• Journal of the Korean School Mathematics Society
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• v.4 no.2
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• pp.135-142
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• 2001

The selected questions for this study was their conversation in problem solving way of working together. To achieve its purpose researcher I chose more detail questions for this study as follows. $\circled1$ What is the difference of strategy according to its level \ulcorner $\circled2$ What is the mathematical ability difference in problem solving process concerning its level \ulcorner This is the result of the study $\circled1$ Difference in the strategy of each class of students. High class-high class students found rules with trial and error strategy, simplified them and restated them in uncertain framed problems, and write a formula with recalling their theorem and definition and solved them. High class-middle class students' knowledge and understanding of the problem, yet middle class students tended to rely on high class students' problem solving ability, using trial and error strategy. However, middle class-middle class students had difficulties in finding rules to solve the problem and relied upon guessing the answers through illogical way instead of using the strategy of writing a formula. $\circled2$ Mathematical ability difference in problem solving process of each class. There was not much difference between high class-high class and high class-middle class, but with middle class-middle class was very distinctive. High class-high class students were quick in understanding and they chose the right strategy to solve the problem High class-middle class students tried to solve the problem based upon the high class students' ideas and were better than middle class-middle class students in calculating ability to solve the problem. High class-high class students took the process of resection to make the answer, but high class-middle class students relied on high class students' guessing to reconsider other ways of problem-solving. Middle class-middle class students made variables, without knowing how to use them, and solved the problem illogically. Also the accuracy was relatively low and they had difficulties in understanding the definition.

• Information Systems Review
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• v.12 no.1
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• pp.131-144
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• 2010

To survive in the rapidly changing business environment, it is very important for companies to respond to the changing environment effectively as well as agilely. As an approach to appropriately respond to the changing environment, companies have developed and exploited various business rules and related knowledge and attempted to implement them through information systems. However, most of legacy information systems used in companies have suffered from the limitations that do not properly utilize and systematically organize the business rules. This study proposes an introduction of BRE (business rule engine) as a solution to cope with the limitations and explores its effect on organizational agility based the case analysis of "W" Investment and securities Co., Ltd.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.1
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• pp.109-115
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• 2010

This paper deals with trajectory control of computer simulated mobile robot via fuzzy control. Mobile robot is controlled by Mamdani type fuzzy controller. Inputs of the fuzzy controller are angle between mobil robot and target, changed angle and output is the steering angle, which is control input. Fuzzy rules have seven rules and are selected by human experiential knowledge. Also we propose a scaling factors tuning scheme which is the another focus in designing fuzzy controller. In this paper, we adapt the RCGA which is well known in parameter optimization to adjust scaling factors. The simulation results show that the fuzzy control effectively realize trajectory stabilization of the mobile robot along a given reference target from various initial steering angles.

• Explosives and Blasting
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• v.22 no.1
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• pp.45-56
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• 2004

The industrial explosives in Korea have been made and used since 1952. The blasting accidents have increased gradually with the use of explosives. Based on the Police Department and Guns & Explosives Safety Technology Association's researching materials, the blasting accidents between 1988 and 1997 have been investigated and analyzed in this paper. FTA method was applied for the analysis of the blasting accidents which occurred in the tunnels, roads, subways, and various kinds of building construction area. The results show that the majority of the accidents, about 45.7% of the total, are due to the fly rocks. It is similar trend in Japan. The FTA analysis performed on the accidents by fly rocks shows that the major source of the accident is human factors such as non-observance of the safety rules, less knowledge of the safety and so on. The results of the study ate expected to provide basic data for making and observing the safety rules, making and amending the laws concerned and planning the security project. It will be helpful in preventing the blasting accidents and in reducing the loss of valuable lives and the financial damage.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.51
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• pp.465-495
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• 2011

Domestic franchised businesses have been showing relatively fast growth, but the growth is expected to slow down as in those developed countries. In face of this changing market environment, domestic franchisers will have to turn their eyes abroad to achieve sustainable growth. On the other hand, more international franchisors could pursue expanding into the Korean market due to economic or strategic reasons in their home countries. In general, enterprises are faced with several barriers when entering foreign markets by franchising their operation. Issues relating to such entry barriers can be broadly classified into legal and managerial. To begin, international franchising necessitates enterprises to handle various aspects of legal issues. There are no internationally unified rules for franchise agreements as in international goods purchase contracts. This forces franchisors to have deep knowledge of concerned regulations and practices of each of the individual target countries, in particular franchising practices which differ from those of their own countries in terms of rights and obligations of the involved parties. Having regard to this situation, this study reviewed the EU's PEL CAFDC and other domestic and overseas regulations governing franchising. From the results, several contractual obligations were derived that need to be taken into account when handling the issues around the international franchise agreement. In closing this paper mainly having in mind enterprises in various business lines seeking to expand into international franchising, some unmet needs are worth commenting. First, there is an urgent need to establish practical guidelines along with the model agreement addressing the issues of international franchising in the absence of any unified international rules. Second, to meet the first need above, it is needed that the relevant authorities conduct a comprehensive review of the existing franchising regulations available across overseas countries and, based on the results, embark on gathering good common elements in the existing franchising regulations in individual countries, ultimately developing the best possible guidelines and examples.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.

• Journal of Advanced Navigation Technology
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• v.2 no.1
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• pp.61-67
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• 1998

In this paper, we have described the Ramp Activity Coordination Expert System (RACES) which can solve aircraft parking problems. RACES includes a knowledge-based scheduling problem which assigns every daily arriving and departing flight to the gates and remote spots with the domain specific knowledge and heuristics acquired from human experts. RACES processes complex scheduling problem such as dynamic inter-relations among the characteristics of remote spots/gates and aircraft with various other constraints, for example, custome and ground handling factors at an airport. By user-driven modeling for end users and knowledge-driven near optimal scheduling acquired from human experts, RACES can produce parking schedules of aircraft in about 20 seconds for about 400 daily flights, whereas it normally takes about 4 to 5 hours by human experts. Scheduling results in the form of Gantt charts produced by the RACES are also accepted by the domain experts. RACES is also designed to deal with the partial adjustment of the schedule when unexpected events occur. After daily scheduling is completed, the messages for aircraft changes and delay messages are reflected and updated into the schedule according to the knowledge of the domain experts. By analyzing the knowledge model of the domain expert, the reactive scheduling steps are effectively represented as rules and the scenarios of the Graphic User Interfaces (GUI) are designed. Since the modification of the aircraft dispositions such as aircraft changes and cancellations of flights are reflected to the current schedule, the modification should be notified to RACES from the mainframe for the reactive scheduling. The adjustments of the schedule are made semi-automatically by RACES since there are many irregularities in dealing with the partial rescheduling.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• 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.17 no.3
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• pp.169-185
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• 2011

Data redundancy is problematic in that it not only induces heavy storage management cost but also could bring critical degradation of information systems. Unfortunately, to our knowledge, only few enterprises willingly afford time and efforts for the faithful conceptual design to prevent the degree of inappropriate data as much as they could, while most of enterprises pay rare attention to the notion of that sort of data quality. Wondering if there would be any other way to design the enterprise.wide data design without prior knowledge about business works is our major motivation for this study. In this paper, we present our data modeling methodology in which associations among objects in each sentences of a business job descriptions are treated as the focal point in database design. A proposed agent for automated design tool simply takes a business job description written in natural language as an input, and then designs an entity relationship diagram with some smart rules. We introduce the scope of the proposed agent and its detailed logics with several examples. And then, we verify the appropriateness of the resulted associations among objects. Lastly, we perform case studies to evaluate the devised agent's applicability to a business field.