• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.1-16
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• 2000

This paper analyzes the approaches of Web-based expert systems by comparing their pros and cons. and proposes a methodology of implementing the Web-based backward inference engines with reduced burden to Web servers. There are several alternatives to implement expert systems under the WWW environment : CGI, Web servers embedding inference engines external viewers Java Applets and HTML. Each of the alternatives have advantages and disadvantages of each own in terms of development and deployment testing scalability portability maintenance and mass service. Especially inference engines implemented using HTML possess relatively large number of advantages compared with those implemented using other techniques. This paper explains the methodology to present rules and variables for backward inference by HTML and JavaScript and suggests a framework for design and development of HTML-based Expert System. A methodology to convert a traditional rule base to an Experts Diagram and then generate a new HTML-based Expert System from the Experts Diagram is also addressed.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.501-517
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• 2014

Binary translation is a restructuring process in order to execute a program targeting a specific device on the other devices. In binary translation, it is very important to generate the translation rules between two devices. There are two methods for generating the translation rules, direct and indirect mapping. The direct mapping is the method for performance, while the indirect mapping is the method for retargetability. This paper suggests a binary translation method based on the direct mapping for the embedded systems. Because, however, the retargetability is also important requirement, we suggest the direct mapping based binary translation with considering the retargetability. In addition, we implement an automatic generation tool for translation rules to prove our concept. Through this method, we can generate the translation rules with considering the performance as well as the retargetability. Furthermore, we can reduce costs for the binary translation.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.77-86
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• 2008

The Web information sources can be regarded as the largest distributed database to the users. By virtually integrating the distributed information sources and regarding them as a single huge database, we can query the database to extract information. This capability is important to develop Web application programs. We have to infer a database schema from browsing-oriented Web documents in order to integrate databases. This paper presents a heuristic algorithm to infer the XML Schema fully automatically from semi-structured Web documents. The algorithm first extracts candidate pattern regions based on predefined structure-making tags, and determines a target pattern region using a few heuristic factors, and then derives XML Schema extraction rules from the target pattern region. The schema extraction rule is represented in XQuery, which makes development of various application systems possible using open standard XML tools. We also present the experimental results for several public web sources to show the effectiveness of the algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.69-76
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• 2008

This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.3
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• pp.241-264
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• 2022

Bibliographic metadata can help researchers effectively utilize essential publications that they need and grasp academic trends of their own fields. With the manual creation of the metadata costly and time-consuming. it is nontrivial to effectively automatize the metadata construction using rule-based methods due to the immoderate variety of the article forms and styles according to publishers and academic societies. Therefore, this study proposes a two-step extraction process based on rules and deep neural networks for generating bibliographic metadata of scientific articlles to overcome the difficulties above. The extraction target areas in articles were identified by using a deep neural network-based model, and then the details in the areas were analyzed and sub-divided into relevant metadata elements. IThe proposed model also includes a model for generating reference summary information, which is able to separate the end of the text and the starting point of a reference, and to extract individual references by essential rule set, and to identify all the bibliographic items in each reference by a deep neural network. In addition, in order to confirm the possibility of a model that generates the bibliographic information of academic papers without pre- and post-processing, we conducted an in-depth comparative experiment with various settings and configurations. As a result of the experiment, the method proposed in this paper showed higher performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.43-54
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• 1997

Knowledge acquisition is one of the most difficult problems in designing fuzzy systems. As application domains of fuzzy systems become larger and more complex, it is more difficult to find the relations among the system's input- outpiit variables. Moreover, it takes a lot of efforts to formulate expert's knowledge about complex systems' control actions by linguistic variables. Another difficulty is to define and adjust membership functions properly. Soin conventional fuzzy systems, the membership functions should be adjusted to improve the system performance. This is time-consuming process. In this paper, we suggest a new approach to design a fuzzy system. We design a fuzzy system using two neural networks, Kohonen neural network and backpropagation neural network, which generate fuzzy rules automatically and construct inference network. Since fuzzy inference is performed based on fuzzy relation in this approach, we don't need the membership functions of each variable. Therefore it is unnecessary to define and adjust membership functions and we can get fuzzy rules automatically. The design process of fuzzy system becomes simple. The proposed approach is applied to a simulated automatic car speed control system. We can be sure that this approach not only makes the design process of fuzzy systems simple but also produces appropriate inference results.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.121-145
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• 2010

University timetabling depending on the educational environments of universities is an NP-hard problem that the amount of computation required to find solutions increases exponentially with the problem size. For many years, there have been lots of studies on university timetabling from the necessity of automatic timetable generation for students' convenience and effective lesson, and for the effective allocation of subjects, lecturers, and classrooms. Timetables are classified into a course timetable and an examination timetable. This study focuses on the former. In general, a course timetable for liberal arts is scheduled by the office of academic affairs and a course timetable for major subjects is scheduled by each department of a university. We found several problems from the analysis of current course timetabling in departments. First, it is time-consuming and inefficient for each department to do the routine and repetitive timetabling work manually. Second, many classes are concentrated into several time slots in a timetable. This tendency decreases the effectiveness of students' classes. Third, several major subjects might overlap some required subjects in liberal arts at the same time slots in the timetable. In this case, it is required that students should choose only one from the overlapped subjects. Fourth, many subjects are lectured by same lecturers every year and most of lecturers prefer the same time slots for the subjects compared with last year. This means that it will be helpful if departments reuse the previous timetables. To solve such problems and support the effective course timetabling in each department, this study proposes a university timetabling support system based on two phases. In the first phase, each department generates a timetable template from the most similar timetable case, which is based on case-based reasoning. In the second phase, the department schedules a timetable with the help of interactive user interface under the timetabling criteria, which is based on rule-based approach. This study provides the illustrations of Hanshin University. We classified timetabling criteria into intrinsic and extrinsic criteria. In intrinsic criteria, there are three criteria related to lecturer, class, and classroom which are all hard constraints. In extrinsic criteria, there are four criteria related to 'the numbers of lesson hours' by the lecturer, 'prohibition of lecture allocation to specific day-hours' for committee members, 'the number of subjects in the same day-hour,' and 'the use of common classrooms.' In 'the numbers of lesson hours' by the lecturer, there are three kinds of criteria : 'minimum number of lesson hours per week,' 'maximum number of lesson hours per week,' 'maximum number of lesson hours per day.' Extrinsic criteria are also all hard constraints except for 'minimum number of lesson hours per week' considered as a soft constraint. In addition, we proposed two indices for measuring similarities between subjects of current semester and subjects of the previous timetables, and for evaluating distribution degrees of a scheduled timetable. Similarity is measured by comparison of two attributes-subject name and its lecturer-between current semester and a previous semester. The index of distribution degree, based on information entropy, indicates a distribution of subjects in the timetable. To show this study's viability, we implemented a prototype system and performed experiments with the real data of Hanshin University. Average similarity from the most similar cases of all departments was estimated as 41.72%. It means that a timetable template generated from the most similar case will be helpful. Through sensitivity analysis, the result shows that distribution degree will increase if we set 'the number of subjects in the same day-hour' to more than 90%.