• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.5
• /
• pp.474-480
• /
• 2007

This paper presents an application of intelligent modeling method to manual control system with human operator. Human operator as a part of controller is difficult to be modeled because of changes in individual characteristics and operation environment. So in these situation, a fuzzy model developed relying on the expert's experiences or trial and error may not be acceptable. To supplement the fuzzy model block, a neural network based modeling error compensator is incorporated. The feasibility of the present fuzzy-neural modeling scheme has been investigated for the real human based target tracking system.

• Proceedings of the Korean Reliability Society Conference
• /
• 2000.04a
• /
• pp.223-230
• /
• 2000

This work aims to develop modeling methodology of machine part reliability. The reliability model is to be used for predicting and improving reliability in planning and design processes of products. In order to develop the reliability model of machine parts, the functions and interactions of sub-units of machine parts are analyzed first and function network is constructed. Using the function network, function block diagram is developed, which can be the basis for deriving reliability block diagram. Modeling of machine part reliability has not been widely studied since the reliability modeling of machine parts requires understanding of the functions and failures of their components in several viewpoints. This work tries to find general methodology of reliability modeling and proposes a framework for reliability improvement during machine part development.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.247-260
• /
• 2021

Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims to provide richer expressiveness and incorporate a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate the viewing of connections and ideas on a database. The described structure of the data is often represented in an entity–relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes, and relationships. This paper attempts to examine the basic ER modeling notions in order to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships, and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.2
• /
• pp.215-221
• /
• 2003

Generally, we want to be searched the newest as well as some appropriate personalized information from the internet resources. However, it is a complex and repeated procedure to search some appropriate information. Moreover, because the user's interests are changed as time goes, the real time modeling of a user's interests should be necessary. In this paper, I propose PREA system that can search and filter documents that users are interested from the World Wide Web. And then it constructs the user's interest model by a modified spreading neural network. Based on this network, PREA can easily produce some queries to search web documents, and it ranks them. The conventional spreading neural network does not have a visualization function, so that the users could not know how to be configured his or her interest model by the network. To solve this problem, PREA gives a visualization function being shown how to be made his interest user model to many users.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.196-196
• /
• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, teaming itself, and the adequacy of the design controller. The performance of the controller can be verified through simulation. The real autonomous electric vehicle using neural network controller verified good results.

• Journal of Information Technology Services
• /
• v.18 no.4
• /
• pp.115-134
• /
• 2019

The global music industry has been growing steadily every year. In particular, the proportion of music streaming services in the overall market are expanding. It shows same pattern in domestic market. Also, several companies are providing music streaming services competitively. It is clear that there is growing interest in effective operations of music streaming services. In this study, we examine the impact of customization and network effect on service quality and customer loyalty. We collect survey data and analyze relationships between latent variables using structural equations modeling. We find that customization and network effect of music streaming services positively affect service quality. However, the effect of customization on customer loyalty is not significant and there is a negative effect of network effect on customer loyalty. Finally, we find that service quality works as a mediator between customization and customer loyalty, and service quality also works as a mediator between network effect and customer loyalty.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.155-162
• /
• 2003

Today's network consists of a large number of routers and servers running a variety of applications. Policy-based network provides a means by which the management process can be simplified and largely automated. In this paper we build a foundation of policy-based network modeling and simulation environment. The procedure and structure for the induction of policy rules from vulnerabilities stored in SVDB (Simulation based Vulnerability Data Base) are developed. The structure also transforms the policy rules into PCIM (Policy Core Information Model). The effect on a particular policy can be tested and analyzed through the simulation with the PCIMs and SVDB.

• Journal of Korean Society of Transportation
• /
• v.12 no.3
• /
• pp.49-64
• /
• 1994

The development of a mathematical modular framework based on Petri Network theory to model a traffic network is the subject of this paper. Traffic intersections are the primitive elements of a transportation network and are characterized as event driven and asynchronous systems. Petri network have been utilized to model these discrete event systems; further analysis of their structure can reveal information relevant to the concurrency, parallelism, synchronization, and deadlock avoidance issuse. The Petri-net based model of a generic traffic junction is presented. These modular networks are effective in synchronizing their components and can be used for modeling purposes of an asynchronous large scale transportation system. The derived model is suitable for simulations on a multiprocessor computer since its program execution safety is secured. The software pseudocode for simulating a transportation network model on a multiprocessor system is presented.

• Journal of Internet Computing and Services
• /
• v.5 no.3
• /
• pp.87-98
• /
• 2004

There are several types of distributed subscribers network using Asymmetric Digital Subscriber Line (ADSL), Very high-bit rate Digital Subscriber Line (VDSL), and Data Over Cable Service Interface Specifications (DOCSIS), The efficient and concentrated network management of those several distributed subscribers networks and resources require the general information model of network, which has abstract and conceptional managed objects independent of type of network and its equipment to manage the integrated subscriber network, This paper presents the general Internet subscribers network modeling framework using RM-ODP (Reference Model Open Distributed Processing) to manage that network In the form of integrated hierarchy, This paper adopts the object-oriented development methodology with UML (Unified Modeling Language) and designs and implements the HFC network of DOCSIS as an example of the subscriber network.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.365-373
• /
• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.