• Korean Journal of Computational Design and Engineering
• /
• v.10 no.1
• /
• pp.61-69
• /
• 2005

In the global ship structure and vibration analysis, the FE(finite element) analysis model is required in the early design stage before the 3D CAD model is defined. And the analysis model generation process is a time-consuming job and takes much more time than the engineering work itself. In particular, ship structure has too many associated structural members such as stringers, stiffness and girders etc. These structural members should be satisfied as the constraints in analysis modeling. Therefore it is necessary to support generation of analysis model with satisfying these constraints as an automatic manner. For the effective support of the global ship analysis modeling, a method to generate analysis model using initial design information within ship design process, that hull form offset data and compartment data, is developed. In order to easily handle initial design information and FE model information, flexible data structure is proposed. An automatic quadrilateral mesh generation algorithm using initial design information to satisfy the constraints imposed on the ship structure is also proposed. The proposed data structure and mesh generation algorithm are applied for the various type of vessels for the usability test. Through this test, we have verified the stability and usefulness of this system including mesh generation algorithm.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.46 no.6
• /
• pp.1004-1022
• /
• 2022

This study seeks to investigate K-fashion's external image by examining the trends in global media reporting. It applies Dynamic Topic Modeling (DTM), which captures the evolution of topics in a sequentially organized corpus of documents, and consists of text preprocessing, the determination of the number of topics, and a timeseries analysis of the probability distribution of words within topics. The data set comprised 551 online media articles on 'Korean fashion' or 'K-fashion' published on Google News between 2010 and 2021. The analysis identifies seven topics: 'brand look and style,' 'lifestyle,' 'traditional style,' 'Seoul Fashion Week (SFW) event,' 'model size,' 'K-pop,' and 'fashion market,' as well as annual topic proportion trends. It also explores annual word changes within the topic and indicates increasing and decreasing word patterns. In most topics, the probability distribution of the word 'brand' is confirmed to be on the increase, while 'digital,' 'platform,' and 'virtual' have been newly created in the 'SFW event' topic. Moreover, this study confirms the transition of each K-fashion topic over the past 12 years, along with various factors related to Hallyu content, traditional culture, government support, and digital technology innovation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.3
• /
• pp.231-238
• /
• 2002

As a modeling method where the merits of fuzzy inference system and evolutionary computation are put together, evolutionary fuzzy modeling performs global approximate optimization. The paper proposes fuzzy clustering as fuzzy rule generation process which is one of the most important steps in evolutionary fuzzy modeling. With application of fuzzy clustering into the experiment or simulation results, fuzzy rules which properly describe non-linear and complex design problem can be obtained. The efficiency of evolutionary fuzzy modeling can be improved utilizing the membership degrees of data to clusters from the results of fuzzy clustering. To ensure the validity of the proposed method, the real design problem of an automotive inner trim is applied and the global approximation is achieved. Evolutionary fuzzy modeling is performed for several cases which differ in the number of clusters and the criterion of rule selection and their results are compared to prove that the proposed method can provide proper fuzzy rules for a given system and reduce computation time while maintaining the errors of modeling as a satisfactory level.

• Journal of Internet Computing and Services
• /
• v.6 no.2
• /
• pp.141-152
• /
• 2005

Many organizations have been recognizing the necessity of workflow automation technologies according to the rapid expansion of business process oriented applications, such as enterprise resource pianning, customer relationship management, electronic approval management, and so on, Thus, they have started adopting workflow management systems as an essential technological solution for their workflow processes, However, we need some technological extensions and improvements on them in order to accommodate a new type of workflow processes, which is called cross-organizational global workflow processes that require a certain level of collaborations between the organizations engaged in the global workflow processes, Fragment-driven workflow modeling methodology is a Bottom-Up methodology composing a global workflow by defining each organization's own activities, which is called a fragment through a realtime cooperative system. The approach is able to not only simplify the modeling work but also keep each organization's independence in modeling a global workflow, In this paper, we describe the fragment-driven workflow modeling methodology and realize the methodology through the implementation of a cooperative swimlane workflow modeling system.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 2000.11a
• /
• pp.39-48
• /
• 2000

• Proceedings of the CALSEC Conference
• /
• 2004.02a
• /
• pp.350-354
• /
• 2004

□ Many specification language has been developed for workflow modeling, but their emphasis on the process semantics make it difficult for use on the business process modeling. □ Also their lack of understanding of global view of Business process adds another problem to model workflow in a seamless and accurate and timely fashion(omitted)

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.69-94
• /
• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Atmosphere
• /
• v.33 no.1
• /
• pp.33-47
• /
• 2023

In this study, for the application of observation errors to the Korean Integrated Model (KIM) to utilize the Constellation Observing System for Meteorology, Ionosphere & Climate-2 (COSMIC-2) new satellites, the observation errors were diagnosed based on the Desroziers method using the cost function in the process of variational data assimilation. We calculated observation errors for all observational species being utilized for KIM and compared with their relative values. The observation error of the calculated the Global Navigation Satellite System Radio Occultation (GNSS RO) was about six times smaller than that of other satellites. In order to balance with other satellites, we conducted two experiments in which the GNSS RO data expanded by about twice the observation error. The performance of the analysis field was significantly improved in the tropics, where the COSMIC-2 data are more available, and in the Southern Hemisphere, where the influence of GNSS RO data is significantly greater. In particular, the prediction performance of the Southern Hemisphere was improved by doubling the observation error in global region, rather than doubling the COSMIC-2 data only in areas with high density, which seems to have been balanced with other observations.

• International Journal of Advanced Culture Technology
• /
• v.11 no.4
• /
• pp.193-201
• /
• 2023

With the end of global COVID-19 epidemic, hospital staff are likely to be "physically and mentally exhausted" after three years of grueling work in the fight against the epidemic. At this point, it is especially important to enable them to continue to maintain their previous proactive work behavior. This study focuses on 400 employees of various types in three-A grade hospitals in Zhanjiang, Guangdong Province, through the proactive motivation model. Statistical software SPSS 25.0 and AOMS 22.0 were used to analyze the survey data to test whether role modeling in hospital management can have an impact on employees' proactive behaviors, in addition to verifying the mediating role of transactional psychological contract. The results of this study show that: First, role modeling of hospital leaders has a positive effect on employees' proactive behavior and a negative effect on their transactional psychological contract; Second, transactional psychological contract has a negative effect on employees' proactive behavior; Third, the transactional psychological contract mediates the effect between role modeling of leaders and employees' proactive behavior. The results of this research add to the F-path of proactive motivation model, and provide enlightenments and implications for hospital management.

• Journal of Korean Society for Atmospheric Environment
• /
• v.15 no.E
• /
• pp.55-64
• /
• 1999

The CIT(California Institute of Technology) three-dimensional Eulerian photochemical model was applied to the Greater Seoul Area, Korea for July 24, 1994, a day of the 9-day ozone episode to understand the characteristics of photochemical air pollution problems in the area. The modeling domain was 60km$\times$60km with the girl size of 2km$\times$2km. As the base case emissions, air pollutant emission data of the National Institute of Environmental Research, Korea for the year of 1991 were used with modifications based on EKMA(Empirical Kinetic Modeling Approach) resutls. Comparisons between predicted and observed concentrations showed that the model predicted the peak concentration over the domain reasonably. It was found that the location of the peak ozone concentration was mainly decided by metorological conditions. But the model could not resolve the spatial variations of concentration station by station, which was mainly caused by localized variations in emission and meteorology.