• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2270-2273
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• 2003

Fuzzy logic control has been widely implemented in air conditioning and ventilation systems which has uncertainty or high robust system. Since the dynamic behaviors of the systems contain complexity and uncertainty in its parameters , several fuzzy logic controllers had been implemented to control room temperature in the field of air conditioning system. In this paper, the fuzzy logic control has been developed to control room temperature and humidity in the precision air conditioning systems. The nonlinear mathematical model was formulated using energy and continuity equations. MATLAB was used to simulate the fuzzy logic control of the multi-variable air conditioning systems. The simulation results show that fuzzy logic controller can reduce the steady-state errors of the room temperature and relative humidity in multivariable air conditioning systems. The offset are less than 0.5 degree Celsius and 3 percent in relative humidity respectively under random step disturbance in heating load and moisture load respectively

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.49-53
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• 2008

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.111-118
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• 2001

In this paper, in order to implement the multi-layered perceptron neural network using pure digital logic circuit model, we propose the new logic neuron structure, the digital canonical multi-layered logic neural network structure, and the multi-stage multi-layered logic neural network structure for pattern recognition applications. And we show that the proposed approach provides an incremental additive learning algorithm, which is very simple and effective.

• Journal of Educational Research in Mathematics
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• v.9 no.1
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• pp.111-119
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• 1999

This paper discusses jLogic, a mathematical logic education software developed by the author. jLogic is basically a MS-Windows based software that can construct first-order models, formulas and thet their satisfiablity. Logical formulas are easily input by a "keyboard" maintained by jLogic. A special finite model, called the "Toy World" can be visually cinstructed and modified. The user is supposed to answer the following 3 questions about the selected logical expression: 1. Is it a grammatically correct logical formula? 2. Is it a sentence that has a definite truth value? 3. Is th sentence true or false? When the user inputs his answer in the "Inspector window" and then presses the OK button, jLogic instantly tests the validity of the answer and tells the user the result. jLogic is freely downloaded from http://gauss.kyungpook.ac.kr/~jlogic/

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.834-841
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• 2006

Recently, many enterprises are installing AMSs(Automated Manufacturing Systems) for their competitive advantages. As the level of automation increases, proper design and validation of control logic is a imperative task for the successful operation of AMSs. However, current discrete event simulation methods mainly focus on the performance evaluation. As a result, they lack the modeling capabilities for the detail logic of automated manufacturing system controller. Proposed in this paper is a method of validation of the controller logic for automated material handling system using an object-oriented design and simulation. Using this method, FA engineers can validate the controller logic easily in earlier stage of system design, so they can reduce the time for correcting the logic errors and enhance the productivity of control program development Generated simulation model can also be used as a communication tool among FA engineers who have different experiences and disciplines.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.65-70
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• 2005

This paper introduces an approach to multi-layered product knowledge model for collaborative engineering environment. The participants in collaborative engineering want to share and reason product knowledge through internet without any heterogeneity and ambiguity. However the previous knowledge models are limited in providing those aspects. In this paper, the collaborative engineering domain is analyzed and then the product knowledge is organized into four levels such as product context model, product specific model, product design model and product manufacturing model. The four levels are represented by first-order logic in layered fashion. The concepts and the instances of a formal ontology are used for recursive representation of the four levels. The instances of the concepts of an upper level like product context model are considered as the concepts of an adjacent lower level like product specific model, and this mechanism is applied to the other levels. These logic representations are integrated with the schema and the instances of a relational database. OWL representation of the four levels is defined through the integration of the logic representation and OWL primitives. The four product knowledge models have their major representation according to the characteristics of each model. This approach enables engineer to share product knowledge through internet without any ambiguity and utilize it as basis for additional reasoning.

• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.77-107
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• 1995

This paper documents the development of a fuzzy logic based incident detection model for urban diamond interchanges. Research in incident detection for intersections and arterials is at a very initial stage. Existing algorithms are still far from being robust in dealing with the difficulties related with data availability and the multi-dimensional nature of the incident detection problem. The purpose of this study is to develop a new real-time incident detection model for urban diamond interchanges. The development of the algorithm is based on fuzzy logic. The incident detection model developed through this research is capable of detecting lane¬blocking incidents when their effects are manifested by certain patterns of deterioration in traffic conditions and, thereby, adjustments in signal control strategies are required. The model overcomes the boundary condition problem inherent in conventional threshold-based concepts. The model captures system-wide incident effects utilizing multiple measures for more accurate and reliable detection, and serves as a component module of a real-time traffic adaptive diamond interchange control system. The model is designed to be readily scalable and expandable for larger systems of arterial streets. The prototype incident detection model was applied to an actual diamond interchange to investigate its performance. A simulation study was performed to evaluate the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy logic based approach to incident detection is promising.

• Journal of architectural history
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• v.6 no.3 s.13
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• pp.45-59
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• 1997

One of the main purposes of architectural history is the analysis of existing designs in order to find laws and orders of certain types, while space planning emphasizes the generation of design. In this study, relational space planning methodology is used to generate Chosun upper-class housing prototypes based on 'Yaejae' logic model. During the Chosun Dynasty era in Korea, Confucianism was the ruling ideology for its society. The patio type house was the main upper-class housing type during the Chosun Dynasty, and it can be viewed that space planning was heavily influenced by the law of 'Yaejae' in Confucianism. The logic of 'Yaejae' can be interpreted as relationships between spaces. Relational space planning methodology that reasons through constraint propagation is used to generate prototypes. Prototypes are compared in order to verify actual applications of the logic into space planning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.781-784
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• 1993

Most linguistic models of processes or plants known are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show two models for synchronous finite state machines (FSM) based on fuzzy logic, namely the Crisp-State-Fuzzy-Output (CSFO FSM) and Fuzzy-State-Fuzzy Output (FSFO FSM). As a result of the introduction of the FSM models, the improved architectures for fuzzy logic controller have been defined. These architectures featuring pipelined intelligent fuzzy controller are discussed in terms of dimensionality of the model. VLSI integrated circuit implementation issues of the fuzzy logic controller are also considered. The presented approach can be utilized for fuzzy controller hardware accelerators intended to work in the real-time environment.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.103-109
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• 2001

Soft sensor is an effective method to deal with the estimation of variables, which are difficult to measure because of the reasons of economy or technology. Fuzzy logic system can be used to develop the soft sensor model by infinite rules, but the fuzzy dividing of variable sets is a key problem to achieve an accurate fuzzy logic model, In this paper, we proposed a new method to develop soft sensor model based on fuzzy neural network. First, using a novel method to divide the variable fuzzy sets by the process input and output data. Second, developing the fuzzy logic model based on that fuzzy set dividing. After that, expressing the fuzzy system with a fuzzy neural network and getting the initial soft sensor model based FNN. Last, adjusting the relative parameters of soft sensor model by the BP learning method. The effectiveness of the method proposed and the preferable generalization ability of soft sensor model built are demonstrated by the simulation.