• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.2-157
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• 2001

This paper describes the synergism of Artificial Neural Network and Fuzzy Logic based approach to improve the reliability of transformer differential protection, the conventional transformer differential protection commonly used a harmonic restraint principle to prevent a tripping from inrush current during initial transformer´s energization but such a principle can not performs the best optimization on tripping time. Furthermore, in some cases there may be false operation such as during CT saturation, high DC offset or harmonic containing in the line. Therefore an artificial neural network and fuzzy logic has been proposed to improve reliability of the transformer protection relay. By using EMTP-ATP the power transformer is modeled, all currents flowing ...

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.4
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• pp.106-115
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• 1994

This paper presents the design of a technology mapping system for optimizing delays of combinational and synchronous sequential logic circuits. The proposed system performs delay optimization for combinational logic circuits by remapping, buffering, and gate merging methods through the correct delay calculation in which the loading values are considered. To get time optimized synchronous sequential circuits, heuristic algorithms are proposed. The proposed algorithms reallocate registers by considering the critical path characteristics. Experimental results show that the proposed system produces a more optimized technology mapping for MCNC benchmarks compared with mis-II.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.28A no.5
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• pp.387-397
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• 1991

This paper presents the design and implementation of a performance-driven logic synthesis system that automatically generates circuits satisfying the given timing and fanout constraints in minimal silicon area. After performing technology independent and dependent optimization, the system identifies and resynthesizes the gates with large loading delay due to excessive fanouts to eliminate the critical path. Experimental results for MCNC benchmark circuits show that proposed system generates the circuits with less delay by up to 20%.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.131-136
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• 1990

In this paper, new methods are proposed which speed up the logical redundancy identification for the gate-level logic optimization. Redundancy indentification, as well as deterministic test pattern generation, can be viewed as a finite space search problem, of which execution time depends on the size of the search space. For the purpose of efficient search, we propose dynamic head line and mandatory assignment. Dynamic head lines are changed dynamically in the process of the redundancy identification. Mandatory assignement can avoid unnecessary assignment. They can reduce the search size efficiently. Especially they can be used even though the circuit is modified in the optimization procedure, that is different from the test pattern generation methods. Some experimental results are presented indicating that the proposed methods are faster than existing methods.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.11-18
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• 2006

The logical analysis of data(LAD) is a Boolean-logic based data mining tool. A critical step in analyzing data by LAD is the pattern generation stage where useful knowledge and hidden structural information in data is discovered in the form of patterns. A conventional method for pattern generation in LAD is based on term enumeration that renders the generation of higher degree patterns practically impossible. In this paper, we present a novel optimization-based pattern generation methodology and propose two mathematical programming models, a mixed 0-1 integer and linear programming (MILP) formulation and a well-studied set covering problem (SCP) formulation for the generation of optimal and heuristic patterns, respectively. With benchmark datasets, we demonstrate the effectiveness of our models by automatically generating with ease patterns of high complexity that cannot be generated with the conventional approach.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.5
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• pp.257-271
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• 2010

As the end of photolithographic integration era is approaching fast, numerous nanoscale devices and systems based on novel nanoscale materials and assembly techniques are recently emerging. Notably, various reconfigurable architectures with considerable promise have been proposed based on nanowire crossbar structure as the primitive building block. Unfortunately, high-density sys-tems consisting of nanometer-scale elements are likely to have numerous physical imperfections and variations. Therefore, defect-tolerance is considered as one of the most exigent challenges in nanowire crossbar systems. In this work, three different defect-avoidant logic mapping algorithms to circumvent defective crosspoints in nanowire reconfigurable crossbar systems are evaluated in terms of various performance metrics. Then, a novel method to find the most cost-effective repair solution is demonstrated by considering all major repair parameters and quantitatively estimating the performance and cost-effectiveness of each algorithm. Extensive parametric simulation results are reported to compare overall repair costs of the repair algorithms under consideration and to validate the cost-driven repair optimization technique.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.1
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• pp.69-78
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• 2011

In this study, an optimization method using multi-objective genetic algorithm(MOGA) has been proposed to develop a fuzzy control algorithm that can effectively control a smart tuned mass damper(TMD). A 76-story benchmark building subjected to wind load was selected as an example structure. The smart TMD consists of 100kN MR damper and the natural period of the smart TMD was tuned to the first mode natural period of the example structure. Damping force of MR damper is controlled to reduce the wind-induced responses of the example structure by a fuzzy logic controller. Two input variables of the fuzzy logic controller are the acceleration of 75th floor and the displacement of the smart TMD and the output variable is the command voltage sent to MR damper. Multi-objective genetic algorithm(NSGA-II) was used for optimization of the fuzzy logic controller and the acceleration of 75th story and the displacement of the smart TMD were used as objective function. After optimization, a series of fuzzy logic controllers which could appropriately reduce both wind responses of the building and smart TMD were obtained. Based on numerical results, it has been shown that the control performance of the smart TMD is much better than that of the passive TMD and it is even better than that of the sample active TMD in some cases.

• The KIPS Transactions:PartA
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• v.15A no.1
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• pp.9-16
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• 2008

This paper presents a new Boolean extraction technique for logic synthesis. This method extracts two-cube Boolean subexpression pairs from each logic expression. It begins by creating two-cube array, which is extended and compressed with complements of two-cube Boolean subexpressions. Next, the compressed two-cube array is analyzed to extract common subexpressions for several logic expressions. The method is greedy and extracts the best common subexpression. Experimental results show the improvements in the literal counts over well-known logic synthesis tools for some benchmark circuits.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.7
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• pp.1374-1383
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• 2001

In the fuzzy control method behaves more robustness than conventional control method, we propose a intelligent control method that membership functions and scaling factor of the fuzzy logic controller are optimized by genetic algorithm under off-line, and then fuzzy logic controller is constructed by the optimization parameters under on-line. In order to verify the usefulness of the proposed control method, we are applied to one link manipulator, and confirmed that the proposed control method is reduced the fuzzy rule base and is the better performance than the conventional fuzzy control method.