve such problems by expressing the filed survey results graphically.

• Journal of Chest Surgery
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• v.19 no.4
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• pp.606-617
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• 1986

During the period from November 1981 through June 1986, 18 cases of coronary arterial bypass graft were performed at Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital. They consisted of 13 males and 5 females with the mean age of 49 [range: 28-69 years]. History of myocardial infarction was noted in 50% of the patients and cardiomegaly on chest PA in 2 patients with preserved LV function. On resting EKG, except the evidences of old myocardial infarction, the findings of LVH were noted in 7 cases, acute myocardial infarction in 2, diffuse myocardial ischemia in 1, and significant ventricular arrhythmia in 2 cases. The angina by type of presentation is stable in 3 patients, unstable in 15 patients with resting, postinfarction and progressive angina as the criteria of unstability. The patterns of involvement of significant disease were single vessel involvement [5 cases] double vessel involvement [8 cases], and triple vessel involvement [5 cases] including 5 cases of left main coronary arterial diseases. The pattern of coronary arterial disease in individual patient was one or more stenosis of the proximal left coronary arterial system with or without right coronary involvement, in every case. We performed 9 cases of double bypass and 9 cases of triple bypass with great saphenous vein using single anastomosis technique except in 4 cases, One of the 4 cases is our first case, sequential anastomosis between LAD and diagonal was performed due to shortage of the prepared vein graft. In the other 3 cases, our latest experience, we adopted the left internal mammary artery for the left anterior descending coronary revascularization. The distribution of sites of distal anastomosis revealed more striking predilection to LAD, showing our attention on the significance of the revascularization of LAD system. The ischemic time was 35 minutes per graft and mean number of grafts per patient was 2.5. Of the 18 patients, 13 [77.2%] had complete revascularization, and incomplete in 5 cases with the causes of incompleteness as presented. The early results of operation were as followed: surgical death in 2 [11%], perioperative infarction 2 [11%], need of inotropic support 5 [28%], arrhythmia 2 [11%], wound problem, bleeding, and emotional dysfunction. The actuarial anginal free survival during the period of 6 months through 2 years was 85.2% with excellent symptomatic control according to the angina classification of Canadian Cardiovascular Society.

• Journal of Preventive Medicine and Public Health
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• v.37 no.3
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• pp.282-291
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• 2004

Objectives : The aim of this study was to investigate the functional status variables related to the care time of health professionals for patients in long-term care facilities. Methods : The functional stati of 1001 patients in 8 long-term care hospitals were examined by the Resident Assessment Instrument for Long-term Care Facility Version 2.0. The care time of health professionals for patients was calculated using data from a self-reported task survey by nurses, auxiliary nurses, private aides, doctors, physiotherapists and social workers. Results : The average care time per diem was 240.6 minutes. The care time by doctors, nurses and private aides were 11.0, 71.0 and 139.5 minutes, respectively. The lower the function of activities of daily living (ADL) and the greater the symptoms of extensive services, special care and clinical complexity, the more care time was served. On the contrary, the greater the symptoms of nursing rehabilitation, depression, cognitive disorder, behavior problem and psychiatry/mood disorder, the less care time was served. Age and gender were not significantly related to the care time. Conclusions : Developing a case mix classification system for elderly long term care patients may be helpful for both of patients and health care providers. The ADL, extensive services, special care and clinical complexity of variables should be considered in the development of a case mix system for the long term care of patients in Korea.

• Biomolecules & Therapeutics
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• v.27 no.3
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• pp.302-310
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• 2019

Melanoma cells have been shown to respond to BRAF inhibitors; however, intrinsic and acquired resistance limits their clinical application. In this study, we performed RNA-Seq analysis with BRAF inhibitor-sensitive (A375P) and -resistant (A375P/Mdr with acquired resistance and SK-MEL-2 with intrinsic resistance) melanoma cell lines, to reveal the genes and pathways potentially involved in intrinsic and acquired resistance to BRAF inhibitors. A total of 546 differentially expressed genes (DEGs), including 239 up-regulated and 307 down-regulated genes, were identified in both intrinsic and acquired resistant cells. Gene ontology (GO) analysis revealed that the top 10 biological processes associated with these genes included angiogenesis, immune response, cell adhesion, antigen processing and presentation, extracellular matrix organization, osteoblast differentiation, collagen catabolic process, viral entry into host cell, cell migration, and positive regulation of protein kinase B signaling. In addition, using the PAN-THER GO classification system, we showed that the highest enriched GOs targeted by the 546 DEGs were responses to cellular processes (ontology: biological process), binding (ontology: molecular function), and cell subcellular localization (ontology: cellular component). Ingenuity pathway analysis (IPA) network analysis showed a network that was common to two BRAF inhibitorresistant cells. Taken together, the present study may provide a useful platform to further reveal biological processes associated with BRAF inhibitor resistance, and present areas for therapeutic tool development to overcome BRAF inhibitor resistance.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• The Journal of Information Systems
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• v.31 no.1
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• pp.59-90
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• 2022

Purpose This paper presents a new method of classifying online platforms. It also explains how to apply the framework using case studies and generate new insight about platform strategies and policy development. Design/methodology/approach This paper focuses on the relationship between platforms, especially the hierarchy and power relations, and broadly classifies platforms as follows: content/services, meta information, app stores, operating systems, and cloud. Both the content/service platform and the meta information platform have matching as their main function. However, most content/services tend to collect and access information through meta-information platforms, so meta-information platforms are closer to infrastructure than content/service platforms. App store, operating system, and cloud can be said to be platforms of platforms. A small number of companies in the US and China dominate platforms of platforms, and become the recent development and regulatory targets of their respective governments. Findings We should be wary of the attempts to regulate domestic platforms by importing foreign regulations that ignore the hierarchical structure that our framework highlights. We believe that Korea's strategy to become a true platform powerhouse is clear. As one of the few countries with significant companies in the area of meta information platforms, it will be necessary to fully utilize the position and advance into the strategically important area of platforms of platforms. Furthermore, it is necessary to encourage world-class companies to appear in Korea in the app store, operating system, and cloud. To do so, the government needs to introduce promotion policies to strategically nurture such platforms rather than to regulate them.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.259-266
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• 2022

In this study, we developed a deep learning and vision sensor-based assembly performance evaluation method isfor prefabricated steel structures. The assembly parts were segmented using a modified version of the receptive field block convolution module inspired by the eccentric function of the human visual system. The quality of the assembly was evaluated by detecting the bolt holes in the segmented assembly part and calculating the bolt hole positions. To validate the performance of the evaluation, models of standard and defective assembly parts were produced using a 3D printer. The assembly part segmentation network was trained based on the 3D model images captured from a vision sensor. The sbolt hole positions in the segmented assembly image were calculated using image processing techniques, and the assembly performance evaluation using the k-nearest neighbor algorithm was verified. The experimental results show that the assembly parts were segmented with high precision, and the assembly performance based on the positions of the bolt holes in the detected assembly part was evaluated with a classification error of less than 5%.