• 기술혁신학회지
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• 제7권1호
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• pp.64-88
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• 2004

This paper aims at investigating which factors play important roles in selecting government's new core IT industries and how competitive they are. We surveyed 6 competitiveness factors and 17 IT industries for the expert group. The logit and probit models were estimated and SWOT analysis was performed. The empirical results show that government put emphasis on marketability, externality and technology, not publicity, when selecting IT new core industry. The skilled human resources turn out to be a threat factor in the government selected IT new core industries such as home-network, third generation semi-conductor. Therefore, training or education system for skilled labors is required to develop and nurture such industries. The contribution to small medium venture industry and publicity are lower in the several industries such as intelligent service robots, post PC, embodied S/W, next generation battery, which are selected by government, not by standardized data based criterion. in such industries, marketabilities, technology, skilled human resources are threats factors to such industries. Therefore every effort for enhancing the marketability and R&D investment and education system for skilled labor are necessary to develop the industries.

• 한국조경학회지
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• 제21권4호
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• pp.131-145
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• 1994

The leisure demands and the interests for the quality of urban residents have been increased, but the leisure space is absolutely deficient. In the leisure site planning, the concrete understanding about people's leisure site choice is the most important thing, not only for the aspect of the leisure demands reception and the improvement of the life quality but also the aspect of the efficiency of land use. The purposes of this study are firstly, to find out prefered leisure sites, secondly, to establish the choice models of the each prefered leisure site to be substituted for existing indiscriminating leisure space planning. And for the choice model establishment, we used Logit Model, which has been used in the Traffic, the Toursim, the Economics fields. We extract people's perfered leisure sites in Seoul through 1st and 2nd survey, those are a park, a pocket park, a play hall, a recreation center(sport center), a hobby facility, a library. The established choice model for each prefered site can predict people's choice about 70 percents correctly. It indicates that the Logit model is useful for the explanation about the choice of residents in the urban area. Specially, the main affect factors to the choice of each prefered leisure site are different. It means that different consideration factors or different standards are needed for each leisure site planning.

• Asia Pacific Journal of Business Review
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• 제5권2호
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• pp.19-44
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• 2021

Sub Saharan Africa (SSA) is receiving increased investments in the energy sector under the belt and road initiative (BRI) project since its inception in 2013. SSA has a worse energy efficiency ratio coupled with deficient electricity access, through analysis showed varied impacts on the SSA countries due to the BRI initiative. This study dilves into the influencing factors for Energy Efficiency (EE) in 38 SSA countries, applying the probit and logit approach for 2000-2018. The Multiple-regression model shows significant results of some variables such as foreign direct investment, gross domestic product, and port infrastructure quality being significant on EE under BRI initiative countries. However, the logit and probit models produce similar results and the marginal effect for the entire variable, except energy imports that do not likely impact EE. Furthermore, the interaction of quality of port infrastructure and foreign direct investment variables produces significant results, highlighting the increased investments SSA receives under the BRI initiative in the energy and transport sectors. The model Percent correctly predicted (PCP) value was about 84%, indicating it correctly classified the variables and about 16% not classified. The study recommends EE performance standards should be incorporated on energy projects in SSA to ensure that these projects are energy efficient and decouple SSA's energy demand from economic growth. The research proffers suggestions for policy regarding the BRI initiative in SSA and the implications on sustainable energy and building a community with a shared future.

• 유통과학연구
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• 제20권10호
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• pp.61-66
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• 2022

Purpose: Predicting the financial distress distribution of an enterprise is important to warn enterprises about their future. Predicting the possibility of financial distress helps companies have action plans to avoid the possibility of bankruptcy. In this study, the author conducted a forecast of the financial distress distribution of enterprises. Research design, data and methodology: The forecasting method is based on Logit and Discriminant analysis models. The data was collected from companies listed on Vietnam Stock Exchange from 2012 to 2020. In which there are both companies suffer from financial distress and non-financial distress. Results: The forecast analysis results show that the Logistic model has better predictability than the Discriminant analysis model. At the same time, the results also indicate three main factors affecting the financial distress of enterprises at all three research stages: (1) Liquidity, (2) Interest payment, and (3) firm size. In addition, at each stage, the impact of factors on financial distress differs. Conclusions: From the results of this study, the author also made several recommendations to help companies better control company operations to avoid falling into financial distress. Adjustments to current assets, debt, and company expansion considerations are the most important factors for companies.

• 아태비즈니스연구
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• 제13권2호
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• pp.79-94
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• 2022

Purpose - The purpose of this study is to examine firm-level attributes related to Korean manufacturing small and medium-sized enterprises' (SMEs') decisions to implement smart factories. Design/methodology/approach - This study uses the provided by the Ministry of SMEs and Startups of Korea and the Korea Federation of SMEs. Manufacturing SMEs' decisions to implement smart factories in 2018-2019 were analyzed using multinomial logit and ordered logit models. Findings - The findings of this study suggest that firms' decisions to implement smart factories were positively related to firm size, R&D intensity, international market scope, and transactional relationships with customers. However, smart factory implementation decisions were not related to firm age and CEO gender. Research implications or Originality - This study illuminates firm-level attributes that may drive organizational innovation in the era of Industry 4.0 and thus contributes to the innovation adoption literature. This study also contributes to growing research on smart factories by analyzing the actual, progressive decisions to implement smart factories, as opposed to perceived intentions to implement them.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2024년도 춘계학술발표대회
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• pp.711-714
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• 2024

In the dynamic landscape of modern machine learning, Federated Learning (FL) has emerged as a compelling paradigm designed to enhance privacy by enabling participants to collaboratively train models without sharing their private data. Specifically, Distillation-based Federated Learning, like Federated Learning with Model Distillation (FedMD), Federated Gradient Encryption and Model Sharing (FedGEMS), and Differentially Secure Federated Learning (DS-FL), has arisen as a novel approach aimed at addressing Non-IID data challenges by leveraging Federated Learning. These methods refine the standard FL framework by distilling insights from public dataset predictions, securing data transmissions through gradient encryption, and applying differential privacy to mask individual contributions. Despite these innovations, our survey identifies persistent vulnerabilities, particularly concerning the susceptibility to logit inversion attacks where malicious actors could reconstruct private data from shared public predictions. This exploration reveals that even advanced Distillation-based Federated Learning systems harbor significant privacy risks, challenging the prevailing assumptions about their security and underscoring the need for continued advancements in secure Federated Learning methodologies.

• 대한교통학회지
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• 제26권1호
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• pp.145-153
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• 2008

기존의 최단경로 탐색모형들은 주로 경로의 단일 속성만을 고려한다. 그러나 실제로 통행자가 단일 속성만을 고려하여 경로를 선택하는 경우는 드물며, 대부분의 경로는 통행시간이나 경로길이 또는 통행자의 개인적인 선호 등과 같은 다양한 속성들이 종합적으로 고려되어 선택되어진다. 따라서 최적경로를 탐색하기 위해서는 이와 같은 다양한 속성들을 종합적으로 고려하여야 한다. 본 논문에서는 다양한 경로속성들을 고려하기 위하여 이산선택모형을 사용하여 네트워크의 노드별 효용을 산출하고, 이를 이용하여 최대의 효용을 가지는 경로를 탐색한다. 경로선택모형을 구축하기 위하여 경로선택에 영향을 미치는 요소들을 통행시간, 지체시간, 경로길이, 신호교차로수, 회전수, 전용도로의 포함비율 6가지로 선정하고, 모형의 모수를 추정하기 위한 현시선호자료를 구하기 위하여 서울시와 인접 신도시 간의 기종점 5개에 대한 경로를 선정하여 설문조사를 실시하였다. 경로선택모형의 함수형태로는 다항로짓모형을 사용하였으며, 모수추정 결과 통행시간과 신호 교차로수, 전용도로의 포함비율을 제외한 경로길이, 지체시간, 회전수를 가지고 모수를 추정한 결과가 통계적 유의성이 가장 높은 모형으로 도출되었다. 경로탐색 알고리즘으로는 도심부에서 U-turn과 회전제한의 반영이 가능한 기존의 수정형 덩굴망 알고리즘을 사용하였으며, 이를 구현하여 실제 네트워크에 적용하였다.

• 대한교통학회지
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• 제11권1호
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• pp.67-86
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• 1993

A formulation of dynamic traffic assignment between multiple origins and single destination was first introduced in 1987 by Merchant and Nemhauser, and then expanded for multiple destination in the late 1980's (Carey, 1987). Based on behavioral choice theory which provides proper demand elasticities with respect to changes in policy variables, traffic phenomena can be analysed more realistically, especially in peak periods. However, algorithms for these models are not well developed so far(working with only small toy network) and solutions of these models are not unique. In this paper, a new model is developed which keeps the simplicity of static models, but provides the sensitivity of dynamic models with changes of O-D flows over time. It can be viewed as a joint departure time and route choice model, in the given time periods(6-7, 7-8, 8-9 and 9-10 am). Standard multinomial logit model has been used for simulating the choice behavior of destination, mode, route and departure time within a framework of the incremental network assignment model. The model developed is workable in a PC 386 with 175 traffic zones and 3581 links of Seoul and tested for evaluating the exclusive use of Namsan tunnel for HOV and the left-turn prohibition. Model's performance results and their statistical significance are also presented.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• 동아시아경상학회지
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• 제4권2호
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• pp.1-11
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• 2016

This study investigated commercial power theory of traditional market through the analysis of literature review. Consumers' store selection models are made up a theory based on normative hypothesis, theory of mutual reaction, utility function estimation model, and cognitive-behavioral model. Detailed models are as follows. Normative hypothesis based theory is divided into Reilly's retail gratification theory and Converse's revised retail g ratification theory. Interaction theory is composed of Huff's probability gratification theory, MCI model and Multi-nominal Logit Model (MNL model). There are four models in retail organization position theory such as central place theories, single store position theory, multi store position - assign model, and retail growth potential model. In case of single store position theory, theoretical and empirical techniques have developed for a decision to optimum single store position. Those are like these, a check list, the most simple and systematic method, analogy, and microanalysis technique. Aforementioned models are theoretical and mathematical commercial power measurement and/or model. The study has rather limitations because the variation factors included in formula are only a part of actual commercial power. Therefore, further study shall be made continuously to commercial power areas and variables.