• Journal of the Korean Society for Railway
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• v.11 no.5
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• pp.482-488
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• 2008

Seoul Metro Line 9 (SML9) will link Gimpo Airport to the Gangnam district opening in May 2009. SML9 has a new model in Korea constructed by metropolitan government and private company and operated by specialized public transportation service provider. SML9 is confronted with tough environment of stagnated public transportation and strong competitor, Olympic city expressway. Consequently SML9 must lead the maximum efficiency by using its material and human resource. Hereupon, I propose renovation plans from 3 viewpoints of operation field of SML9: Rapid-Local combination, close connection with other transportation and direct connecting service into Incheon Air-port Railroad(AREX).

• Journal of the Society of Naval Architects of Korea
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• v.58 no.3
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• pp.129-136
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• 2021

Cavitation erosion is one of the major factors causing damage by lowering the structural strength of the marine propeller and the risk of it has been qualitatively evaluated by each institution with their own criteria based on the experiences. In this study, in order to quantitatively evaluate the risk of cavitation erosion on the propeller, we implement a deep learning algorithm based on a convolutional neural network. We train and verify it using the model tests results, including cavitation characteristics of various ship types. Here, we adopt the validated well-known networks such as VGG, GoogLeNet, and ResNet, and the results are compared with the expert's qualitative prediction results to confirm the feasibility of the prediction algorithm using a convolutional neural network.

• International Journal of Knowledge Content Development & Technology
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• v.9 no.1
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• pp.81-108
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• 2019

This research has investigated the perceptions of subject specialization services and the opinions of students majoring in library and information science preparing for librarianship, librarians operating in the field, and library directors on the status and ways of nurturing subject specialization for librarians, among others. To this end, based on the results of previous research and the survey questionnaire analysis, we have presented a policy to train subject librarians. First, we have proposed a plan for systematizing the current educational system within the department of library and information science. We have also suggested ways to secure subject expertise based on curriculum management, minor programs, multi-major programs, and interdisciplinary major programs based on the standard curriculum model. Second, we have presented a subject specialization educational system for field librarians, and further suggested details for the development of an educational program that can help build subject expertise and the operation of educational methods as well as the personnel in charge of implementing the educational programs. Third, we have proposed institutionalization of the qualifications of the subject librarian where the qualification requirements have been organized considering academic background, major program, library career, and career experience in the subject specialization service, further suggesting the implementation and maintenance of the system.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.40-49
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• 2019

Reinforcement learning has been applied to various problems in robotics. However, it was still hard to train complex robotic manipulation tasks since there is a few models which can be applicable to general tasks. Such general models require a lot of training episodes. In these reasons, deep neural networks which have shown to be good function approximators have not been actively used for robot manipulation task. Recently, some of these challenges are solved by a set of methods, such as Guided Policy Search, which guide or limit search directions while training of a deep neural network based policy model. These frameworks are already applied to a humanoid robot, PR2. However, in robotics, it is not trivial to adjust existing algorithms designed for one robot to another robot. In this paper, we present our implementation of Guided Policy Search to the robotic arms of the Baxter Research Robot. To meet the goals and needs of the project, we build on an existing implementation of Baxter Agent class for the Guided Policy Search algorithm code using the built-in Python interface. This work is expected to play an important role in popularizing robot manipulation reinforcement learning methods on cost-effective robot platforms.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• Structural Engineering and Mechanics
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• v.70 no.4
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• pp.457-466
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• 2019

This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.79-86
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• 2021

Alzheimer's is a chronic progressive disease which exhibits varied symptoms and behavioural traits from person to person. The deterioration in cognitive abilities is more noticeable through their Activities and Instrumental Activities of Daily Living rather than biological markers. This information discussed in social media communities was collected and features were extracted by using the proposed fuzzy logic based algorithm to address the uncertainties and imprecision in the data reported. The data thus obtained is used to train machine learning models in order to predict the longevity of the patients. Models built on features extracted using the proposed algorithm performs better than models trained on full set of features. Important findings are discussed and Support Vector Regressor with RBF kernel is identified as the best performing model in predicting the longevity of Alzheimer's patients. The results would prove to be of high value for healthcare practitioners and palliative care providers to design interventions that can alleviate the trauma faced by patients and caregivers due to chronic diseases.

• Safety and Health at Work
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• v.12 no.2
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• pp.217-224
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• 2021

Introduction: We evaluated depression and anxiety in service and sales workers from Korea who interacted with angry clients to identify factors that mediated and moderated depression and anxiety in these workers. Methods: This was a secondary analysis of data from the fifth Korean Working Conditions Survey conducted in 2017. A structural equation model was used for mediation and moderation analysis. Results: Service and sales workers who had more interactions with angry clients had increased risk for depression and anxiety. Experiencing clients' adverse behaviors (acute episodes) mediated the relationship between interacting with angry clients (a chronic situation) on depression and anxiety. Job satisfaction and managers' support moderated the relationship between interacting with angry clients and mental health problems. Conclusion: We suggest that employers of service and sales workers should recruit staff based on their aptitude for such work, thus ensuring job satisfaction, and train them to deal with angry clients in such a way that they experience less emotional burden. Employers should also make bylaws requiring managers to directly take care of adverse social behavior by clients. Furthermore, a sociocultural campaign to prevent adverse social behavior by clients is also needed.

• Journal of The Geomorphological Association of Korea
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• v.25 no.1
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• pp.97-113
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• 2018

Low-frequency geological natural disaster events such as Pohang earthquake have been occurred. As a results, there's a growing recognition on the importance of education and training for low frequency geological disasters in Korea. In spite of many years of scientific researches on volcanic disaster prevention and preparedness on Baekdusan volcano, the results do not provide the proper scenario for the training for volcanic ash event. Fall 3D volcanic ash diffusion model was run based on wind field data for the last five year, assuming Aso Mountain's explosion with volcanic explosion index 5 for seventy two hours. The management criteria values for proper actions in the previous studies were applied to make a scenario for thirteen groups of the disaster response teams such as train transportation, water supply, electrical facilities and human health. The models on the relationship between education and training for disaster prevention and response were suggested to fulfill the scientific and practical training at local level.

• Environmental and Resource Economics Review
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• v.30 no.2
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• pp.207-235
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• 2021

This paper examines the effects of ambient air pollution by ozone and particulate matter on traveling by mode of transport. We estimate the SUR model of travel time by different modes of transportation using individual level data of travel diaries. We find that, as air pollution levels rises, traveling by privately-owned vehicles increases but traveling by bus decreases. Our results also show that, when an air quality alert is issued, bus traveling increases in an effort to reduce pollution levels, but traveling by own car does not change and traveling by train declines. This suggests that alert programs may not be highly effective in reducing air pollution emissions from vehicles because voluntary switching to public transportation induced by air quality alerts is outweighed by individual effort of avoiding exposure to pollution.