• Journal of the Korean Society for Railway
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• v.20 no.1
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• pp.156-163
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• 2017

In the major operation sections of the urban railway, there has been habitual delay, and delay propagation; another problem is the increase of crowds and of inconvenience to passengers. The urban railway has different characteristics from rural railways, such as uncertainty of demand and irregularity of train operation. In urban railways, recently, operators manage quality indicators of service using operation results, such as the delay of train operation and the congestion of trains. However, because the urban railway has characteristics in which demand, passenger behavior, and train operation mutually affect each other, it is difficult to express the quality of service that passengers actually feel. In this paper, we suggest a quality indicator of service from the viewpoint of passengers, and present a demand responsive multi-train simulation method to predict dynamic dwell time and train operation status; we also use simulation results to consider changes in the quality indicator of service.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.440-447
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• 2016

Faced with the recent global economic recession, academics looked to the industrial B2B and B2C markets as potential new growth engines. In the B2B market, focusing on the relationship with customers, the relationship benefits are important, and institutional factors will affect the relationship and relationship values with customers. In this study, the relationship values between South Korea and Taiwan are compared by considering their national characteristics. By applying a mixed research method based on sequential exploratory design, 6 relationship values (supplier knowhow, service support, time to market, delivery performance, personal interaction, and product quality) are extracted. In particular, South Korea and Taiwan give priority to supplier knowhow and service support, respectively. Our research findings have both theoretical and practical implications for other emerging countries, as well as developed countries.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.17-36
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• 2022

The purpose of this study is to diagnose problems in the regulation and management of Restricted Development Zone and to prepare a construction plan to convert it to a data-based monitoring system. Unlike other land-use zones, the Restricted Development Zone is a exceptional zone that prohibits all development activities other than the minimum maintenance and must be strictly controlled and managed by the local government. However, the current Restricted Development Zone management is distributed according to the conditions of each local government, and it is not possible to monitor changes in the entire Restricted Development Zone as shown in the survey results. In particular, in this study, by introducing an AI-based monitoring system, MOLIT sends the results of detecting changes across the country at regular time points(monthly and quarterly) to the local governments based on the same regulation standards, and the local governments can be trusted while inputting the regulation results into the system. To propose this methodology, first, a survey and interview were conducted with local government officials and experts. Second, we analyzed cases in which AI analysis was applied to local governments and proposed a plan to improve the efficiency of regulation work according to the introduction of the monitoring system. Third, a plan was prepared to establish a monitoring system based on the advancement of the management information system. This monitoring system can be expanded and applied to land that needs periodic regulation and management in the future, and this study tried to propose a methodology and policy for this.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.375-386
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• 2008

Recently, development paradigm of information system is turning into object oriented and component based, and this methodology is leading the software industry. To acclimatize aptly to this trend, users demand the assessment of software expenses to change with the appropriate model of computing costs of the environment, and some people are actually studying the concept of Object Oriented Function Point and UCP method. Especially, Object Oriented Function Point Measurement Process has good points in overcoming the bound of LOC and the existing the Function Point Measurement Process because Object Oriented Function Point Measurement Process is applicable to the early stage of development project mainly with the used cases, and valid to the life long period as the each stage of software products develops, and always understandable to communicate with users by the UML mark rules. Accordingly, this research is to measure Functional Point at ROFP and AOFP in accordance with the development project of information system by the national defense CBD methodology procedures and UML Interrelation Analysis that are recently and widely used in the developmental environment of object oriented information system. Furthermore, this study suggests the measurement method to obtain Functional Point, and identifies service function and object/class function in the correlation analysis of use case and class based on the products and UML modeling via traditional FPA model and object oriented FPA model. Above all, this study is to demonstrate the improvement of traditional Function Point Measurement Process, IFPUG-CPM and software cost basis, and reveal Function Point Measurement Process, which is appropriate to the development of object oriented information system, and suggest the evaluation results of the compatibility through case studies.

• Spatial Information Research
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• v.14 no.4 s.39
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• pp.391-401
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• 2006

We propose a method for geographically enhanced representation of social networks and implement a Web-based 3D visualization of geographical contexts in social networks. A renovated social network graph is illustrated by using two key components: (i) GWCMs (geographically weighted centrality measures) that reflect the differences in interaction intensity and spatial proximity among nodes and (ii) MSNG (map-integrated social network graph) that incorporates the GWCMs and the geographically referenced arrangement of nodes on a choroplethic map. For the integrated 3D visualization of the renovated social network graph, we employ X3D (Extensible 3D), a standard 3D authoring tool for the Web. An experimental case study of regional R&D collaboration provides a visual clue to geographical contexts in social networks including how the social centralization relates to spatial centralization.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.284-294
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• 2020

As a solution to local and social problems, the active use of smart city living labs is becoming increasingly important. The answer to solving local and social problems lies in the citizen and the field. The purpose of this study is to establish a smart city living lab model based on citizen participation. In this study, smart city living lab model(4P-SCLLM) based on citizen participation was established through domestic and overseas living lab methodology and case analysis. In order to evaluate the systemicity and specificity of the 4P-SCLLM, a smart city living lab model, we recently compared it with the living lab process in Busan where smart city living lab is applied. As a result of analyzing, the analysis shows similar trends in each stage, and Busan's private sector showed a similar process to 4P-SCLLM On the other hand, public and private sector cooperation and support systems were found to be less than the 4P-SCLLM model And In technology and methodology, the 4P-SCLLM model is analyzed to have a living lab process that incorporates new technologies. In order to maintain the 4P-SCLLM continuously, first, participants and stakeholders need to participate actively and communicate while collaborating on the whole process from start to finish. Second, public awareness needs to be improved. Third, continuous citizenship verification of services is needed. Fourth, citizens' constant participation is needed. Through these implications, this study proposed 4P-SCLLM as a smart city living lab model suitable for the domestic situation.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.