• Journal of Korea Port Economic Association
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• v.30 no.4
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• pp.47-67
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• 2014

This study examined elements which could evaluate a container terminal logistics system from the viewpoint of supply chain management. This study derived the elements of a container terminal logistics system such as flexibility, reliability, responsiveness, and information sharing and 16 evaluation sub-items in the aspect of a supply chain. In the result of analysis, the weight between SCM elements of a container terminal logistics system was the highest in reliability(0.282), followed by flexibility(0.273), responsiveness(0.224), and information sharing(0.221). The conversion weight was calculated by combining the weight of elements of a container terminal logistics system and the weight of evaluation sub-items. The highest weight which was considered as the most important factor to evaluate a container terminal logistics system was work planning(berth, yard) of flexibility(0.081), followed by accurate fulfillment of container work schedule(ship, yard) and the optimum distribution and arrangement of equipment(QC, TC, YT)(0.079), stable works without damage of containers and ships(0.071), and preventive maintenance of equipment and operators' skill(0.070).

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.42-51
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• 2008

We believe that smart spaces, offering pervasive services, will proliferate. However, at present, those islands of smart spaces should be joined seamlessly with each other. As users move about, they will have to roam from one autonomous smart space to another. When they move into the new island of smart space, they should setup their devices and service manually or not have access to the services available in their home spaces. Sometimes, there will conflicts between users when they try to occupy the same space or use a specific device at the same time. It will also be critical to elder people who suffer from Alzheimer or other cognitive impairments when they travel from their smart space to other visited spaces (e.g., grocery stores, museums). Furthermore our experience in building the Gator Tech Smart House reveals to us that home residents generally do not want to lose or be denied all the features or services they have come to expect simply because they move to a new smart space. The seamless inter-space interaction requirements and issues are raised automatically when the ubiquitous pervasive computing system tries to establish the user's service environment by allocating relevant resources after the user moves to a new location where there are no prior settings for the new environment. In this paper, we raise and present several critical inter-space interactions issues impacting middleware architecture design of ubiquitous pervasive computing. We propose requirements for resolving these issues on seamless inter-space operation. We also illustrate our approach and ideas via a service scenario moving around two smart spaces.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11a
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• pp.201-204
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• 2003

At a consequence of WWW large popularity, the internet has suffered from various performance problems, such as network congestion and overloaded servers. These days, it is not uncommon to find servers refusing connections because they are overloaded. Web server performance has always been a key issue in the design and operation of on-line systems. With regard to Internet, performance is also critical, because users want fast and easy access to all objects (e.g., documents, graphics, audio, and video) available on the net. To solve this problem, a number of companies are exploring the benefits of having multiple geographically or locally distributed Internet sites. This requires a comprehensive scheme for traffic management, which includes the principle of an optimal load balancing of client requests across multiple clusters of real servers. This paper focuses on the performance analysis of Web server and we apply these results to load balancing in clustering web server. It also discusses the mam steps needed to carry out a WWW performance analysis effort and shows relations between the workload characteristics and system resource usage. Also, we will introduce an optimal load balancing algorithm base on the RTSM (Real-Time Server Monitor) and Fuzzy Inference Engine for the local status of a real server, and the benefits is provided with of the suggested method.

• Journal of Navigation and Port Research
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• v.32 no.3
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• pp.199-205
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• 2008

Currently, ITs(Information Technologies} performances have been recognized as an essential tool for terminal operations. However, there have been little research that analyze level of ITs performance on port operation. Thus, the aim of this research is to analyze current level of ITs performance of major container terminals in Busan and Gwangyang. To achieve this objective, we developed HFP-based index that improve pervious indicator for evaluation of ITs performance. And then, level of ITs performance of four major container terminals were measured by the collected data through face to face interviews. Finally, through this analysis, some findings including weaknesses of ITs performance and implications were discussed.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.4
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• pp.581-589
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• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.299-302
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• 2019

스마트홈 구축을 위한 다양한 IoT 기술 발전과 더불어 IoT 장비들의 저전력, 고효율, 편리성에 대한 사용자 니즈에 대한 관심이 급증하고 있다. 이러한 사용자 니즈를 반영하듯이 최근 아두이노 및 라즈베리 파이등 저비용으로 IoT 장비를 구축할 수 있는 다양한 연구들이 진행되고 있다. 우리는 본 연구를 통해 뉴로-퍼지기술을 이용해 저전력, 고효율, 편리성을 겸비한 저렴한 IoT 장비 및 솔루션을 제안하고자 한다. 뉴로-퍼지를 이용하게 되면 기존의 단순한 센싱에 대한 제어와 다르게 다양한 환경변수를 분석하고 고려해 효율적인 제어가 가능할 수 있다. 본 논문에서는 욕실 환경에서의 발생할 수 있는 다양한 환경변수를 추출하고 사용자가 편리하게 사용할 수 있도록 실시간 발생하는 상황과 온도, 습도 등을 뉴로-퍼지를 통해 제어할 수 있는 알고리즘을 설계한다. 그리하여 뉴로-퍼지를 이용하여 설계된 욕실환경 제어 시스템은 사용자의 욕실 이용에 효과적인 영향을 줄 수 있을 것이다. 우리는 본 연구에서 저비용 고효율의 효율적 시스템을 구성할 수 있는 IoT 센서와 아두이노 및 라즈베리 파이를 활용해 다양한 IoT 장비들을 모니터링하고 분석하여 쾌적한 스마트 홈을 구축할 수 있는 방향을 제시하였다. 또한 그러한 IoT 장비들을 제어할 수 있는 IoT gateway system을 설계하고 구현하였다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.177-186
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• 2014

Failure mode and effect analysis (FMEA) is a systematic approach for identifying potential failures before they occur, with the intent to minimize the risk associated with them. It has been widely used in the various manufacturing industries as a solution to reliability problems. As the importance of the service sector is increasing, however, it has been recently extended to some applications in services. Despite these attempts, FMEA cannot be directly applied to the reliability problems in a service industry. Due to the heterogeneity and customer participation in service process, we cannot perfectly prevent service failures. For this reason, we suggest a new risk priority number with three input parameters that consist of severity, probability of occurrence, and recoverability. In this paper, we propose an approach for assessing service risk and service reliability using the service-oriented risk priority number (S-RPN). An example regarding a hypermarket service process is used to demonstrate the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.95-103
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• 1998

This paper deals with a problem occuring in recognition of tactile images due to the effects of imposed force at a me urement moment. Tactile image of a contact surface, used for recognition of the surface type, varies depending on the forces imposed so that a false recognition may result in. This paper fuzzifies two parameters of the contour of a tactile image with the membership function formed by considering the imposed force. Two fuzzifed paramenters are fused by the average Minkowski's dist; lnce. The proposed algorithm was implemented on the multisensor system cnmposed of an optical tact le sensor and a 6 axes forceltorque sensor. By the experiments, the proposed algorithm has shown average recognition ratio greater than 869% over all imposed force ranges and object models which is about 14% enhancement comparing to the case where only the contour information is used. The pro- ~oseda lgorithm can be used for end-effectors manipulating a deformable or fragile objects or for recognition of 3D objects by implementing on multi-fingered robot hand.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4C
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• pp.255-264
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• 2006

The auxiliary reinforcement method is normally applied to prevent a possible collapse of the tunnel face where the ground condition is not favorable or geologic information is not sufficient. Recently, several engineering approaches have been made to choose the effective reinforcement methods using expert system such as neural network and fuzzy theory field, among others. Even if the expert system has offered many decision aid tools to properly select the reinforcement method, the quantitative assessment items are not easy to estimate and this is why the data mining technique, widely used in the field of social science, medical treatment, banking and agriculture, is introduced in this study. Using decision tree together with PDA, the decision aids for reinforcement method based on field construction data are created to derive the field rules and future study will be concentrated on the application of the proposed methods in a variety of underground development cases.