• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2002.05a
• /
• pp.979-986
• /
• 2002

Constraint Programming is an appealing technology for modeling and solving various real-world problems. and metaheuristic is the most successful technique available for solving large real-world vehicle routing problems. Constraint Programming and metaheuristic are complementary to each other. This paper describes how iterative improvement techniques can be used in a Constraint Programming framework(LOG Solver and ILOG Dispatcher) for Vehicle Routing Problem. As local search gets trapped in local solution, the improvement techniques are used in conjunction with metaheuristic method.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.1
• /
• pp.44-51
• /
• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.3
• /
• pp.263-267
• /
• 2005

Building classifiers for financial real-world classification problems is often plagued by severely overlapping and highly skewed class distribution. New performance measures such as receiver operating characteristic (ROC) curve and area under ROC curve (AUC) have been recently introduced in evaluating and building classifiers for those kind of problems. They are, however, in-effective to evaluation of classifier's discrimination performance in a particular class of the classification problems that interests lie in only a local operating range of the classifier, In this paper, a new method is proposed that enables us to directly improve classifier's discrimination performance at a desired local operating range by defining and optimizing a partial area under ROC curve or domain-specific curve, which is difficult to achieve with conventional classification accuracy based learning methods. The effectiveness of the proposed approach is demonstrated in terms of fraud detection capability in a real-world fraud detection problem compared with the MSE-based approach.

• Journal of Auto-vehicle Safety Association
• /
• v.5 no.1
• /
• pp.11-15
• /
• 2013

Small overlap crash caused fatal injury in real-world crash. IIHS(Insurance Institute for Highway Safety) proposed the small overlap test. The objective of this study is to analyze dummy injury criteria and dummy excursion on the sled reinforced body angle. Result of the comparisons of dummy injury criteria of a head, neck, and chest was best correlation between sled and vehicle test on base $angle+3^{\circ}$. However, lower extremity was not correlation because sled test could not copy of intrusion. There were a correlation between dummy movement and sled reinforced body angle. Sled reinforced body angle affects the lateral direction of excursion more than longitudinal excursion.

• IE interfaces
• /
• v.24 no.1
• /
• pp.51-57
• /
• 2011

The traveling purchaser problem (TPP) is a generalization of the well-known traveling salesman problem (TSP), which has many real-world applications such as purchasing the required raw materials for the manufacturing factories and the scheduling of a set of jobs over some machines, and many others. In the last decade, TPP has received some attention of the researchers in the operational research area. However, all of the past researches for TPP are restricted on a single purchaser (vehicle). It could be the limitation to solve the real world problem. The purpose of this paper is to suggest the capacitated multiple TPP (CMTPP). It could be used in inbound logistics optimization in supply chain management area and many others. Since TPP is known as NP-hard, we also developed the heuristic algorithm to solve the CMTPP.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2544-2557
• /
• 2019

Adaptive bitrate streaming is a crucial element in the implementation of high-quality streaming of media content over the Internet. Dynamic adaptive streaming over HTTP (MPEG-DASH) has lately emerged as the de facto solution for over-the-top (OTT) video streaming services. In this paper, we perform macro-level analysis on a real-world MPEG-DASH-based Internet live broadcasting service to gain insight into its behavior throughout the end-to-end service-provision chain, from broadcasters to viewers. Based on this analysis, we propose methods to improve the quality-of-experience (QoE) of MPEG-DASH-based services, particularly with regard to reducing broadcasting delays between recording and viewing of videos, as well as synchronizing client terminals.

• Journal of Information Technology Services
• /
• v.18 no.1
• /
• pp.79-90
• /
• 2019

Maximizing energy efficiency minimizes the energy consumption of computation, storage and communications required for IT services, resulting in economic and environmental benefits. Recent advancement of flash and next generation non-volatile memory technology and price decrease of those memories have led to the rise of so-called AFA (All-Flash Array) storage devices made of flash or next generation non-volatile memory. Currently, the AFA devices are rapidly replacing traditional storages in the high-performance servers due to their fast input/output characteristics. However, it is not well known how effective the energy efficiency of the AFA devices in the real world. This paper shows input/output performance and power consumption of the AFA devices measured on the Linux XFS file system via experiments and discusses energy efficiency of the AFA devices in the real world.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.11
• /
• pp.4557-4572
• /
• 2020

The Alaska competition provides an opportunity to study the practical problems of real-world steganalysis. Participants are required to solve steganalysis involving various embedding schemes, inconsistency JPEG Quality Factor and various processing pipelines. In this paper, we propose a method to ensemble multiple deep learning steganalyzers. We select SRNet and RESDET as our base models. Then we design a three-layers model ensemble network to fuse these base models and output the final prediction. By separating the three colors channels for base model training and feature replacement strategy instead of simply merging features, the performance of the model ensemble is greatly improved. The proposed method won second place in the Alaska 1 competition in the end.

• Journal of Preventive Medicine and Public Health
• /
• v.54 no.3
• /
• pp.166-172
• /
• 2021

Traditional epidemiological assessments, which mainly focused on evaluating the statistical association between two major components-the exposure and outcome-have recently evolved to ascertain the in-between process, which can explain the underlying causal pathway. Mediation analysis has emerged as a compelling method to disentangle the complex nature of these pathways. The statistical method of mediation analysis has evolved from simple regression analysis to causal mediation analysis, and each amendment refined the underlying mathematical theory and required assumptions. This short guide will introduce the basic statistical framework and assumptions of both traditional and modern mediation analyses, providing examples conducted with real-world data.

• Journal of Information Processing Systems
• /
• v.18 no.2
• /
• pp.268-281
• /
• 2022

The development of information technology is bringing many changes to everyday life, and machine learning can be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data are essential processes in applying machine learning to real-world problems. As a method of processing data in machine learning, we propose an approach based on applying multiple linear regression models by interlacing data to the task of classifying similar software. Linear regression is widely used in estimation problems to model the relationship between input and output data. In our approach, multiple linear regression models are generated by training on interlaced feature data. A combination of these multiple models is then used as the prediction model for classifying similar software. Experiments are performed to evaluate the proposed approach as compared to conventional linear regression, and the experimental results show that the proposed method classifies similar software more accurately than the conventional model. We anticipate the proposed approach to be applied to various kinds of classification problems to improve the accuracy of conventional linear regression.