• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.111-122
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• 2015

This study purposed on introducing a new automated solution for detecting railway tracks and reconstructing track models from the mobile laser scanning data. The proposed solution completes following procedures; the study initiated with detecting a potential railway region, called Region Of Interest (ROI), and approximating the orientation of railway track trajectory with the raw data. At next, the knowledge-based detection of railway tracks was performed for localizing track candidates in the first strip. In here, a strip -referring the local track search region- is generated in the orthogonal direction to the orientation of track trajectory. Lastly, an initial track model generated over the candidate points, which were detected by GMM-EM (Gaussian Mixture Model-Expectation & Maximization) -based clustering strip- wisely grows to capture all track points of interest and thus converted into geometric track model in the tracking by detection framework. Therefore, the proposed railway track tracking process includes following key features; it is able to reduce the complexity in detecting track points by using a hypothetical track model. Also, it enhances the efficiency of track modeling process by simultaneously capturing track points and modeling tracks that resulted in the minimization of data processing time and cost. The proposed method was developed using the C++ program language and was evaluated by the LiDAR data, which was acquired from MMS over an urban railway track area with a complex railway scene as well.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.121-123
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• 2002

The design of an air seat cushion for preventing decubitus ulcer includes many design factors such as the even distribution of interface pressure, the minimization of mean and peak interface pressure values, and the reduction of interface shear force and pressure gradient. It involves the anatomic condition of plegia's buttock as well as air pressure in air cells of cushion. As a result, a suitable design of the cushion satisfying the all requirements is a difficult problem. Therefore, an appropriate and effective numerical tool to develop an air cushion orthosis is required. The purpose of the present study was to develop an air seat cushion orthosis having optimized air cells for evenly distributed interface pressure between the buttock and cushion surface. For the purpose, an advanced finite element (FE) model for the design of air cushion was developed. Since the interface pressure and shear force behavior, as well as stress analyses were primary concern, a FE air cell model was developed and verified by the experiments. Then, the interactions of two cells were checked. Also, the human part of the developed numerical model includes every material property and geometry related to buttock and femoral parts. For construction of dimension data of buttock and femoral parts, CT scans were performed. A commercial FE program was employed for the simulation representing the seating process on the orthosis. Then, sensitive analyses were performed with varying design parameters. A set of optimal design parameters was found satisfying the design criteria of the orthosis. The results were utilized to produce a prototype of the orthosis. Experimentally, the buttock interface pressure distributions from the optimized and previous ones were compared. The new seat orthosis showed a significantly improved interface pressure characteristics compared to the most popular one in the market. The new orthosis will be used for the development of the AI(artificial intelligent) controlled seat orthosis fur prevention of decubitus ulcer fur various plegic patients and the elderly.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.105-117
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• 2010

In recent years, dynamic collaboration (DC) among cloud providers (CPs) is becoming an inevitable approach for the widely use of cloud computing and to realize the greatest value of it. In our previous paper, we proposed a combinatorial auction (CA) based cloud market model called CACM that enables a DC platform among different CPs. The CACM model allows any CP to dynamically collaborate with suitable partner CPs to form a group before joining an auction and thus addresses the issue of conflicts minimization that may occur when negotiating among providers. But how to determine optimal group bidding prices, how to obtain the stability condition of the group and how to distribute the winning prices/profits among the group members in the CACM model have not been studied thoroughly. In this paper, we propose to formulate the above problems of cooperative negotiation in the CACM model as a bankruptcy game which is a special type of N-person cooperative game. The stability of the group is analyzed by using the concept of the core and the amount of allocationsto each member of the group is obtained by using Shapley value. Numerical results are presented to demonstrate the behaviors of the proposed approaches.

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.478-487
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• 2015

This study proposed a Variable Speed Limits(VSL) algorithm to use traffic information based on Cumulative Demand-Capacity Analysis and evaluated its performance. According to the analysis result, the total of delay consisted of 3 separate parts. There was no change in total travel time although the total of delay decreased. These effects was analysed theoretically and then, evaluated through VISSIM, a microscopic simulator. VISSIM simulation results show almost same as those of theoretical analysis. Furthermore in SSAM analysis with VISSIM simulation log, the number of high risk collisions decreased 36.0 %. However, the total delay decrease effect is not real meaning of decrease effect because the drivers' desired speed is same whether the VSL model is operated or not. Nevertheless this VSL model maintains free flow speed for longer and increases the cycle of traffic speed fluctuation. In other words, this is decrease of delay occurrence and scale. The decrease of speed gap between upstream and downstream stabilizes the traffic flow and leads decrease number of high risk collision. In conclusion, we can expect increase of safety through total delay minimization according to this VSL model.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.21-30
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• 2010

In this paper, the parallel distributed rainfall runoff model(K-DRUM) using MPI(Message Passing Interface) technique was developed to solve the problem of calculation time as it is one of the demerits of the distributed model for performing physical and complicated numerical calculations for large scale watersheds. The K-DRUM model which is based on GIS can simulate temporal and spatial distribution of surface flow and sub-surface flow during flood period, and input parameters of ASCII format as pre-process can be extracted using ArcView. The comparison studies were performed with various domain divisions in Namgang Dam watershed in case of typoon 'Ewiniar' at 2006. The numerical simulation using the cluster system was performed to check a parallelization effectiveness increasing the domain divisions from 1 to 25. As a result, the computer memory size reduced and the calculation time was decreased with increase of divided domains. And also, the tool was suggested in order to decreasing the discharge error on each domain connections. The result shows that the calculation and communication times in each domain have to repeats three times at each time steps in order to minimization of discharge error.

• Database Research
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• v.34 no.3
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• pp.137-147
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• 2018

A key to a well-performing human activity recognition (HAR) system through machine learning technique is the availability of a substantial amount of labeled data. Collecting sufficient labeled data is an expensive and time-consuming task. To build a HAR system in a new environment (i.e., the target domain) with very limited labeled data, it is unfavorable to naively exploit the data or trained classifier model from the existing environment (i.e., the source domain) as it is due to the domain difference. While traditional machine learning approaches are unable to address such distribution mismatch, transfer learning approach leverages the utilization of knowledge from existing well-established source domains that help to build an accurate classifier in the target domain. In this work, we propose a transfer learning approach to create an accurate HAR classifier with very limited data through the multitask neural network. The classifier loss function minimization for source and target domain are treated as two different tasks. The knowledge transfer is performed by simultaneously minimizing the loss function of both tasks using a single neural network model. Furthermore, we utilize the unlabeled data in an unsupervised manner to help the model training. The experiment result shows that the proposed work consistently outperforms existing approaches.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.149-158
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• 2002

For the purpose of preciously describing real time traffic pattern in urban road network, dynamic network loading(DNL) models able to simulate traffic behavior are required. A number of different methods are available, including macroscopic, microscopic dynamic network models, as well as analytical model. Equivalency minimization problem and Variation inequality problem are the analytical models, which include explicit mathematical travel cost function for describing traffic behaviors on the network. While microscopic simulation models move vehicles according to behavioral car-following and cell-transmission. However, DNL models embedding such travel time function have some limitations ; analytical model has lacking of describing traffic characteristics such as relations between flow and speed, between speed and density Microscopic simulation models are the most detailed and realistic, but they are difficult to calibrate and may not be the most practical tools for large-scale networks. To cope with such problems, this paper develops a new DNL model appropriate for dynamic traffic assignment(DTA), The model is combined with vertical queue model representing vehicles as vertical queues at the end of links. In order to compare and to assess the model, we use a contrived example network. From the numerical results, we found that the DNL model presented in the paper were able to describe traffic characteristics with reasonable amount of computing time. The model also showed good relationship between travel time and traffic flow and expressed the feature of backward turn at near capacity.

• Journal of Korean Academy of Nursing
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• v.29 no.2
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• pp.314-327
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• 1999

In this study, domains, contents, and effects of pre-existed intervention programs for individuals with arthritis were meta-analyzed to develop arthritis health promotion program based on Holistic Model. The developed program includes strategies of cognition, environment, and behavior. and also generates positive changes in the physical, psychological, and social demensions. Then needs assessment on conveniently selected 153 women who visited a university hospital in Seoul or in Inchon are conducted to identify the objective domains of arthritis health promotion program According to the study results. target health problems of the arthritis health promotion program were shown as pain, disability, depression, and role impediment in social domain. These objectives could be achieved by including the strategies of changing cognition, the strategies of changing behavior through learning the skill related to the health promoting behavior. and the strategies of changing environment in the health promotion program. That is, it is analyzed that the contents of program are not exclusive one another in physical. psychological. and social demensions, and also are not exclusive one another in aspect of cognition, behavior, and environment. The necessary methods to achieve the desired objectives for the developed arthritis health promotion program and evaluation subjects are as follows : (1) In the arthritis health promotion program, knowledge on management of arthritis, efficacy related to arthritis management, skill for pain management, skill for exercise, establishment of positive self-concept, enhancement of positive thinking, stress management. skill for problem solving, skill for setting goals. skill for requesting help, and skill for communication are all included. Through the improvement of all those strategies, intermediate objectives, such as “joint protection, and maintenance of pain management behavior”, “maintenance of regular exercise”, and “promotion of coping skill in psychosocial dimension” are achieved. (2) These intermediate objectives are also the methods for achieving objectives in next stage. It implies that through the intermediate objectives. the final objectives such as “minimization of physical symptoms and signs”, “maximization of psychological function”, and “maximazation of role performance in social domain” could be achieved. Each of these final objectives reflects the different dimension of quality of life, respectively. When these objectives are achieved, the quality of life that client perceives is improved. Therefore, through evaluation of these final objectives, the level of achieving final outcome of arthritis health promotion such as quality of life is determined.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.61-74
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• 2010

ITS (Intelligent transportation systems) collects real-time traffic data, and accumulates vest historical data. But tremendous historical data has not been managed and employed efficiently. With the introduction of data management systems like ADMS (Archived Data Management System), the potentiality of huge historical data dramatically surfs up. However, traffic data in any data management system includes missing values in nature, and one of major obstacles in applying these data has been the missing data because it makes an entire dataset useless every so often. For these reasons, imputation techniques take a key role in data management systems. To address these limitations, this paper presents a promising imputation technique which could be mounted in data management systems and robustly generates the estimations for missing values included in historical data. The developed model, based on NPR (Non-Parametric Regression) approach, employs various traffic data patterns in historical data and is designated for practical requirements such as the minimization of parameters, computational speed, the imputation of various types of missing data, and multiple imputation. The model was tested under the conditions of various missing data types. The results showed that the model outperforms reported existing approaches in the side of prediction accuracy, and meets the computational speed required to be mounted in traffic data management systems.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.