• Journal of the East Asian Society of Dietary Life
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• v.15 no.4
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• pp.475-482
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• 2005

As Korea has approached the aging society, older Koreans have become an important force in restaurant sales today. To succeed with this silver market, it is important for restaurant managers to know who they are and which factor influence the older Koreans' eating-out decision making process. The purpose of this study was to investigate the effect of the elderly consumers financial independency on restaurant selection process. Data were collected from 178 older consumers above 55 years old and analyzed using the descriptive statistic analysis, MANOVA, and one-way ANOVA. The results showed that the elderly consumers financial independency significantly influenced the decision making process in determining where they eat out Significant differences were found between high income group and low income group in the Problem Recognition Step(Wilks' Lambda=0.776, F=3.796), Information Search Step(Wilks' Lambda=0.779, F=2.959), Alternative Evaluation Step (I :Wilks' Lambda=0.835, F=1.748/ II :Wilks' Lambda=0.764, F=3.212), and Purchase Decision Step(Wilks' Lambda=0.849, F=2.412), except the Post-Purchase Behavior(Wilks' Lambda=0.933, F=1.179). The more financially independent older consumers were, the more directly they were involved in the eating out decision making process. Older consumers with higher income and more personal property were likely to 'propose to eat out by themselves'(F=10.986), to obtain restaurant information from the 'printed materials'(F=9.707), to consider 'convenient location' as most important factor when they eat out(F=5.594), and to go to 'family restaurant'(F=7.067), 'Japanese restaurant'(F=7.391) and 'fine dining restaurants'(F-=6.382). In conclusion, we found that the elderly consumers financial independency did influence the eating-out decision making process. Considering that older Korean will become a financially independent consumer and will be eating away from home more often, food service operations should actively position themselves for this market and develop the market-driven menus and services to meet their needs and expectations.

• Water Engineering Research
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• v.3 no.2
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• Journal of Applied Reliability
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• v.8 no.3
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• pp.113-124
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• 2008

In reliability engineering, failure reporting, analysis, and corrective action system (FRACAS) is an useful tool for effective failure reporting and related operations. FRACAS is generally mainly focused on implementation of its closed-loop process, but also includes various related information which has to be effectively managed such as failure types, failure modes, failure mechanisms, and corrective actions. In this study, we adopt and utilize the concept of process knowledge, and create it through abstraction of FRACAS information. At each step of closed-loop process, the necessary type of knowledge, priority and usability are clearly defined. This study also suggests corresponding management tools such as business process management system, knowledge management system, and their key elements and functions to deal with process knowledge. A prototype system using simple closed-loop process with its process knowledge is presented to demonstrate the feasibility of the proposed work.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.181-187
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• 2011

Since 2003, state transportation study core technology development is being promoted as part of the bimodal trams operating in accordance with the development of refractive vehicle as research infrastructure for building high-tech road transport system has been the research and development. Bimodal trams of refraction as the vehicle for him to introduce domestic first ever operation management system also developed in Korea according to case-based technology system, but most of the country, and, in this study, mainly those based on technology integration building management system and the bimodal trams of refraction of a vehicle operated was to highlight the features and benefits. Bimodal tram station itself is the way the exclusive properties and to operate the route with large transport capacity has the characteristics of the railway, but the only routes such as railroad lines is not of closed roads under certain circumstances, the flexibility to use has to be integrated operations management system of bimodal trams characteristics of the railroads and public transportation by combining the characteristics of a flexible, convenient and secure services to users with the aim of providing research and will denote the system developed. In this study, bimodal integration system required for the operation of the tram station around the wired and wireless network management center, applying the organic integration into one system so that you have to be centrally managed. In addition, the existing traffic management system operates as a unidirectional rather than monitoring all system-wide management via the interactive network through real-time requests and responses were configured to allow management and control. These findings of the existing traffic operation management system that you can jump step can be based on future unmanned vehicles and related systems through control of the operation management system will be offered as a basis.

• Journal of the Korea Society for Simulation
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• v.12 no.1
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• pp.1-10
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• 2003

Recently, various network protocols have been developed to keep step with the rapid growth of Internet users. For the education of students in the computer networks classes, there have been many attempts to allow students experiment routers setting, operations and management of networks by themselves, in addition to the classroom lectures based on textbooks. One of the attempts is to install expensive real router experiment sets in laboratories for students, and one another is using router and network simulators for virtual experiments. This paper presents the design and implementation of NetSim, a scalable, component-based simulator environment for the network experimentation. NetSim expands the existing component-based JavaSim simulation tool for the education purpose, and it allows the design and experiment of various networks and protocols. For the evaluation of NetSim, it is shown that the network design and RIP-based router setting process is possible in NetSim.

• Asian Journal of Business Environment
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• v.11 no.3
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• pp.33-43
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• 2021

Purpose: The purpose of the paper is to assess the greening of investment flows as well as the orientation of financial sources of effective environmental protection. Both India and Vietnam are considered to be two Asian countries that are radically affected by global climate change. Governments of the two countries have implimented numerous measures against environmental pollution through the banking and finance sector. Developing green banking operations in India and Vietnam is a new direction in the socio-economic development strategy coupled with effective environmental protection. Research design, data and methodology: The data was mainly based on Asia Development Bank Institution (ADBI) and Bank of India (BOI) from 2015 - 2018. This paper focused on comparing and evaluating the development of green banks between the two countries. Result: The banks' contribution to green growth and green economy is shown in two basic aspects: (i) the connection between organizations, and individuals, and management process for investment projects, including environmental risks; and (ii) the operation of banks has a direct impact on the environment, through the application of technology to pay documents and apply e-banking. Conclusion: Paper reflects, compares and evaluates green banking operations in India and Vietnam to provide new directions aiming to develop financial and economic system along with effective and efficient step toward climate change control. India's green banking operations, after a specific assessment, will be lessons for the Government of Vietnam during the process of socio-economic development and environmental protection.

• Korean Management Science Review
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• v.16 no.1
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• pp.157-171
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• 1999

In a increasingly competitive marketplace, the manufacturing companies have no choice but looking for ways to improve productivity to sustain their competitiveness and survive in the industry. Recently cellular manufacturing has been under discussion as an option to be easily implemented without burdensome capital investment. The objective of cellular manufacturing is to realize many aspects of efficiencies associated with mass production in the less repetitive job-shop production systems. The very first step for cellular manufacturing is to group the sets of parts having similar processing requirements into part families, and the equipment needed to process a particular part family into machine cells. The underlying problem to determine the part and machine assignments to each manufacturing cell is called the cell formation. The purpose of this study is to develop a clustering algorithm based on the neural network approach which overcomes the drawbacks of ART1 algorithm for cell formation problems. In this paper, a generalized learning vector quantization(GLVQ) algorithm was devised in order to transform a 0/1 part-machine assignment matrix into the matrix with diagonal blocks in such a way to increase clustering performance. Furthermore, an assignment problem model and a rearrangement procedure has been embedded to increase efficiency. The performance of the proposed algorithm has been evaluated using data sets adopted by prior studies on cell formation. The proposed algorithm dominates almost all the cell formation reported so far, based on the grouping index($\alpha$ = 0.2). Among 27 cell formation problems investigated, the result by the proposed algorithm was superior in 11, equal 15, and inferior only in 1.

• Korean Business Review
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• v.4
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• pp.281-295
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• 1991

Nothing is more incorrect than forecasting. Nevertheless, forecasting is one of the most important business activities for the effective management. There has been rapid changes of the growth rate in every respect of the Korean hospitaity industry, especially the hotel industry, before and after the 88 Olympic Games. Therefore, the hoteliers shall be in need of more-than-ever accourate demand forecasting for the more systematic management and control. Under the above circumstances, this study suggested the best forecasting technique and method for the better sales and operations of the hotel rooms. The number of rooms sold is selected as a dependent variable of this study which is regarded as the best representative factor of measuring the growth rate of the rooms division performance of the hotels. The first step was to select the most verifiable independent variable diferently from the other countries or other areas of Korea. As a result, the number of foreign visitors was chosen. Empirical research, i.e. correlation and multiple regression analysis, shows that this independent variable has a strong relationship with the dependent variable told above. The second procedure was to estimate the number of rooms will be sold in 1991 on the basis of the formula calculated through the multiple regression analysis. Time series technique was conducted using the data of the number of foreign visitors by purpose of travel from 1987 to 1990. For the more correct forecasting, however, it would be desirable to adopt the data from 1989 considering the product or the industry life cycle. In addition, deeper analysis for the monthly or seasonal forecasting method is needed as a future research.

• Korean Management Science Review
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• v.32 no.2
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• pp.69-78
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• 2015

This paper considers a Bayesian Poisson model for multivariate count data using multiplicative rates. More specifically we compose the parameter for overall arrival rates by the product of two parameters, a common effect and an individual effect. The common effect is composed of autoregressive evolution of the parameter, which allows for analysis on seasonal effects on all multivariate time series. In addition, analysis on individual effects allows the researcher to differentiate the time series by whatevercharacterization of their choice. This type of model allows the researcher to specifically analyze two different forms of effects separately and produce a more robust result. We illustrate a simple MCMC generation combined with a Gibbs sampler step in estimating the posterior joint distribution of all parameters in the model. On the whole, the model presented in this study is an intuitive model which may handle complicated problems, and we highlight the properties and possible applications of the model with an example, analyzing real time series data involving customer arrivals to a large retail store.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.53-62
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• 2021

Deep learning models such as convolutional neural networks and recurrent neual networks process a huge amounts of data, so they require a lot of storage and consume a lot of time and power due to memory access. Recently, research is being conducted to reduce memory usage and access by compressing data using the feature that many of deep learning data are highly sparse and localized. In this paper, we propose a compression-decompression method of storing only the non-zero data and the location information of the non-zero data excluding zero data. In order to make the location information of non-zero data, the matrix data is divided into sections uniformly. And whether there is non-zero data in the corresponding section is indicated. In this case, section division is not executed only once, but repeatedly executed, and location information is stored in each step. Therefore, it can be properly compressed according to the ratio and distribution of zero data. In addition, we propose a hardware structure that enables compression and decompression without complex operations. It was designed and verified with Verilog, and it was confirmed that it can be used in hardware deep learning accelerators.