• Journal of Korea Port Economic Association
• /
• v.24 no.1
• /
• pp.85-102
• /
• 2008

The purpose of this study is to analyze the effects of Logistic Information System on performance by efficiency of internal operation and organizational innovation. The results of this study as follows : The characteristic variable evaluation model that extended from the performance(user's value, perceived usefulness) of Logistic Information System were verified meaningfully. In this study, the efficiency of internal operation and organizational innovation were very important factor, to analyze the effects of Logistic Information System on performance. This study expect that Logistic Information System will achieve their Logistic Information System competitiveness through continuous quality measurement and improvement to increase the performance.

• Proceedings of the KSR Conference
• /
• 2009.05a
• /
• pp.1390-1394
• /
• 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
• /
• v.15 no.2
• /
• pp.367-378
• /
• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2001.10a
• /
• pp.7-10
• /
• 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.

• Journal of Korea Water Resources Association
• /
• v.42 no.4
• /
• pp.331-341
• /
• 2009

In this study, we compared the observed information matrix with the Fisher information matrix to estimate the uncertainty of maximum likelihood estimators of the generalized logistic (GL) distribution. The previous literatures recommended the use of the observed information matrix because this is convenient since this matrix is determined as the part of the parameter estimation procedure and there is little difference in accuracy between the observed information matrix and the Fisher information matrix for large sample size. The observed information matrix has been applied for the generalized logistic distribution based on the previous study without verification. For this purpose, a simulation experiment was performed to verify which matrix gave the better accuracy for the GL model. The simulation results showed that the variance-covariance of the ML parameters for the GL distribution came up with similar results to those of previous literature, but it is preferable to use of the Fisher information matrix to estimate the uncertainty of quantile of ML estimators.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.1
• /
• pp.119-129
• /
• 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
• /
• v.28 no.6
• /
• pp.1565-1571
• /
• 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.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.4
• /
• pp.9-16
• /
• 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.

• Management & Information Systems Review
• /
• v.7
• /
• pp.427-450
• /
• 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)
• /
• v.15 no.8
• /
• pp.3086-3101
• /
• 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.