• Proceedings of the KOR-KST Conference
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• 1995.05b
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• pp.69-76
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• 1995

Difficulties in riding , driving and parking are a common issue which inevitably appears in city urbanization and city modernization development all over the world. However, those cities differ in controlling the traffic. According to the specific practice in Shanghai, this article presents the various factors which affect the traffic capacity and operation quality . This article also outlines transportation system management technique of overall coordination and comprehensive control in the following seven aspects : controlling traffic motivation : improving traffic distribution : reasonably and scientifically selecting travel mode, travel route and travel time : reinforcing traffic management and road construction. The article finally suggests that the above mentioned points are cost-effective ways of the handling traffic plight for metropolitan transport.

• Proceedings of the KOR-KST Conference
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• 1995.05a
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• pp.61-68
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• 1995

Difficulties in riding, driving and parking are a common issue which inevitably appears in city urbanization and city modernization development all over the world. However, those cities differ in controlling the traffic. According to the specific practice in Shanghai, this article presents the various factors which affect the traffic capacity and the operation quality. This article also outlines transportation system management technique of overall coordination and comprehensive control in the following seven aspects: controlling traffic motivation; improving traffic distribution; reasonably and scientifically selecting travel mode, travel route and travel time; reinforcing traffic management and road construction. The article finally suggests that above mentioned points are cost-effective ways of handling traffic plight for metropolitan transport.

• Journal of Power Electronics
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• v.5 no.3
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• pp.198-205
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• 2005

The mechanical system of a drive can often be modeled as a two- or three-mass-system. The load is coupled to the driving motor by a shaft able to perform torsion oscillations. For the automatic tuning of the control, it is necessary to know the mathematical description of the system and the corresponding parameters. As the manpower and setup-time necessary during the commissioning of electrical drives are major cost factors, the development of self-operating identification strategies is a task worth pursuing. This paper presents an identification method which can be utilized for the assisted commissioning of electrical drives. The shaft assembly can be approximated as a two-mass non-rigid mechanical system with four parameters that have to be identified. The mathematical background for an identification procedure is developed and some important implementation issues are addressed. In order to avoid the excitation of the system with its natural resonance frequency, the frequency response can be obtained by exciting the system with a Pseudo Random Binary Signal (PRBS) and using the cross correlation function (CCF) and the auto correlation function (ACF). The reference torque is used as stimulation and the response is the mechanical speed. To determine the parameters, especially in advanced control schemes, a numerical algorithm with excellent convergence characteristics has also been used that can be implemented together with the proposed measurement procedure in order to assist the drive commissioning or to achieve an automatic setting of the control parameters. Simulations and experiments validate the efficiency and reliability of the identification procedure.

• New & Renewable Energy
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• v.18 no.3
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• pp.32-42
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• 2022

Rapid spread of intermittent renewable energy has amplified the instability and uncertainty of power systems. The Korea Power Exchange (KPX) promoted efficient management by opening the power brokerage market in 2019. By combining small-scale intermittent renewable energy with a flexible facility through the power brokerage market, the KPX aims to develop a virtual power plant system that will allow the conversion of existing intermittent renewable energy into collective power plants. However, the participation rate of renewable power owners in the power brokerage market is relatively low because other markets such as the small solar power contract market or the Korea Electric Power Corporation power purchase agreement are more profitable. In this study, we used a choice experiment to determine the attributes affecting the participation rate in the power brokerage market for 113 renewable power owners and estimate the value of the power brokerage market. According to the estimation results, a low smart meter installation cost, low profit variations, long contract periods, and few clearances increased the probability of participation. Moreover, the average value of the power brokerage market was estimated to be 2.63 million KRW per power owner.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.414-423
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• 2009

Folksonomy, which is collaborative classification created by freely selected keywords, is one of the driving factors of the web 2.0. Folksonomy has advantage of being built at low cost while its weakness is lack of hierarchical or systematic structure in comparison with taxonomy. If we can build classifier that is able to classify web resources from collective intelligence in taxonomy, we can build taxonomy at low cost. In this paper, targeting folksonomy in Slashdot.org, we define a general model and show that collective intelligence, which can build classifier, really exists in folksonomy using a stability value. We suggest method that builds SVM classifier using stability that is result from this collective intelligence. The experiment shows that our proposed method managed to build taxonomy from folksonomy with high accuracy.

• Information Systems Review
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• v.25 no.2
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• pp.145-162
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• 2023

Asset management is a complex and difficult field that requires insight into numerous variables and even human psychology. Thus, it has traditionally been the domain of professionals, and these services have been expensive to obtain. Changes are taking place in these markets, and the driving force is the digital revolution, so-called the fourth industrial revolution. Among them, the Robo-Advisor service using artificial intelligence technology is the highlight. The reason is that it is possible to popularize investment advisory services with convenient accessibility and low cost. This study aims to clarify what factors are critically important when selecting robo-advisors for service users and providers in Korea, and what perception differences exist in the selection factors between user and provider groups. The framework of the study was based on the marketing mix 4C model, and the design and analysis of the model used Delphi survey and AHP. Through the study design, 4 main criteria and 15 sub-criteria were derived, and the findings of the study are as follows. First, the importance of the four main criteria was in the order of customer needs > customer convenience > customer cost > customer communication for both groups. Second, looking at the 15 sub-criteria, it was found that investment purpose coverage, investment propensity coverage, fee level and accessibility factors were the most important. Third, when comparing between groups, the user group found that the fee level and accessibility factors were the most important, and the provider group recognized the investment purpose coverage and investment propensity coverage factors as important. This study derived useful implications in practice. First, when designing for the spread of the robo-advisor service, the basis for constructing a user-oriented system was prepared by considering the priority of importance according to the weight difference between the four main criteria and the 15 sub-criteria. In addition, the difference in priority of each sub-criteria shown in the group comparison and the cause of the sub-criteria with large weight differences were identified. In addition, it was suggested that it is very important to form a consensus to resolve the difference in perception of factors between those in charge of strategy and marketing and system development within the provider group. Academically, it is meaningful in that it is an early study that presented various perspectives and perspectives by deriving a number of robo-advisor selection factors. Through the findings of this study, it is expected that a successful user-oriented robo-advisor system can be built and spread in Korea to help users.

• International Commerce and Information Review
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• v.14 no.2
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• pp.249-269
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• 2012

The focus of our work is to identify and understand the drivers of alliance performance so that businesses can maximize their chances of a successful alliance-an area that has received little attention in empirical modeling. Although both conceptual and applied research on alliances has increased, an empirically tested comprehensive theoretical model that explains alliance performance has yet to be developed. Using five salient perspective, namely market power theory, transaction cost theory, the resource-based view, institutional theory, real option theory, this paper attempts to provide a theoretical rationale linking motives of global alliance networks on firm's capabilities, partner's capabilities, operating structures, and performances of Korean companies. The key contribution of this study is that it paints a picture of what matters in driving alliance performance. Our work shows the complex nature of driving performance and the interplay of firm's capabilities, partner's capabilities, and operating structures for understanding alliance performances. This study has given us a small but significant step forward towards understanding the intricacies of alliance performance. We are now better able to understand the respective roles played by various alliance factors and derive insights that lead to improved alliance performance.

• Journal of the Ergonomics Society of Korea
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• v.24 no.1
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• pp.9-17
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• 2005

81.1% of traffic accidents is attributed to the drivers. In this regard, D/E model is a practical and effective method in terms of the cost and time in evaluating the road hazardousness. To examine the validity of the threshold values by the levels of demand We selected 10 subjects and collected their physiological signals while they were driving on Honam Highway (Jeonju ${\leftrighttarro}$ Hoideog section). Based on the collected data, the hazardous road condition was evaluated using the new threshold values of the effort level determined by cluster analysis. In applying the D/E model, a decision method based on the demand level was suggested, using a traffic accident prediction model. Additionally, the limit value of the effort level was determined using the drivers' physiological signal data collected at the highway. A comparison analysis of the two D/E models revealed no significant difference: The existing method and the clustering method determined 9 and 7 hazardous road zones, respectively, while actual traffic accidents were reported in 6 and 4 zones, respectively among the predicted road hazardous zones. However, the latter method suggested a more scientific and rational basis in determining the limit value of the Effort level. In conclusion, although D/E model has a great merit as a pioneering method to reflect human factors in evaluating the road hazardousness, it is believed that this method could be improved by a more dynamic method that considers the traffic conditions and the individual physiological signal of the drivers simultaneously in determining a better limit.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.632-638
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• 2019

This paper presents the conceptual design and reaction force analysis of a bogie structure for an inter-modal automated transportation system, including road and rail transportation. The proposed system was based on a train with rotation-aligning bogie mechanism that can save significant time and cost. One of the critical issues in conceptual design is the lateral forces applied to the rail caused by the characteristic shapes and structure of the rails and bogie. In particular, the lateral forces are significant in the transition section between the driving and platform sections. This paper provides design guidance for the transition section through reaction force analysis. Based on the analysis result, it was confirmed that the proposed concept can be a valid design candidate of a practical system, and the radius of the rail and the distance between rails are major factors for reaction force generation.