• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.145-154
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• 2004

The growing demand for customer-responsive, made-to-order manufacturing is stimulating the need for improved dynamic decision-making processes in commercial fleet operations. Moreover, the rapid growth of electronic commerce through the internet is also requiring advanced and precise real-time operation of vehicle fleets. Accompanying these demand side developments/pressures, the growing availability of technologies such as AVL(Automatic Vehicle Location) systems and continuous two-way communication devices is driving developments on the supply side. These technologies enable the dispatcher to identify the current location of trucks and to communicate with drivers in real time affording the carrier fleet dispatcher the opportunity to dynamically respond to changes in demand, driver and vehicle availability, as well as traffic network conditions. This research investigates key aspects of real time dynamic routing and scheduling problems in fleet operation particularly in a truckload pickup-and-delivery problem under various settings, in which information of stochastic demands is revealed on a continuous basis, i.e., as the scheduled routes are executed. The most promising solution strategies for dealing with this real-time problem are analyzed and integrated. Furthermore, this research develops. analyzes, and implements hybrid algorithms for solving them, which combine fast local heuristic approach with an optimization-based approach. In addition, various partitioning algorithms being able to deal with large fleet of vehicles are developed based on 'divided & conquer' technique. Simulation experiments are developed and conducted to evaluate the performance of these algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.973-980
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• 2024

Fuzzing is an automated testing technique that generates a lot of testcases and monitors for exceptions to test a program. Recently, fuzzing research using machine learning has been actively proposed to solve various problems in the fuzzing process, but a comprehensive evaluation of fuzzing research using machine learning is lacking. In this paper, we analyze recent research that applies machine learning to scheduling techniques for fuzzing, categorizing them into reinforcement learning-based and supervised learning-based fuzzers. We evaluated the coverage performance of the analyzed machine learning-based fuzzers against real-world programs with four different file formats and bug detection performance against the LAVA-M dataset. The results showed that AFL-HIER, which applied seed clustering and seed scheduling with reinforcement learning outperformed in coverage and bug detection. In the case of supervised learning, it showed high coverage on tcpdumps with high code complexity, and its superior bug detection performance when applied to hybrid fuzzing. This research shows that performance of machine learning-based fuzzer is better when both machine learning and additional fuzzing techniques are used to optimize the fuzzing process. Future research is needed on practical and robust machine learning-based fuzzing techniques that can be effectively applied to programs that handle various input formats.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.236-244
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• 2005

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks(FPNN) that is based on fuzzy relation and evolutionally optimized Multi-Layer Perceptron, discuss a comprehensive design methodology and carry out a series of numeric experiments. The construction of the evolutionally optimized FPNN(EFPNN) exploits fundamental technologies of Computational Intelligence. The architecture of the resulting EFPNN results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks(PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the EFPNN. The consequence part of the EFPNN is designed using PNN. As the consequence part of the EFPNN, the development of the genetically optimized PNN(gPNN) dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the EFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed EFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.890-895
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• 2016

As the wireless communication technique is developing rapidly, the use of smart devices is increasing. Due to gradually increasing data traffic, a new area, more than 6GHz of bandwidth to increase capacity of the network, has been studied. Millimeter Wave(MmWave) communications utilizes the bandwidth above 6GHz, which makes it possible to achieve one gigabit per second data rate. To overcome the path loss due to the smaller wavelength, the mass of the antenna arrangement is used. This paper presents an algorithm that maximizes the spectral efficiency of the system in the pre-coding process using a hybrid beamforming. Also it is suggested with the optimization of the number of beams that maximizes the spectral efficiency was maximized by the propose method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.327-327
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• 2010

자동차에서 실내음질은 구매결정 요소들 중의 하나로 그 중요성이 점차 증가하고 있다. 따라서 다양한 운전조건에서 운전자가 기대하는 실내음질의 기대 수준을 충족시켜야 한다. 소비자는 운전경험과 습관에 따라 기대하는 음질에 차이가 있고 소리에 대한 표현방식도 모호하기 때문에 이러한 주관적 특성을 하나의 통일된 표현으로 정의 하기가 어렵다. 그러나 지난 이십여 년 동안의 음질개발과 차량 실내소음 주관평가의 통계처리로 통일된 표현을 할 수 있었다. 나아가 심리음향학 및 신호처리기술의 발달과 꾸준한 음질연구결과로 소리특성을 객관적으로 나타내는 소리의 시각화가 가능하였으며, 운전자가 인식하는 주관평가와의 상관관계를 높여 차량의 대표적인 음질인자로 정량화하여 음질목표를 설정할 수 있었다. 실내소음의 구성은 엔진 투과음, 흡배기 소음, 바람 소음, 도로 기인 소음 등으로 다양하므로 소음원에 따라 음의 균형을 맞추어 조화로운 음질개발을 하는 것이 중요했다. 또한 차량 판매되는 지역에 따라 선호음이 상이하여 지역별 실내음질의 차별화가 필요했다. 궁극적으로는 운전자의 감성품질을 만족할 수 있도록 음을 제어하여 브랜드 사운드를 개발하고 있다. 이러한 실내음질을 달성하기 위한 방법으로 소음원과 전달경로에 대해 기여도를 분석하고, 경로를 구성하는 시스템 별로 세분화하여 시스템 목표를 설정하였다. 시스템 개발에 중요한 인자로 차량의 동강성 및 흡차음 성능을 들 수 있다. 특히 디젤차량의 비중이 큰 유럽업체의 차량의 동강성 및 흡차음 개발 능력은 높게 평가되고 있다. 이에 유럽의 부품전문회사가 가지고 있는 해석과 시험적인 개발 방법을 통하여 전달계 특성을 만족하기 위한 시스템의 동강성 및 흡차음 특성을 개발하고 있다. 차량음질 튜닝의 중요한 기법 중 하나로 흡배기 개발을 추진하고 있다. 친환경자동차인 하이브리드차량, 전기차량 및 연료전지차량의 경우 전기구동부품에서 발생하는 각종 이음 발생을 최소화 했다. 보행자를 보호하고 운전의 즐거움을 향상하기 위한 가상사운드 개발을 진행하고 있다. 회사 수익성 향상을 위한 원가절감 및 구조 경량화에 따른 음질악화와 연비 향상 및 배기가스 규제 강화로 고성능 고출력 엔진탑재에 따른 음질악화 요인을 극복해야 했다. 운전자의 청감은 차량의 운전성에 따라서도 크게 영향을 받게 되므로 엔진제어와 변속기제어를 통해 음질과 운전성이 조화를 이룰 수 있도록 개발하고 있다. 향후, 소음원에 따른 시스템 최적화 개발, 운전성과 음질 연계 개발과 친환경차량의 가상사운드 개발 등이 자동차 음질 개발의 중요한 이슈로 생각한다.

• The Transactions of the Korean Institute of Power Electronics
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• v.15 no.5
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• pp.369-375
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• 2010

This paper presents a design and implementation of 3.3 kW on-board battery charger for electric vehicles or plug-in hybrid electric vehicles. Considering characteristics of the electric vehicles, a series-loaded resonant dc-dc converter and frequency control scheme are adopted to improve efficiency and reliability, and to reduce volume and cost. The developed on-board battery charger is designed and implemented by using high frequency of 80-130 kHz and zero voltage switching method. The experimental result indicates 92.5% of the maximum efficiency, 5.84 liters in volume, and 5.8kg in weight through optimal hardware design.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.11
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• pp.409-418
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• 2014

The amount of data located in storage servers has dramatically increased with the growth in cloud and social networking services. Storage systems with very large capacities may suffer from poor reliability and long latency, problems which can be addressed by the use of a hybrid disk, in which mechanical and flash memory storage are combined. The Linux-based SSD(solid-state disk) uses a caching technique based on the DM-cache utility. We assess the limitations of DM-cache by evaluating its performance in diverse environments, and identify problems with the caching policy that it operates in response to various commands. This policy is effective in reducing latency when Linux is running in native mode; but when Linux is installed as a guest operating systems on a virtual machine, the overhead incurred by caching actually reduces performance.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.81-90
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• 2014

It is an element certainly required for the cost reduction of a company that forward and reverse logistics chain are unified and constitutes a resource closed-loop supply chain (CLSC). In this study, the inventory control which unifies inventory of distribution centers (DCs) of forward logistics and processing center of reverse logistics in the CLSC environment is proposed. The inventory system model for newly-constructed CLSC considers the JIT(Just-In-Time) delivery from the processing center to the manufacturer, including the making of decisions on whether to wait for the arrival of end-of-life products or to back-order necessary products for manufacturer when the supply of end-of-life products at the processing center via the returning center is insufficient for the demands of the manufacturers. The validity of the proposed model was verified using the genetic algorithm (GA). In order that a parameter might investigate the effect which it has on a solution, the simulation was carried out for priGA(priority-based GA) on three kinds of parameter conditions. Moreover, mhGA(modified hybrid GA) to which a parameter is adjusted for every Study on Reducing Logistics Costs and Inventory Control System according to facilities integration in the Closed-Loop Supply Chain Environment generation, the simulation was carried out to a four-kind numerical example.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.273-280
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• 2021

This paper presents the results of study of linear oscillating electric machine; Cartesian, cylindrical type with permanent magnet, flux reversal, cylindrical reluctance, and transverse flux type. The focus of the work is the suggestion of the characteristics and design process of propose topology, respectively. First of all, there are five types of the proposed to this study on the basis of the existing literatures; Cartesian type, cylindrical type, flux reversal type, cylindrical reluctance type, and transverse flux type. All topology is achieved using equivalent magnetic circuit considering leakage elements as initial modeling. Cartesian type is investigated by number of phases and number of pole pairs using optimal process. A cylindrical type is described by number of phases and displacement of stroke. The flux reversal type is proposed based on the symmetrical and non symmetrical stator cores of the surface mounted PMs mover, and non slanted PMs and slanted PMs of the flux concentrating PMs mover. A cylindrical reluctance type is studied by the shape of mover teeth in geometric aspect to reduce force ripple and increase magnetic flux. A transverse flux type is considered by dividing the transverse flux electric excited and the transverse flux permanent magnet excited. It is significant that the study gives a design rules and features of linear oscillating electric machine.