• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.435-440
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• 2001

The development of web-aware knowledge discovery system has received a great deal of attention in recent years. It plays a key-enabling role for competitive businesses in the E-commerce era. One of the challenges in developing web-aware knowledge discovery systems is to integrate and coordinate and coordinate existing standalone or legacy knowledge discovery applications in a seamless manner, so that cost-effective systems can be developed without the need of costly proprietary products. In this paper, we present an approach for developing a framework of web-aware interoperable knowledge discovery system to achieve this purpose. This approach applies RMI and high-level code wrapper of Java distributed object computing to address the issues of interoperability in heterogeneous environments, which includes programming language, platform, and visual object model. The effectiveness of the proposed framework is demonstrated through the integration and extension of the two well-known standalone knowledge discovery tools, SOM_PAK and Nenet. It confirms that a variety of interoperable knowledge discovery systems can be constructed efficiently on the basis of the framework to meet various requirements of knowledge discovery tasks.

• The Journal of Information Systems
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• v.15 no.4
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• pp.171-188
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• 2006

Information technology and the Internet have had a dramatic effect on education method and individual life. Universities and companies we making large investments in e-Learning applications but are hard to pressed to evaluate the success of their e-Learning systems. e-Learning can be seen as not only one of Internet based information systems which can provide education services but also one of teaching-teaming methods which can implement self-directed teaming. This paper tests the updated model of information system success proposed by Delone and McLean using a field study of a e-Learning. The five dimensions - information quality, system quality, service quality, user satisfaction, net benefit - of the updated model are parsimonious framework for organizing the e-learning success metrics identified in the literature. Questionaires are collected from 107 students who are enrolling a e-learning class using online survey. The model is tested using SPSS and LISREL. The results show that information quality and service quality are significant predictors of user satisfaction with the e-Learning system but system quality is not. Also user satisfaction is found to be a strong predictor of the learning performance. This strong association between user satisfaction and teaming performance suggests that user satisfaction may serve as a valid surrogate for teaming performance. Empirical testing of the updated DeLone & McLean model should therefore be extended to cover a wider variety of systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1125-1132
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• 2010

WiBro(Wireless Broadband Internet) is the standard of high-speed portable internet based on OFDMA/TDD (Orthogonal frequency division multiple access / Time division duplexing) techniques, and the subset of consolidated version of IEEE802.16e Wireless MAN standard. In this paper, we propose performance improvements of WiBro system using the 64QAM SOFM(Self-Organizing Feature Maps)prefiltering. Proposed method used the prefiltering SOFM neural network blind equalization in the Broadband 64 QAM WiBro system receiver. The prefiltering SOFM neural network constellates 64QAM that is transmitter data shape and the blind equalization removes ICI(Inter Carrier Interference). To verificate the proposed method usability, the MSE and the BER are simulated. The simulation results shown that is improved the performances of the proposed WiBro system using the 64QAM SOFM Prefiltering than the existing WiBro system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1291-1300
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• 2008

In this paper, we propose a intelligent system to improve the accuracy of pattern recognition. The proposed intelligent system consist in SOFM, LVQ and FCM algorithm. We are confirmed the effectiveness of the proposed intelligent system through the several experiments that classify Fisher's Iris data and face image data that offered by ORL of Cambridge Univ. and EMG data. As the results of experiments, the proposed intelligent system has better accuracy of pattern recognition than general LVQ.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.295-306
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• 2004

In this paper, a novel air-interface is presented for Fleet-Net1, a self-organizing network for inter-vehicle and vehicle-toroadsidecommunication. The air-interface is based upon the lowchip-rate version of UMTS/TDD. To adapt the cellular UMTS standard to an air-interface for ad hoc networks, changes of the physical layer, medium access control sub-layer and radio resource management are required. An overview of the required modifications is given here. Particularly, a decentralized synchronization mechanism is presented and analyzed by means of simulations. Furthermore, changes for the medium access control are explained in detail, which allow for an efficient operation in partly meshed networks and prioritization. Performance results of the overall system considering throughput and delay are derived by means of analytical evaluations and event-driven simulations. Based on realistic mobility models, it is shown that the presented solution provides a robust communication platform even in vehicular environments. The proposed air-interface is a cost-effective solution not only for inter-vehicle communication, but also for ad hoc networking in general, benefiting from the mass-market of UMTS.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.333-336
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• 2007

지능로봇에 관한 열기가 확산되면서 다양한 로봇이 연구, 개발되고 있다. 본 논문에서는 소리 인식, 변신 기능, 무게 중심 인식, 장애물 감지 및 4족 자율보행의 기능을 가지는 로봇의 설계 및 제작에 관하여 기술한다. 자기변신 로봇의 무게 중심이 아래로 향할 수 있도록 현재 상태를 파악한 후 중심 추를 이동시켜서 무게 중심을 변화시킨다. 이동 중 소리 인식과 장애물 인식 및 낭떠러지 인식으로 원활한 이동을 하기위해 다양한 센서들을 사용 한다. 시뮬레이션을 통해 제안한 방법의 유용성을 검증하고, 이를 실제 자기변신 로봇의 설계 및 제작에 적용하여 타당성을 입증한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.8-17
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• 1998

Autonomous mobile robot agents(AMRAs) are robots which perform a given task by the same distributed self-organizing rules. In this paper, we let them form an equi-distance circle for the task of cooperative behaviors. Here, we suggest an algorithm to perform the taqk and then utilize a fuzzy system to improve the algorithm. It is shown that various cooperative activities appear in the simulation and particularly chaotic behaviors also appear by increasing the robot speed. Moreover, we prove the characteristics of this chaotic behaviors by calculating Lyapunov exponent.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.253-257
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• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.220-222
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• 2001

컴퓨터의 사용인구가 많아지고, 인터넷의 보급이 급속히 늘어남에 따라 많은 정보가 생산되고 있다. 그리고 이러한 정보를 사용자에게 좀더 효율적인 방법으로 제공하는 서비스들도 많아지게 되었다. 그러나 컴퓨터에 익숙하지 않은 사용자들은 쉽게 이러한 서비스를 이용하지 못하기 때문에 사용자를 돕는 시스템이 필요하다. 인터넷 서비스제공 업체들은 사용자의 질문에 대해 관리자가 직접 답을 해주는데, 이들 시스템을 이용하는 사용자들의 증가로 질의응답 업무의 양이 커지고 있다. 본 논문에서는 이를 해결하기 위해 사용자의 질의를 자동으로 분류하여 응답하고 사용자가 FAQ를 개념적으로 브라우징 할 수 있는 시스템의 유용성을 입증하기 위하여, 그 적용 가능성과 일반 사용자들의 이용 결과를 통계적으로 분석하였다.