• Journal of KIBIM
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• v.12 no.1
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• pp.42-54
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• 2022

BIM facilitates data transparency and consistency through three-dimensional parametric modeling and promotes the accurate managing and sharing of project information. In Korea, however, BIM-based detailed design of architectural interior finishes required during the Construction Documents phase increases the burden on architectural firms due to frequent design changes and manual workload. Therefore, the purpose of this study was to establish rule-based modules using parametric modeling that automates repetitive tasks that occur during the detailed design of interior finishing. Interviews with practitioners were conducted to analyze the major finishing elements. Of these floors, walls, and ceilings, which were the most rudimentary and common items, were selected as the subjects of the study. The modules developed in this study have two functions. One is to create new finish types, and the other is the automatic modeling of new types into rooms. For these functions, parameters that belonged to each finish and room element in a BIM model were analyzed and valid parameters directly used for parametric modeling were derived. Then, based on these parameters, rule-based modules for three elements, I.e., floors, walls, and ceilings were constructed with Revit Dynamo, and the effectiveness of the modules was verified with a pilot test. In conclusion, this study suggested a series of processes for automatic finishing to improve the efficiency of BIM-based architectural detailed design of finishes and to contribute in solving the chronic problems occuring during current design processes.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.155-162
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• 1999

reliability from components reliability. In this case, it assumes that components failure is mutually independent, but it may not true in real systems. In this study, the mean cost per unit time is computed as the ratio of mean life to the mean cost. The mean life is obtained by the reliability function under power rule model. The mean cost is obtained by the mathematical model based on the inspection interval. A heuristic method is proposed to determine the optimal number of redundant units and the optimal inspection interval to minimize the mean cost per unit time. The assumptions of this study are as following : First, in the load-sharing k-out-of-n:G system, total loads are applied to the system and shared by the operating components. Secondly, the number of failed components affects the failure rate of surviving components as a function of the total load applied. Finally, the relation between the load and the failure rate of surviving components is set by the power rule model. For the practical application of the above methods, numerical examples are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1254-1259
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.911-914
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• 1993

This paper makes a trial to build the model of car-following in the state of starting to stable driving on the basic of driver's knowledge that is easily characterized by linguistical cognition. There are three main steps in building the model. Firstly, each driver's rule of three testees is studied in linguistical experssion by the interview and questionary surveys that are repeated once a day for ten days. Secondly, quantification of the linguistical expression is investigated by driving experiments that includes the questionary survey to the testee in the test vehicle, and the membership functions of variables of rule are obtained. Thirdly, implicaton and composition of fuzzy inference is made by Max-Min Methods and defuzzification by gravity method. It can be said that the proposed model of car-following based on driver's knowledge is practically allpicable to the estimation of drivering of car-following on trunk roads in urban area.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.831-840
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• 1992

This paper presents an efficient short-term generation scheduling method using a rule-based expert/consulting system approach to assist electric energy system operators and planners. The expert system approach is applied to improve the Dynamic Programming(DP) based generation scheduling algorithm. In the selection procedure of the feasible combinations of generating units at each stage, automatic consulting on the manipulation of several constraints such as the minimum up time, the minimum down time and the maximum running time constraints of generating units will be performed by the expert/consulting system. In order to maximize the solution feasibility, the aforementioned constraints are controlled by a rule-based expert system, that is, instead of imposing penalty cost to those constraint violated combinations, which sometimes may become the very reason of no existing solution, several constraints will be manipulated within their flexibilities using the rules and facts that are established by domain experts. In this paper, for the purpose of implementing the consulting of several constraints during the dynamic process of generation scheduling, an expert system named STGSCS is developed. As a building tool of the expert system, C Language Integrated Production System(CLIPS) is used. The effectiveness of the proposed algorithm has been demonstrated by applying it to a model electric energy system.

• Journal of Auto-vehicle Safety Association
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• v.13 no.1
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• pp.14-25
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• 2021

This paper presents a RLS based adaptive sliding mode observer (A-SMO) for unknown fault reconstruction in longitudinal autonomous driving. Securing the functional safety of autonomous vehicles from unexpected faults of sensors is essential for avoidance of fatal accidents. Because the magnitude and type of the faults cannot be known exactly, the RLS based A-SMO for unknown acceleration fault reconstruction has been designed with relationship function in this study. It is assumed that longitudinal acceleration of preceding vehicle can be obtained by using the V2V (Vehicle to Vehicle) communication. The kinematic model that represents relative relation between subject and preceding vehicles has been used for fault reconstruction. In order to reconstruct fault signal in acceleration, the magnitude of the injection term has been adjusted by adaptation rule designed based on MIT rule. The proposed A-SMO in this study was developed in Matlab/Simulink environment. Performance evaluation has been conducted using the commercial software (CarMaker) with car-following scenario and evaluation results show that maximum reconstruction error ratios exist within range of ±10%.

• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.493-506
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• 2008

In this paper we suggest dynamic access control model based on context satisfied with requirement of u-healthcare environment through researching the role based access control model. For the dynamic security domain management, we used a distributed object group framework and context information for dynamic access control used the constructed database. We defined decision rule by knowledge reduction in decision making table, and applied this rule in our model as a rough set theory. We showed the executed results of context based dynamic security service through u-healthcare application which is based on distributed object group framework. As a result, our dynamic access control model provides an appropriate security service according to security domain, more flexible access control in u-healthcare environment.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.497-504
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• 2002

This paper proposes fault diagnosis and correction algorithms using agent collaboration, and a management model for managing network configuration faults. This management model is composed of three processes-fault detection, fault diagnosis and fault correction. Each process, based on RBR, operates on using rules which are consisted in Rule-based Knowledge Database. Proposed algorithm selves the complex fault problem that a system could not work out by itself, using agent collaboration. And the algorithm does efficiently diagnose and correct network configuration faults in abnormal network states.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.352-360
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• 2023

Recently, AI pilots using reinforcement learning are developing to a level that is more flexible than rule-based methods and can replace human pilots. In this paper, a curriculum was used to help head-on combat with reinforcement learning. It is not easy to learn head-on with a reinforcement learning method without a curriculum, but in this paper, through the two circle-based head-on air combat learning technique, ownship gradually increase the difficulty and become good at head-on combat. On the two-circle, the ATA angle between the ownship and target gradually increased and the AA angle gradually decreased while learning was conducted. By performing reinforcement learning with and w/o curriculum, it was engaged with the rule-based model. And as the win ratio of the curriculum based model increased to close to 100 %, it was confirmed that the performance was superior.