• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.338-346
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• 1987

This paper describes a method for verifying whether a given geometrical layout correcdtly reflects the original logic level description. The logic description extracted from layout data was directly compadred with the original logic diagram generated at logic level design stage where the logic diagram is represented as a weighted multi-place graph. The comparison is based on graph isomorphism and error messages(error categories and locations)are invoked if any difference is found between the two logic descriptions. An efficient partitioning algorithm which consists of two steps, candidate selection and equal weight partitioning procedure, enables the entire verification process to occur in O(n log n) time.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1687-1692
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• 1991

Burrs, created when metals deform plastically, are by-products of most machining processes. The increasing requirements of precision and reliability in manufacturing processes have led to the development of systems for automated deburring. In this paper the motivations and requirements for automated robotic deburring are discussed. Also, the feasibility of automating the deburring process using fuzzy logic controller is investigated. In implementing the fuzzy logic controller, particular attention is paid to the acoustic emission sensing for the characterization and feedback control of the burr removal process. The results of the experiments reveal the rule based control scheme based on fuzzy logics can be a good alternative to traditional control schemes.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.289-304
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• 2022

It is necessary to apply process algebra and logic in order to analyze and verify safety requirements for Smart IoT Systems due to distributivity and mobility of the systems over some predefined geo-temporal space. However the analysis and verification cannot be fully intuitive over the space due to the fact that the existing process algebra and logic are very limited to express the distributivity and the mobility. In order to overcome the limitations, the paper presents a new logic, namely for GTS-VL (Geo-Temporal Space-Visual Logic), visualization of the analysis and verification over the space. GTS-VL is the first order logic that deals with relations among the different types of blocks over the space, which is the graph that visualizes the system behaviors specified with the existing dTP-Calculus. A tool, called SAVE, was developed over the ADOxx Meta-Modeling Platform in order to demonstrate the feasibility of the approach, and the advantages and practicality of the approach was shown with the comparative analysis of PBC (Producer-Buffer-Consumer) example between the graphical analysis and verification method over the textual method with SAVE tool.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.75.1-75.1
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• 2010

This research presents a fuel cell simulator for control logic verification and operator training. Nowadays, power industries are focusing on clean energy as a response to new policy. The fuel cell can be the solution for clean energy, but operating technology is not well developed compared to other conventional power plans because of its short history. Therefore we need a simulator to verify the new control strategy and train operators, because the price of a real fuel cell system is too high and mechanically weak to be used for these kind of purposes. To develop the simulator, a 300 KW MCFC(Molten Carbonate Fuel Cell) system was modeled with stack, BOPs(pre-reformer, steam generator, etc) and mechanical components(valves, pipes, pumps, blowers, etc). The process model was integrated to emulated control system and HMI(Human Machine Interface). A static load and open loop tests were conducted for verifying the accuracy of the process model, since it is the most important part in the simulation. After verifying the process model, an automatic load change and start-up tests were conducted to verify the performance of a new control strategy(logic and functional loops).

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.1-13
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• 2016

The increase of consumer needs for quality metal cutting related products with more precise tolerances and better product surface roughness has driven the metal cutting industry to continuously improve quality control of metal cutting processes. In this paper, two different approaches are discussed. First, design of experiments (DOE) is used to determine the significant factors and then fuzzy logic approach is presented for the prediction of surface roughness. The data used for the training and checking the fuzzy logic performance is derived from the experiments conducted on a CNC milling machine. In order to obtain better surface roughness, the proper sets of cutting parameters are determined before the process takes place. The factors considered for DOE in the experiment were the depth of cut, feed rate per tooth, cutting speed, tool nose radius, the use of cutting fluid and the three components of the cutting force. Finally the significant factors were used as input factors for fuzzy logic mechanism and surface roughness is predicted with empirical formula developed. Test results show good agreement between the actual process output and the predicted surface roughness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.7-12
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• 2005

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper. The expert system which introduces fuzzy logic in order to process uncertainties is called fuzzy expert system. The fuzzy expert system, however, has a potential problem which may lead to inappropriate results due to the ignorance of some information by applying fuzzy logic in reasoning process in addition to the knowledge acquisition problem. In order to overcome these problems, We construct fuzzy inference network by extending the concept of reasoning network in this paper. In the fuzzy inference network, the propositions which form fuzzy rules are represented by nodes. And these nodes have the truth values representing the belief values of each proposition. The logical operators between propositions of rules are represented by links. And the traditional propagation rule is modified.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.3-12
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• 1995

A controller utilizing fuzzy logic is developed to control the speed of a motor in a washing machine by choosing an appropriate phase. Due to the hardship imposed on obtaining a result from a relation established for inputs, present speed and present rate of speed, and ouput, a phase, of the system that can be tested against an experimental result, it is impossible to apply a genetic algorithm to fine-tune the fuzzy logic controller. To avoid this difficulty, a proper assumption that the parameters of an if-part of a primary fuzzy logic controller have a functional relationship with an error between computed values and experimental ones in made. Setting up of a fuzzy relationship between the parameters and the errors is then achieved through experimentally obtained data. Genetic Algorithm is then applied to this secondary fuzzy logic controller to verify the fuzzy logic. In the verification process, the primary fuzzy logic controller is used in obtaining experimental results. In this way the kind of difficulty in obtaining enough experimental values used to verify the fuzzy logic with genetic algorithm is gotten around. Selection of the parameters that would produce the least error when using the secondary fuzzy logic controller is done with applying genetic algorithm to the then-part of the controller. In doing so the optimal values for the parameters of the if-part of the primary fuzzy logic controller are assumed to be contained. The experimental result presented in the paper validates the assumption.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.3
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• pp.134-142
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• 2005

The implementation of Multiple-Valued Logic(MVL) based on Current-Mode CMOS Logic(CMCL) circuits has recently been achieved. In this paper, four-valued Unary Multiple-Valued logic functions are synthesized using current-mode CMOS logic circuits. We properly make use of the fact that the CMCL addition of logic values represented using discrete current values can be performed at no cost and that negative logic values are readily available via reversing the direction of current flow. A synthesis process for CMCL circuits is based upon a logically complete set of basic elements. Proposed algorithm results in less expensive realization than those achieved using existing techniques in terms of the number of transistors needed. As an alternative to the cost-table techniques Universal Unary Programmable Circuit (UUPC) for a unary function is also proposed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.126-131
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• 2015

Studies on the control of inverted pendulum type systems have been widely reported. This is because this type of system is a typical complex nonlinear system and may be a good model to verify the performance of a proposed control system. In this paper, we propose the design of two fuzzy logic control systems for the control of a Segway mobile robot which is an inverted pendulum type system. We first introduce a dynamic model of the Segway mobile robot and then analyze the system. We then propose the design of the fuzzy logic control system, which shows good performance for the control of any nonlinear system. In this paper, we here design two fuzzy logic control systems for the position and balance control of the Segway mobile robot. We demonstrate their usefulness through simulation examples. We also note the possibility of simplifying the design process and reducing the computational complexity. This possibility is the result of the skew symmetric property of the fuzzy rule tables of the system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.342-345
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• 1995

This paper describes the design process and the experimental results of a fuzzy logic controller to control the tip position of a fixible-link manipulator, directly driven by a AC motor, with a large payload. The joint angle fuzzy logic controller is designed without a costly nonlinear system analysis of the flexible manipulator and the AC motor drive system. The state variables for the fuzzy logic controller are joint angle, joint velocity, link deflection, and link deflection velocity. The simulation and experimental results show that the joint position control is not satisfactory when the controller is designed under the assumption of no link flexibility and that stable joint position control and link vibration suppression can be cahieved with the fuzzy logic controller suggested in this paper.