• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.11-18
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• 2009

Unless certain limitation is increasing the number of the job type will be inevitable in the future because of development of industry, adaptation on speedy life style, and leisure oriented nuclear family style. In this paper, a prospective model of supply and demand of work force has been developed basing on various categories of industries and patterns about employees to look over efficient supply and demand of work force suiting employment of work force policies. In this paper, after Analyzing job preference, we have noticed a more stable job system and the results showed significant improvements over the existing job system.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• Korean Institute of Interior Design Journal
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• v.15 no.6 s.59
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• pp.255-263
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• 2006

This research analyze the demand of the residents against the public housing remodeling of national housing scale $(85m^2)$, focusing on residents and their tendencies within their dwelling spaces. The analysis determines the most appropriate patterns and spatial connections within the floor plan. The result includes that there are two types of the classification into an urban oriented propensity (45%), 'The center of city, the apartment and the convenience' etc, and a rural oriented propensity (55%), 'The pastoral, the house and the circumstance' etc, based on their lifestyle values. Also there are three interior propensity classifications, those tending to warm and sensitive variable space (42%), western and gorgeous dynamic space (34%) and oriental and popular static space (24%). The research illustrated the residents' desired space planning options, based on the analysis of the residents' preference patterns which is various.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.193-197
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• 2021

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

• Journal of the Economic Geographical Society of Korea
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• v.14 no.1
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• pp.33-51
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• 2011

This study investigates the spatial characteristics of the provision of and demand for the private tutoring service industries and the consumer groups. For the purpose, we analyze the spatial characteristics of various types of tutoring institutes in the Seoul Metropolitan area. In particular, we exam the spatial distribution patterns of attendants of tutoring institutes by institution type as well as the resident population by attendant age group. By applying spatial autocorrelation analysis, we examine the spatial clustering patterns of tutoring institutes and attendants by type. The results show distinct differences in the spatial distribution patterns by tutoring institute type as well as by attendant age group. We found significant socio-economic variables which influence on the spatial distribution of tutoring institutes. Finally, we propose private tutoring service provision models constructed with these variables through multiple regression analysis.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.257-269
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• 2024

Purpose: The purpose of this study is to explain the pivotal role of the travel forecasting process in urban transportation planning. This study emphasizes the use of travel forecasting models to anticipate future traffic. Method: This study examines the methodology used in urban travel demand modeling within transportation planning, specifically focusing on the Urban Transportation Modeling System (UTMS). UTMS is designed to predict various aspects of urban transportation, including quantities, temporal patterns, origin-destination pairs, modal preferences, and optimal routes in metropolitan areas. By analyzing UTMS and its operational framework, this research aims to enhance an understanding of contemporary urban travel demand modeling practices and their implications for transportation planning and urban mobility management. Result: The result of this study provides a nuanced understanding of travel dynamics, emphasizing the influence of variables such as average income, household size, and vehicle ownership on travel patterns. Furthermore, the attraction model highlights specific areas of significance, elucidating the role of retail locations, non-retail areas, and other locales in shaping the observed dynamics of transportation. Conclusion: The study methodically addressed urban travel dynamics in a four-ward area, employing a comprehensive modeling approach involving trip generation, attraction, distribution, modal split, and assignment. The findings, such as the prevalence of motorbikes as the primary mode of transportation and the impact of adjusted traffic patterns on reduced travel times, offer valuable insights for urban planners and policymakers in optimizing transportation networks. These insights can inform strategic decisions to enhance efficiency and sustainability in urban mobility planning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.80-92
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• 2023

As cities are becoming densely populated, previously unexpected events such as crimes, accidents, and infectious diseases are bound to affect user demands. With a time-series prediction of demand using information with uncertainty, it is impossible to derive reliable results. In particular, the COVID-19 outbreak in early 2020 caused changes in abnormal travel patterns and made it difficult to predict demand for time series. A methodology that accurately predicts demand by detecting and reflecting these changes is, therefore, required. The current study suggests a time series modeling pipeline that automatically detects and predicts abnormal events caused by COVID-19. We expect its wide application in various situations where there is a change in demand due to irregular and abnormal events.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.536-543
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• 2005

3D online game, with its striking realistic value, is leading the entire Korean game market which has various game genres. Technology sharing is very hard within the Korean game industry. That is because 1)there are few professionals, 2)most of the companies are small-scaled, and 3)there are security reasons. Therefore, it should be significant if we have software design techniques which make it possible to reuse the existing code when developing a network game so that we could save a lot of efforts. In this paper, the author analyzes the demand through the case in the client's design of the network game based on DirectX and proposes the effective software design methods for reusable code based on the creative patterns application in the GoF in the class design.

• Child Health Nursing Research
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• v.3 no.2
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• pp.190-202
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• 1997

Mothers with cerebral palsy children have much stress that is related to the care of children with cerebral palsy and to other household duties, and this state of the mother has an effect on the cerebral palsy child and on other household member. Mothers in such stressful situations use various coping patterns. The purpose of this study was as follows : to develop instruments that can be used for measuring the stress and coping patterns of mothers with cerebral palsy children, and to test a hypothetical model on the relationship between the mother's stress, her coping patterns and the variable affecting the stress and coping patterns. The results of this study can be summarized as follows : 1. The stress scale was composed of 44 items and Cronbach's α was .94, and the coping pattern scale was composed of 19 items and Cronbach's α was. 80. The mean score of stress scale was 136.12 out of a total of 220, and the mean score of the coping scale was 72.87 in a total of 95. 2. In test of the hypothetical model, it was found that extra-care demand, the support of the husband, the degree of handicap, health status and self-esteem had statistically significant influence on the mother's stress(r=.285,-.262,-.133, -.126). And the support of the husband, formal support, informal support, and economic status were found to have statitically significant influence on the mother's coping patterns (r=.412, .178, 178, .138).