• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.4
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• pp.51-62
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• 2004

Fuzzy supervisory control technique for the seismic response control of cable-stayed bridges subject to earthquakes is studied. The proposed technique is a hybrid control method, which adopts a hierarchical structure consisting of several sub-controllers and a fuzzy supervisor. Sub-controllers are independently designed to reduced the responses to be controlled of a cable-stayed bridge, and a fuzzy supervisor achieves improved seismic control performance by tuning the pre-designed sub-controllers. It is realized by converting static gains of the sub-controllers into time-varying dynamic gains through the fuzzy inference mechanism. To evaluate the feasibility of the proposed technique, the benchmark control problem of cable-stayed bridge proposed by Dyke et al. is adopted. The control variables for the seismic response control of the cable-stayed bridge are determined to be t도 shear forces and bending moments at the base of the towers, the longitudinal displacements at the top of the towers, the relative displacements between the deck and the tower, and the tensions in the stay cables. Comparative results between the fuzzy supervisory controller and LQG controller demonstrate the effectiveness of the proposed control technique.

• Journal of Service Research and Studies
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• v.7 no.1
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• pp.83-96
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• 2017

The importance of service science is emphasized in the modern economy, and the value and necessity of service research still increasing. Since the service research framework was proposed, it has been studied from various perspectives and incorporated into one framework--service research. The direction of service research has been established and a new baseline of research has been established. However, the modern economic and social environment could be described as a new era, the Fourth Industrial Revolution has changed drastically. More and more systematic research on services has become necessary. Therefore, this study analyzed the field of service research in the existing framework. The study suggested how service research could broaden the horizon of service research by studying the 'what'. To do this, we analyzed recent service research trends by themes. We also identified the shortcomings of previous studies about service, and suggested directions and research themes for future research. Based on this study we developed a general approach to the creation of new models from the viewpoint of service science. The authors were also able to develop a general approach to areas such as service innovation, service inference, service solution, and service design leverage. In addition, it is necessary to extend service research and business model to the utilization of service technology. This approach could contribute to forming the basis of future service development, and to utilize social media to create new value of innovative company. The results of this study could contribute to deepening and expanding service research.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.571-576
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• 2002

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.

• Journal of KIISE
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• v.42 no.3
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• pp.307-319
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• 2015

Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).

• Journal of Korean Society of Transportation
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• v.19 no.5
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• pp.119-134
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• 2001

This paper proposed a dynamic transit vehicle simulation model and a dynamic transit passengers simulation model, which can simultaneously simulate the transit vehicles and passengers traveling on a transit network, and also developed an algorithm of dynamic departure time choice model based on individual passenger. The proposed model assumes that each passenger's behavior is heterogeneous based on stochastic process by relaxing the assumption of homogeneity among passengers and travelers have imperfect information and bounded rationality to more actually represent and to simulate each passenger's behavior. The proposed model integrated a inference and preference reforming procedure into the learning and decision making process in order to describe and to analyze the departure time choices of transit passengers. To analyze and evaluate the model an example transit line heading for work place was used. Numerical results indicated that in the model based on heterogeneous passengers the travelers' preference influenced more seriously on the departure time choice behavior, while in the model based on homogeneous passengers it does not. The results based on homogeneous passengers seemed to be unrealistic in the view of rational behavior. These results imply that the aggregated travel demand models such as the traditional network assignment models based on user equilibrium, assuming perfect information on the network, homogeneity and rationality, might be different from the real dynamic travel demand patterns occurred on actual network.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.369-386
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• 2005

Although Intelligent Tutoring System(ITS) offers individualized learning environment that overcome limited function of existent CAI, and consider many learners' variable, there is little development to be using at the sites of schools because of inefficiency of investment and absence of pedagogical content knowledge representation techniques. To solve these problem, we should study a method, which represents knowledge for ITS, and which reuses knowledge base. On the pedagogical content knowledge, the knowledge in education differs from knowledge in a general sense. In this paper, we shall primarily address the multi-complex structure of knowledge and explanation of learning vein using multi-complex structure. Multi-Complex, which is organized into nodes, clusters and uses by knowledge base. In addition, it grows a adaptive knowledge base by self-learning. Therefore, in this paper, we propose the 'Extended Neural Logic Network(X-Neuronet)', which is based on Neural Logic Network with logical inference and topological inflexibility in cognition structure, and includes pedagogical content knowledge and object-oriented conception, verify validity. X-Neuronet defines that a knowledge is directive combination with inertia and weights, and offers basic conceptions for expression, logic operator for operation and processing, node value and connection weight, propagation rule, learning algorithm.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.63-74
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• 1999

A sliding mode fuzzy control (SMFC) algorithm is presented for vibration of large structures. Rule-base of the fuzzy inference engine is constructed based on the sliding mode control, which is one of the nonlinear control algorithms. Fuzziness of the controller makes the control system robust against the uncertainties in the system parameters and the input excitation. Non-linearity of the control rule makes the controller more effective than linear controllers. Design procedure based on the present fuzzy control is more convenient than those of the conventional algorithms based on complex mathematical analysis, such as linear quadratic regulator and sliding mode control(SMC). Robustness of presented controller is illustrated by examining the loop transfer function. For verification of the present algorithm, a numerical study is carried out on the benchmark problem initiated by the ASCE Committee on Structural Control. To achieve a high level of realism, various aspects are considered such as actuator-structure interaction, modeling error, sensor noise, actuator time delay, precision of the A/D and D/A converters, magnitude of control force, and order of control model. Performance of the SMFC is examined in comparison with those of other control algorithms such as $H_{mixed 2/{\infty}}$ optimal polynomial control, neural networks control, and SMC, which were reported by other researchers. The results indicate that the present SMFC is an efficient and attractive control method, since the vibration responses of the structure can be reduced very effectively and the design procedure is simple and convenient.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.135-141
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• 2018

In this paper, we describe design and implementation of optimal smart home control system. Recent developments in technologies such as sensors and communication have enabled the Internet of Things to control a wide range of objects, such as light bulbs, socket-outlet, or clothing. Many businesses rely on the launch of collaborative services between them. However, traditional IoT systems often support a single protocol, although data is transmitted across multiple protocols for end-to-end devices. In addition, depending on the manufacturer of the Internet of things, there is a dedicated application and it has a high degree of complexity in registering and controlling different IoT devices for the internet of things. ARIoT system, special marking points and edge extraction techniques are used to detect objects, but there are relatively low deviations depending on the sampling data. The proposed system implements an IoT gateway of object based on OneM2M to compensate for existing problems. It supports diverse protocols of end to end devices and supported them with a single application. In addition, devices were learned by using deep learning in the artificial intelligence field and improved object recognition of existing systems by inference and detection, reducing the deviation of recognition rates.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.31-47
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• 2008

As OWL(Web Ontology Language) has been selected as a standard ontology description language by W3C, many ontologies have been building and developing in OWL. The lena developed by HP as an Application Programming Interface(API) provides various APIs to develop inference engines as well as storages, and it is widely used for system development. However, the storage model of Jena2 stores most owl documents not acceptable into a single table and it shows low processing performance for a large ontology data set. Most of all, Jena2 storage model does not consider hierarchical structures of classes and properties. In addition, it shows low query processing performance using the hierarchical structure because of many join operations. To solve these issues, this paper proposes an OWL ontology relational database model. The proposed model semantically classifies and stores information such as classes, properties, and instances. It improves the query processing performance by managing hierarchical information in a separate table. This paper also describes the implementation and evaluation results. This paper also shows the experiment and evaluation result and the comparative analysis on both results. The experiment and evaluation show our proposal provides a prominent performance as against Jena2.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.322-331
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• 2013

The purpose of the present study was to develop a convergent education contents for dementia prevention, operating on the web network applying modern information technology(IT). At the preparation stage, local and worldwide literatures related to dementia were analyzed followed by surveying industry demands, based on which the program was designed and developed. In the following enhancement stage, the program was modified as much as possible by advices obtained from experts in various fields. Development results of the present program are summarized as follows. Firstly, 645 intellect development model to prevent dementia was established through peer review and verification of convergent education theories by expert groups. This model was named as "Garisani" meaning "cognition capable of judging objects" in the Korean language. Secondly, 'Find a way' and 'Connect a line' modules were developed in the numeric field as well as 'Identify a letter(I, II)' modules, in the language field for web-based left brain training program. Thirdly, 'Find my car' and 'Vision training' modules in the attention field and 'Object inference' and 'Compare pictures' modules in the cognition field were developed for web-based right brain training program. Fourth, 'Pentomino' and 'BQmaze'(Brain Quotient and maze) modules in the space perception field and 'Visual training' in the memory field were developed for web-based left and right brains training. Fifth, all results were integrated leading to a 52 week Garisani convergent education program for dementia prevention.