• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1479-1501
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• 2020

Dependable functioning of dynamic networks is essential for delivering ubiquitous services. Faults are the root causes of network outages. The comparison diagnosis model, which automates fault's identification, is one of the leading approaches to attain network dependability. Most of the existing research has focused on stationary networks. Nonetheless, the time-free comparison model imposes no time constraints on the system under considerations, and it suits most of the diagnosis requirements of dynamic networks. This paper presents a novel protocol that diagnoses faulty nodes in diagnosable dynamic networks. The proposed protocol comprises two stages, a testing stage, which uses the time-free comparison model to diagnose faulty neighbour nodes, and a disseminating stage, which leverages a Random Linear Network Coding (RLNC) technique to disseminate the partial view of nodes. We analysed and evaluated the performance of the proposed protocol under various scenarios, considering two metrics: communication overhead and diagnosis time. The simulation results revealed that the proposed protocol diagnoses different types of faults in dynamic networks. Compared with most related protocols, our proposed protocol has very low communication overhead and diagnosis time. These results demonstrated that the proposed protocol is energy-efficient, scalable, and robust.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.4
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• pp.384-394
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• 2020

This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

• Journal of the Korean School Mathematics Society
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• v.7 no.1
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• pp.103-120
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• 2004

Today most of Korean students have no interest in mathematics and lack of confidence in it due to the burden of the college entrance examination, which often results in a failure or a dropout in mathematics in school. Therefore there is a need to diagnose the true causes and to find out a solution. As one of these solutions this study has developed and applied a learning model based upon cooperative group activities for the improvement of mathematical power to classroom. For developing this learning model a variety of research methods are used; questionnaires, observation, and analysis of the interview materials. After the application of this learning model, a lot of positive results in mathematics class have been observed.

• Imaging Science in Dentistry
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• v.43 no.3
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• pp.153-161
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• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• Transactions of the Korean hydrogen and new energy society
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• v.28 no.3
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• pp.252-272
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• 2017

Fuel cell systems have to satisfy acceptable operating reliability, sufficient lifetime and price to enter the market in competition with existing products. Fuel cells are made up of complex element technologies and various problems related to the failure of the components can affect the reliability and safety of the system. This problem can be overcome by introducing a monitoring and supervisory control system in addition to automatic control to detect the failure of the fuel cell quickly and properly diagnose the performance degradation. For the fault detection and diagnosis of polymer electrolyte fuel cells, the model based method using the theoretical superposition value and the non-model based method of checking the signal tendency or the converted signal characteristic can be applied. The methods analyzed in this paper can contribute to the development of integrated monitoring and control technology for the whole system as well as the stack.

• Ocean and Polar Research
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• v.39 no.4
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• pp.301-318
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• 2017

The production of Korean offshore fisheries has been gradually decreasing due to the severe depletion of offshore fisheries resources caused by excessive fishing efforts. The production of the offshore fisheries in 2016 was the lowest since 1975. So the federal and local governments in Korea adopted and implemented various fisheries management plans and policies in order to restore fisheries resources. However, these plans and polices have not been successful in re-establishing fisheries resources. Thus, in order to accurately diagnose the situation with regard to offshore fisheries, this study sought to estimate not only the return to scale by fishing gear of offshore fisheries, but marginal productivity of individual fishing gear based on production factors derived from offshore fisheries production functions. The study was organized in the following manner. First of all, this study estimates production functions of offshore fisheries. The Cobb-Douglas and the translog production functions are adopted as offshore fisheries production functions. Specifically, the functions are estimated by crew, vessels, and offshore resource as production factors. The offshore resource is estimated by the Clarke Yoshimoto Pooley model based on the surplus production model. Secondly, the fisheries production functions are extended to the fixed-effect model and the random-effect model with panel data. Thirdly, this study analyzes the return to scale of offshore fisheries and the marginal productivity of the production factors from the estimated offshore fisheries production function. In conclusion, this study suggests plans and countermeasures for productivity improvement by group (labor intensive or technology intensive) based on the characteristics of individual offshore fishing gear.

• Convergence Security Journal
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• v.12 no.4
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• pp.25-31
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• 2012

The SSE-CMM model is how to verify the level of information protection as a process-centric information security products, systems and services to develop the ability to assess the organization's development. The CMM is a model for software developers the ability to assess the development of the entire organization, improving the model's maturity level measuring. However, this method of security engineering process improvement and the ability to asses s the individual rather than organizational level to evaluate the ability of the processes are stopped. In this research project based on their existing research information from the technical point of view is to define the maturity level of protection. How to diagnose an information security vulnerabilities, technical security system, verification, and implementation of technical security shall consist of diagnostic status. The proposed methodology, the scope of the work place and the current state of information systems at the level of vulnerability, status, information protection are implemented to assess the level of satisfaction and function. It is possible that measures to improve information security evaluation based on established reference model as a basis for improving information security by utilizing leverage.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.223-228
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• 2022

In chemical processes, unintended faults can make serious accidents. To tackle them, proper fault diagnosis models should be designed to identify the root cause of faults. To design a fault diagnosis model, a process and its data should be analyzed. However, most previous researches in the field of fault diagnosis just handle the data set of benchmark processes simulated on commercial programs. It indicates that it is really hard to get fresh data sets on real processes. In this study, real faulty conditions of an industrial polystyrene process are tested. In this process, a runaway reaction occurred and this caused a large loss since operators were late aware of the occurrence of this accident. To design a proper fault diagnosis model, we analyzed this process and a real accident data set. At first, a mode classification model based on support vector machine (SVM) was trained and principal component analysis (PCA) model for each mode was constructed under normal operation conditions. The results show that a proposed model can quickly diagnose the occurrence of a fault and they indicate that this model is able to reduce the potential loss.

• Journal of the Society of Disaster Information
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• v.13 no.4
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• pp.570-576
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• 2017

Trucks take up more portions than cars on highways. Due to this, road use relatively diminish and it serves locally as a threatening factor to nearby drivers. Baggage car accident has distinct characteristics so that it needs the application of different analysis opposed to ordinary accidents. Accident prediction model, one of accident analyses, is used to predict the numbers of accident in certain parts, establish traffic plans as well as accident prevention methods, and diagnose the danger of roads. Thus, this study aims to apply the accident rate of baggage car on highways and calculate the correction factor to be put in the accident prediction models. Accident data based on highway was collected and traffic amounts and accident documents between 2014 and 2016 were utilized. The author developed an accident prediction model based on numbers of annual accidents and set mean annual and daily traffic amounts. This study intends to identify the practical accident prediction model on highway and present an appropriate solution by comparing the prediction model in accords with the accident rate between baggage cars.