• Journal of the Korean Home Economics Association
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• v.41 no.6
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• pp.91-104
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• 2003

Using the 1998 Survey of Consumer Finances(SCF), this study examined the factors associated with the probability of saving by female-headed households in the U.S, and investigated how these factors differ by life cycle stages. Age of children, age of female householders and their retirement status were used to categorize three life cycle stages: first stage - have at least one child under age 18; second stage - under the age of 62 and have no children under age 18; third stage - over the age of 62 and retired. Logistic regression analysis results indicated that those with higher education and income and who were White were more likely to save. An interactive model showed that life cycle stages were significantly related to saving decisions. Female-headed households in the first stage or the third stage were less likely than those in the second stage (reference group) to save. For female-headed households in the first stage and the third stage, the amount of income had significantly positive effects on the decision to save. Also, in the group of households in the first stage, the receipt of welfare assistance increased the probability of saving.

• Journal of Family Resource Management and Policy Review
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• v.5 no.1
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• pp.15-31
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• 2001

The purpose of this study is to identify participation-types of the family in the purchasing decision-making on items of commodities (TV, refrigerator, furniture, passenger car, housing, vacation, saving, husbands’clothing and children’s clothing). The other thing is to find who is major factors in each of the decision-making stages(the recognition of problem, the searching of information, the evaluation of alternatives and purchasing) among the subjects of decision-making (husband, wife, couple combination and family combination). To complete the purpose, the data were collected through questionnaire which 549 wives living in Seoul answered. From the data, frequency, percentage and correspondence analysis were executed by SPSS. The results of this study were briefly summarized as follows; Wives played an important role on all of decision-making stages for refrigerator, furniture, children’s clothing and husbands’clothing. Husbands played an important role on vacation and passenger car. And in case of saving, TV and housing, couple combination played an important role. It means that wives have a major influence on the purchasing process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2925-2948
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• 2019

In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5B
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• pp.317-326
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• 2012

Recently, many researchers have studied to solve the energy consumption of network equipments since the interest of Green IT has been increased. In this paper, we apply Low Power Idle (LPI) to OBS network to reduce energy consumption of network devices. Many previous researches have focused on maximizing the sleep time of network equipments to increase the energy saving efficiency of LPI. But transition overhead caused by LPI might not only depreciate the performance of energy saving but also increase packet delay. In this paper, Transition Decision (TD) algorithm is proposed to improve energy saving efficiency by reducing the number of unnecessary transition and guarantee the required QoS such as packet delay. To evaluate the performance of proposed algorithm, we model OBS edge router with LPI by OPNET and analyze the performance of the proposed algorithm in views of energy saving, transition count and average packet delay.

• Journal of Information Technology Applications and Management
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• v.15 no.3
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• pp.43-59
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• 2008

This paper studies small and medium sized financial agency's management states that take advantage of the Korea Federation of Saving Bank's data. It also presents the management state and the decision-making model that monitors progressive insolvency by standardizing transfer path between relevant groups. With this in mind, we extracted explanatory variables for predictions of insolvency by using existing studies of document related insolvency. First of all, we designed a state model based on demarcated groups to take advantage of the self organizing map that groups in line with a neural network. Secondly, we developed a transition model by standardizing the transfer path between individual banks in a state model. Finally, we presented a decision-making model that integrated the state model and the transition model. This paper will provide groundwork for methods of insolvency prevention to businesses in order for them to have a smooth management system in the financial agencies.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.49-70
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• 2006

In recent, Open markets are booming up rapidly. Open markets are one of the online marketplaces which mainly concentrated on spot transactions of commodities, and are differentiated from integrated internet shopping stores with market participants, trading rules and so on. This study investigated on factors affecting satisfaction with and preference on open markets by comparing open markets with integrated internet shopping stores, and aimed to figure out the reasons why open markets are growing up so rapidly and to forecast the future of open markets. To investigate the factors affecting buyers' satisfaction with and preference on internet shopping channel. I extracted several factors through literature reviews. The factors include the pros (cost saving and time saving), the cons (security concerns and privacy concerns), and decision making support suggested by Simon's research as well. Then, I constructed research model and related research hypotheses. To verify research hypotheses, I conducted field survey targeting on online buyers and analyzed research data using structural equation model. According to data analysis result, open markets have competitive advantages over integrated internet shopping stores with respect to cost saving, time saving, and decision making support. However, online buyers are still concerning privacy issues within open markets. In summary, buyers are considering that open markets are cheaper, faster, and more efficient internet shopping channel, compared with integrated internet shopping stores. However, open markets are required to dedicate to lessen buyers' privacy concerns to rebirth as more satisfying and preferable internet shopping channel and to prosper in the future.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• Journal of the Korean Solar Energy Society
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• v.34 no.2
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• pp.24-31
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• 2014

So many energy saving strategies to realize the low energy house are proposed in these days. Even if the energy saving performances are similar between the strategies, the costs are in abroad ranges. Therefore, the application of the energy saving strategies should be based upon two considerations; one is energy saving performance and the other is the cost. In this study, the decision making method for application of the passive design technologies in the design stage of the low energy apartment house are suggested. By utilizing the result of this study, practitioners can make an effective design alternatives which solve the conflicted values; energy saving and cost.

• Journal of Korean Neurosurgical Society
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• v.55 no.4
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• pp.195-199
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• 2014

Objective : Age is a strong predictor of mortality in traumatic brain injuries. A surgical decision making is difficult especially for the elderly patients with severe head injuries. We studied so-called 'withholding a life-saving surgery' over a two year period at a university hospital. Methods : We collected data from 227 elderly patients. In 35 patients with Glasgow Coma Score 3--8, 28 patients had lesions that required operation. A life-saving surgery was withheld in 15 patients either by doctors and/or the families (Group A). Surgery was performed in 13 patients (Group B). We retrospectively examined the medical records and radiological findings of these 28 patients. We calculated the predicted probability of 6 month mortality (IPM) and 6 month unfavorable outcome (IPU) to compare the result of decision by the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) calculator. Results : Types of the mass lesion did not affect on the surgical decision making. None of the motor score 1 underwent surgery, while all patients with reactive pupils underwent surgery. Causes of injury or episodes of hypoxia/hypotension might have affected on the decision making, however, their role was not distinct. All patients in the group A died. In the group B, the outcome was unfavorable in 11 of 13 patients. Patients with high IPM or IPU were more common in group A than group B. Wrong decisions brought futile cares. Conclusion : Ethical training and developing decision-making skills are necessary including shared decision making.

• Journal of Construction Engineering and Project Management
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• v.3 no.3
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• pp.17-22
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• 2013

In many urban cities, super-tall buildings have been being constructed around New York and Chicago as the center since 1930 to improve the efficiency of land use and respond to new residential type. In terms of energy consumption, super-tall buildings are classified as a top energy consumption building. Also, as time passed, the degradation of energy performance occurs in super-tall buildings like general things so that these cannot show the initial performance planned in the design phase. Accordingly, building owners need to make a plan to apply energy saving measures to existing building during the operation phase. In order to select energy saving measures, calculus-based methods and enumerative schemes have been typically used. However, these methods are time-consuming and previous studies which used these methods have problems with not considering the initial construction cost. Consequently, this study proposes a model for selecting an optimal combination of energy saving measures which derives maximum energy saving within allowable cost using genetic algorithms. As a contribution of this research, it would be expected that a model is utilized as one of the decision-making tools during the planning stage for energy saving.