• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.301-317
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• 2002

Recently, a geophysical exploration technology is frequently utilized in the civil engineering field as well as in the resource exploration. It might be important for civil engineers to understand the fundamental theory of seismic survey and limitation of the technique when utilizing these techniques in the civil engineering field. A 3-dimensional migration technique based on the principle of ellipsoid to predict the fractured zone ahead of tunnel face utilizing the tunnel seismic survey was proposed so that the geometry of the fractured zone can be estimated, i.e. the angle between tunnel axis and discontinuity zone, and the dip. Moreover, a numerical analysis technique to simulate the TSP (Tunnel Seismic Prediction) test was proposed in this paper. Based on parametric studies, the best element size, the analysis time step, and the dynamic characteristics of pressure source were suggested to guarantee the stability and accuracy of numerical solution. Example problems on a hypothetical site showed the possibility that the 3-dimensional migration technique proposed in this paper appropriately estimate the 3D-geometry of fractures ahead of tunnel face.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.434-441
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• 2016

Friction Stir Welding (FSW) is a solid-state joining process involving the frictional heat between the materials and tools. The amount of heat conducted into the workpiece determines the quality of the welded zone. Excessive heat input is the cause of oxides and porosity defects, and insufficient heat input can cause problems, such as tunnel defects. Therefore, analyzing the temperature history and distribution at the center of the Friction Stir Welded zone is very important. In this study, the temperature distribution of the friction stir welding region of an AZ61 magnesium alloy was investigated. To achieve this goal, the temperature and metal flow was predicted using the finite element method. In FE analysis, the welding tool was simplified and the friction condition was optimized. Moreover, the temperature measuring test at the center of the welding region was performed to verify the FE results. In this study, the tool rotation speed was a more dominant factor than the welding speed. In addition, the predicted temperature at the center of the welding region showed good agreement with the measurement results within the error range of 5.4% - 7.7%.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.151-166
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• 2020

A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.5
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• pp.35-44
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• 2011

PGA (Peak Ground Acceleration) is the parameter which indicates the peak value for strong ground motion and is mainly due to the intensity of the seismic wave. Usually, seismic waves can consist of different characteristics and can have different effects on structures. Therefore, it may be undesirable that the effects of a seismic wave are evaluated only based on the PGA. In this study, time history analysis was executed with a single degree of freedom model for inelastic seismic analysis. The numerical model was assumed to be a perfect elasto-plastic model. Input accelerations were made with El Centro NS (1940), other earthquake records and artificial earthquakes. The displacement ductility demand and cumulative dissipated energy, which were calculated from other artificial earthquakes, were compared. As a result, different responses from other seismic waves which have the same PGA were identified. Therefore, an index which could reflect both seismic and structural characteristics is needed. The SI (Spectrum Intensity) scale which could be obtained from integration by parts of the velocity response spectrum could be an index reflecting the inelastic seismic response of structures. It can be possible to identify from correlation analysis among the SI scale, displacement ductility demand and cumulative dissipated energy that the SI scale is sufficient to be an index for the inelastic response of structures under seismic conditions.

• Journal of the Korean Magnetics Society
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• v.15 no.4
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• pp.236-240
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• 2005

The magnetocaloric effect and magnetization behaviors have been studied for $La_{1-x}Pb_{x}MnO_3$ (x=0.1, 0.2, 0.3) alloys. The Curie temperature increased from 195 K to 352 K with increasing Pb concentration. A large magnetic entropy change (${\Delta}S_M$), which is calculated from H vs M curves associated with the ferromagnetic-paramagnetic transitions, has been observed. The maximum ${\Delta}S_M$ of $La_{0.8}Pb_{0.2}MnO_3$ was 1.22 J/kg K at 294 K for an applied field of 1.5 T. Adiabatic temperature change (${\Delta}T_ad$) was measured directly by a special cryostat. The maximum ${\Delta}T_ad$ of $La_{0.7}Pb_{0.3}MnO_3$ was 1.00 K at 352 K for an applied field of 2 T.

• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.971-976
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• 2009

This paper demonstrates the design and development of asset management systems suitable for the universities full of very various kind of assets. Universities consists of many departments, which have a multiplicity of many experimental. It is very difficult to record and manage assets with hands. In addition, the equipments are moving freely from one lab to another inside the school, which means it is tough to find the location of the assets and so some stuffs that are given lack attention are likely to disappear. So, these things occurring frequently in the university asset management environment should be considered in the design and embodiment of the asset management system. In the proposed system, location recognition of the assets is realized based on a route tracking method, so it is possible to detect the loss of the high priced assets and entrance, export, and lending of them are controlled efficiently. The system is likely to reduce the load of a manager responsible for asset management, because configured to decrease interventions of the manager in overall asset management process. Especially, the proposed system and implementation method will be suitable for small and medium-scale asset management, path tracking, history management.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.384-389
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• 2006

The needs for Light Railroad Transit(LRT) have been increased due to the heavy traffic congestions in large cities like Seoul, Korea. Korean government is seeking the LRT system development (including planning, designing, construction, and maintenance and operations) in terms of public-private-partnership (PPP). At the private sector side, it is crucial to estimate the life cycle cost (LCC) to project the cash flow during the O&M period. Since the most construction and O&M cost of LRT project is at the bridge construction, a cost analysis model and a cost breakdown structures (CBS) on LRT bridges are discussed through in depth literature reviews. Construction and maintenance cost of bridges are collected and analyzed. LCC is analyzed by types of bridge superstructures and historical data of repair and rehabilitation (R&R) is investigated. There have been scarce number of LCC analysis on railway bridges. This research delivers a well-defined CBS and maintenance cost data, which will be a great benefit to the systematic maintenance strategy development for railroad bridges.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.87-95
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• 2018

Due to global warming and increased energy costs around the world, interests of energy saving and efficiency have been increased. In particular, forging factories need methods to save energy and increase productivity because of needing amounts of energy uses. To solve the problem, we propose a system, which includes collection, monitoring, and analysis process, to monitor energy uses each facility in realtime based on the IoT devices. This system insists of worksheets management, facility/energy management, realtime monitoring, history search, data analysis through connecting with existed ERP/MES Systems in manufacturing factories. The energy monitoring process is to present used energy collected from IoT devices connected with installed gasmeter and wattmeter each facility. This system provide the change of energy uses, usage fee, energy conversion, and green gas information in realtime on Web and mobile devices. This system will be enhanced with energy saving technology by analyzing constructed big data of energy uses. We can also propose a method to increase productivity by integrating this system with functions of digitalized worksheets and optimized models for production process.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.7
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• pp.552-561
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• 2016

In this experimental study, the dynamic stability derivatives of a tailless lambda-shape UAV are estimated from time history data of aerodynamic moments measured from the internal balance while the test model is forced to oscillate at given frequencies and amplitudes. A 3-axis forced oscillation apparatus is designed to induce decoupled roll, yaw, pitch oscillations respectively. The results show that the roll damping derivatives remain stable at the entire range of angle of attack tested, whereas the pitch damping derivatives become unstable beyond $15^{\circ}$ angle of attack. The amplitude and frequency have little impact on roll damping derivatives while the smaller amplitude and frequency of oscillation improves the pitch stability. The yaw damping derivative values are fairly small as expected for a tailless configuration. The results indicate that the proposed methodology and test apparatus area valid for estimating the dynamic stability derivatives of a tailless UAV.