• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.5
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• pp.125-135
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• 2004

The validity of simulation has been well-established for decades in areas such as computer and communication system. Recently, the technique has become entrenched in specific areas such as transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and their driver's characteristics, even though it is known that they are important factors for any traffic flow analysis, have never been considered sufficiently. In this paper, the traffic simulation using a multi-agent approach with considering vehicle dynamics is proposed. The multi-agent system is constructed with the traffic environment and the agents of vehicle and driver. The traffic environment consists of multi-lane roads, nodes, virtual lanes, and signals. To ensure the fast calculation, the agents are performed on the based of the rules to regulate their behaviors. The communication frameworks are proposed for the agents to share the information of vehicles' velocity and position. The model of a driver agent which controls a vehicle agent is described in the companion paper. The vehicle model contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation has proceeded for an interrupted and uninterrupted flow model. The result has shown that the driver agent performs human-like behavior ranging from slow and careful to fast and aggressive driving behavior, and that the change of the traffic state is closely related with the distance and the signal delay between intersections. The system developed shows the effectiveness and the practical usefulness of the traffic simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9B
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• pp.867-875
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• 2009

The reliability of wireless body area networks is an important research issue since it may jeopardize the vital human life, unless managed properly. In this article, a new modeling and analysis of node misbehaviors in wireless body area networks is presented, in the presence of multi-type failures. First, the nodes are classified into types in accordance with routing capability. Then, the node behavior in the presence of failures such as energy exhaustion and/or malicious attacks has been modeled using a novel Semi-Markov process. The proposed model is very useful in analyzing reliability of WBANs in the presence of multi-type failures.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.145-154
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• 2006

In the companion paper, the composition and structure of the MATDYMO (Multi-Agent for Traffic Simulation with Vehicle Dynamic Model) were proposed. MATDYMO consists of the road management system, the vehicle motion control system, the driver management system, and the integration control system. Among these systems, the road management system and the integration control system were discussed In the companion paper. In this paper, the vehicle motion control system and the driver management system are discussed. The driver management system constructs the driver agent capable of having different driving styles ranging from slow and careful driving to fast and aggressive driving through the yielding index and passing index. According to these indices, the agents pass or yield their lane for other vehicles; the driver management system constructs the vehicle agents capable of representing the physical vehicle itself. A vehicle agent shows its behavior according to its dynamic characteristics. The vehicle agent contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation is conducted for an interrupted flow model and its results are verified by comparison with the results from a commercial software, TRANSYT-7F. The interrupted flow model simulation is implemented for three cases. The first case analyzes the agents' behaviors in the interrupted flow model and it confirms that the agent's behavior could characterize the diversity of human behavior and vehicle well through every rule and communication frameworks. The second case analyzes the traffic signals changed at different intervals and as the acceleration rate changed. The third case analyzes the effects of the traffic signals and traffic volume. The results of these analyses showed that the change of the traffic state was closely related with the vehicle acceleration rate, traffic volume, and the traffic signal interval between intersections. These simulations confirmed that MATDYMO can represent the real traffic condition of the interrupted flow model. At the current stage of development, MATDYMO shows great promise and has significant implications on future traffic state forecasting research.

• Journal of Digital Convergence
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• v.13 no.3
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• pp.75-84
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• 2015

As the service industry has been getting increased attention as the core for the national economic growth, in order to promote growth and development of the industry, in-depth research on the workers in the industry has been timely called for. Therefore current study aims to explore the effect of leadership style on organizational citizenship behavior(ie OCB) for increased productivity of workers in the service industry, and the role of multi-dimensional justice in the relationship between them. The findings suggest that transformational and transactional leadership both have positive effect on OCB, implying that application of leadership style that is appropriate to the context may be more important, Also, mediation test of multi-dimensional justice resulted partial mediation effect except for the procedural justice in the relationship between transformational leadership and OCB.

• 한국노년학
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• v.36 no.3
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• pp.673-691
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• 2016

As the provision of long-term care policy takes root and with a gradual increase in elderly population, the use of elderly care service has become a growing norm. More than ever, there exists an urgent need for a paradigm shift in the building of an institutional basis for the improvement of care service, from the prevalent practice of 'need based service' toward the concept of 'human rights based service'. A great focus is being shed on care-workers, at the 'front line' of advocating human rights, as their human rights advocacy behaviour is seen as a key variable in providing high quality care service for elders. This study aims to examine how care-workers' individual human rights awareness levels, and the influence of their respective organizations, as an environmental factor, affect their human rights advocacy behaviour. The study includes a comprehensive analysis of the interactions between the regulatory effect of environmental factors (service orientation?) on an organizational level, human rights awareness (individual level) and the service environment (organizational). The analysis sample consisted of 782 registered non-profit corporation of long-term care facilities all over the country in 2014. The findings of the thesis suggest that human rights awareness at individual levels has a significant influence on human rights advocacy behavior. The interaction of human resources management in service orientations was also found to influence human rights advocacy on a significant level. Both human rights awareness at individual level and service orientations at organizational level were thus determined as key variables for improving the human rights awareness of care worker in long-term care facilities in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• Journal of the Korean Society of Safety
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• v.32 no.2
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• pp.72-77
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• 2017

In S. Korea, recently, building fire accidents of residential accommodations or recreational facilities have taken place more frequently than before. Among various building constructions, Multi-layered structure, such as office-residential complex, are mostly made in S. korea. $O_2$, $CO_2$, CO, $NO_x$, $SO_x$, and HCl, these gases has toxic hazard and harmful for human body. And it is predicted that different concentration of released gases from diesel pool fire with upper and lower layer. Therefore, this study reports the fire characteristics of Multi-layered structure by analyzing the fire behavior and concentration of combustion gases of a experimental compartment via real scale fire experiment, in order to predict risks and secure safety for similar fire accidents.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.

• Korean Journal of Human Ecology
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• v.19 no.4
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• pp.745-760
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• 2010

The purposes of this study were to investigate the state of indoor thermal and air environment during winter in the one-room type multi-family houses occupied by university students and to analyze factors which influenced this environment. Field survey was conducted in 10 houses between 30th January, 2009 and 13th February, 2009 which measured indoor thermal and air elements as well kept records of interviews with residents and other related factors. Measured elements were air temperature, relative humidity, as well as concentrations of $CO_2$, CO, TVOC, and PM-10. The results can be summarized as follows. 1) The mean air temperature in each house ranged from 19.3 to $25.3^{\circ}C$, so most houses were not suitable for evaluation criteria($20-22^{\circ}C$). The average $CO_2$ concentration in each house was 965~3259ppm, so most houses exceeded evaluation criteria(1000ppm). The average TVOC concentration in each house were 0.00~1.17ppm, 5 houses exceeded evaluation criteria(0.12ppm). 2) Relative humidity, CO concentration, and PM-10 concentration were suitable for evaluation criteria. Therefore, indoor thermal and air environment during winter in one-room type multi-family housing occupied by university students was found to be generally uncomfortable. Important factor which were found to influence air temperature and the concentration of $CO_2$ were smaller space capacity than general house. Other factors which were found to influence the environment of these houses were the existence of a balcony as well as factors relating to the behavior of occupants such whether or not heating were operated, whether windows were opened, whether fans used, whether occupants smoked or used cosmetics, and whether the space was dusted.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.2
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• pp.145-166
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• 2019

While a significant number of user-related models have been presented in Human Information Behavior (HIB) research community, the basic assumption of the present study is most of those models including information interaction models are multi-domain models associated with comprehensive research components. Based on such an assumption, this study discusses the shortcomings of multi-domain models and proposes the need to present a new type of model. Accordingly, the study elaborates four essential models of HIB reach community and presents a new type of model based on Specific Domain Focus Modeling (SDFM). As an example of such modeling, this study presents the present author's information retrieval interaction model based on the degree of connections between systems and information resources.