• 한국항만경제학회지
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• 제24권1호
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• pp.85-102
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• 2008

본 연구에서는 물류정보시스템을 도입활용하고 있는 무역업체들을 대상으로 내부업무효율성과 조직혁신정도를 조절변수로 하여, 내부업무효율성과 조직혁신정도가 높은 집단과 낮은 집단으로 구분하여, 이들 각 집단의 물류정보시스템 특성변수가 성과(이용자가치와 인식된 유용성)에 미치는 영향을 분석하였다. 연구결과, 구조화된 업무 환경에서 물류정보시스템을 통한 지속적인 업무수행과 더불어 조직혁신을 위한 기업차원에서의 노력이 병행되어야 하며, 물류정보시스템 도입 무역업체들은 정기적으로 기업의 업무활동전반에 대한 정확한 평가와, 전략수립을 통해 물류정보 시스템의 성과 제고를 위한 노력을 기울여야 할 것이며, 본 연구에서 나타난 바와 같이 물류정보시스템 도입 무역업체들의 낮은 조직혁신정도를 향상시킬 수 있는 조직자체의 각성과 노력이 뒤따라야 할 것이다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집
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• pp.1390-1394
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• 2009

In the paper We have analyzed the case study of the domestic and foreign railway logistic information systems based on the railway logistic to build the information architect basis system of real-time simultaneous. In case of domestic examples, We have analyzed the logistic information system used in Korea Railroad and the information systems of Kyungin ICD(Inland Container Depot) and Busanjin CY(Container Yard). In case of foreign example, we have analyzed the logistic information system of Japanese FRENS(Freight information network system) and the examples of freight tracking using RFID(Radio Frequency Identification). Through the above analysis, We have induced the main problems and improvement methods. We are able to build the railway-oriented information network system for the massive and efficient railway transportation.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.367-378
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• 2004

Recently logistic regression is popular in a variety of fields so that a number of statistical packages are developed for analyzing the logistic regression. This paper briefly considers the several types of logistic regression models used depending on different types of data. In addition, when four statistical packages (MINTAB, SAS, SPSS and STATA) are used to apply logistic regression models to the real fields respectively, their scope and characteristics are investigated.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2001년도 추계학술대회 논문집
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• pp.7-10
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• 2001

The development of information communication technology leads the growth of logistic industry including delivery service as well as electronic commerce. The researchers predict that it will be still improving for the next several years. The logistic information system of railroad courier has been growing for a long time with small-package delivery transportation which is similar to the land-road delivery system. Despite of the long-time growth, it is recently in pain of the great loss since the 1990's, due to the failure to satisfy the customer's need for door-to-door delivery service. But the logistic information system of railroad still has the great potential. There are so many benefits such as timeliness, Punctuality, speed, multi-node storage base, transportation efficiency, energy frugality, environmental sociability, and so on. If the railroad logistic system plays a role of a portion of the nation-wide logistic with other logistic system, the synergy through the balancing logistic will also get much of international competitive advantages. So the objective of this research is to design the model and prototype of the web-based logistic system from which railroad service provider(Korean National Railroad), delivery service providers, and the customers can share the best effective delivery information.

• 한국수자원학회논문집
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• 제42권4호
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• pp.331-341
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• 2009

본 연구에서는 generalized logistic(GL) 분포의 최우도 추정량(maximum likelihood estimate)에 대한 불확실성 추정을 위하여 사용되는 관측정보행렬(observed information matrix)과 Fisher 정보행렬(Fisher information matrix)의 정확도를 비교해 보고자 하였다. 타 분포형에 대한 기존의 연구결과에서 표본의 크기가 클 경우 매개변수 추정시 관측정보행렬이 동시에 추정되어 계산시간도 단축되고 Fisher 정보행렬의 정확도와도 차이도 거의 없어 관측정보행렬의 사용이 추천된 바 있으나, 최근 사용이 증가되고 있는 GL 분포에 대한 연구결과는 아직 전무한 실정이며 기존 연구문헌의 결과를 토대로 구체적인 연구 없이 관측정보행렬을 사용하고 있는 상황이다. 따라서 본 연구에서는 이를 위해 모의실험을 수행하였으며, 모의 결과 최우도법에 의한 매개변수의 분산 및 공분산은 기존의 연구 결과와 비슷한 결과를 보이나, quantile에 대한 불확실성 추정에는 관측정보행렬보다 Fisher 정보행렬의 사용이 더 적절할 것으로 판단되었다.

• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.119-129
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• 2003

We propose a new logistic regression model of normality curves for normal(diseased) and abnormal(nondiseased) classifications by neural networks in data mining. The fitted logistic regression lines are estimated, interpreted and plotted by the neural network technique. A few goodness-of-fit test statistics for normality are discussed and the performances by the fitted logistic regression lines are conducted.

• Journal of the Korean Data and Information Science Society
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• 제28권6호
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• pp.1565-1571
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• 2017

The standard logistic map is an iterative function, which forms a discrete-time dynamic system. The chaotic logistic map is a kind of ergodic map defined over the unit interval. In this paper we study the limiting behaviors on the several processes induced by the chaotic logistic map. We derive the law of large numbers for the process induced by the chaotic logistic map. We also derive the uniform law of large numbers for this process. When deriving the uniform law of large numbers, we study the role of bracketing of the indexed class of functions associated with the process. Then we apply the idea of DeHardt (1971) associated with the bracketing method to the process induced by the logistic map. We finally illustrate an application to Monte Carlo integration.

• 한국컴퓨터정보학회논문지
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• 제22권4호
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• pp.9-16
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• 2017

Machine learning has found widespread implementations and applications in many different domains in our life. Logistic regression is a type of classification in machine leaning, and is used widely in many fields, including medicine, economics, marketing and social sciences. In this paper, we present the MapReduce implementation of three existing algorithms, this is, Gradient Descent algorithm, Cost Minimization algorithm and Newton-Raphson algorithm, for logistic regression on RHadoop that integrates R and Hadoop environment applicable to large scale data. We compare the performance of these algorithms for estimation of logistic regression coefficients with real and simulated data sets. We also compare the performance of our RHadoop and RHIPE platforms. The performance experiments showed that our Newton-Raphson algorithm when compared to Gradient Descent and Cost Minimization algorithms appeared to be better to all data tested, also showed that our RHadoop was better than RHIPE in real data, and was opposite in simulated data.

• 경영과정보연구
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• 제7권
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• pp.427-450
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• 2001

This research suggests the estimation methodology of Logistics. This paper elucidates the main problems associated with estimation in the regression model. We review the methods for estimating the parameters in the model and introduce a modified procedure in which all models are fitted and combined to construct a combination of estimates. The resulting estimators are found to be as efficient as the maximum likelihood (ML) estimators in various cases. Our method requires more computations but has an advantage for large data sets. Also, it enables to detect particular features in the data structure. Examples of real data are used to illustrate the properties of the estimators. The backgrounds of estimation of logistic regression model is the increasing logistic environment importance today. In the first phase, we conduct an exploratory study to discuss 9 independent variables. In the second phase, we try to find the fittest logistic regression model. In the third phase, we calculate the logistic estimation using logistic regression model. The parameters of logistic regression model were estimated using ordinary least squares regression. The standard assumptions of OLS estimation were tested. The calculated value of the F-statistics for the logistic regression model is significant at the 5% level. The logistic regression model also explains a significant amount of variance in the dependent variable. The parameter estimates of the logistic regression model with t-statistics in parentheses are presented in Table. The object of this paper is to find the best logistic regression model to estimate the comparative accurate logistics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권8호
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• pp.3086-3101
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• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.