• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.571-578
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• 2012

In this paper, we propose two main points. The first is the constraint for logic synthesis, and the second is an efficient logic synthesis method. Logic synthesis is a process to obtain the gate-level netlist from RTL (register transfer level) codes using logic mapping and optimization with the specified constraints. The result of logic synthesis is tightly dependent on constraint and logic synthesis method. Since the size and timing can be dramatically changed by these, we should precisely consider them. In this paper, we present the considering items in the process of logic synthesis by using our experience and experimental results. The proposed techniques was applied to a circuit with the hardware resource of about 650K gates. The synthesis time for the hybrid method was reduced by 47% comparing the bottom-up method and It has better timing property about slack than top-down method.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.881-888
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• 2017

Logic design using multiplexer has been used as a useful method for design convenience and flexibility in structural digital system design. In this paper, we analyze the effect of don't care conditions on logic optimization in a multiplexer-based logic design, which was not discussed enough in the previous studies in multiplexer based logic design, and describe the use of don't care conditions for designing of a single multiplexer and multiple multiplexer-based logic design. Especially, the design method when the number of data input is not 2m (as the number of selection lines is m) is considered. We also describe how to apply the proposed technique to the digital logic design education in conjunction with microprocessor design using Arduino which is widely used in creative engineering education recently.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.65-67
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• 1996

This paper presents the automatic construction and parameter optimization technique for the fuzzy logic controller using genetic algorithm. In general the design of fuzzy controller has difficulties in the acquisition of expert's knowledge and relies to a great extent on empirical and heuristic knowledge which, in many cases, cannot be objectively justified. Therefor the performance of the controller can be degraded in the case of plant parameter variations or unpredictable incident which the designer may lave ignored. And fuzzy logic controller parameters elicited form the expert may not be global. Some of these problems can be resolved by application of genetic algorithm. Finally, we provides the second order dead time plant to evaluate the feasibility and generality of our proposed method. Comparison shows that the proposed method can produce a fuzzy logic controller with higher accuracy and a smaller number of fuzzy roles than manually billed fuzzy logic controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.225-228
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• 2002

When the fuzzy logic controller (FLC), which is designed based on the plant model, is applied to the real control system, satisfactory control performance may not be attained due to modeling errors from the plant model. In such cases, the control parameters of the controller must be adjusted to enhance control performance. Until now, the trial and error method has been used, consuming much time and effort. To resolve such problem, response surface methodology (RSM), a new method of adjusting the control parameters of the controller, is suggested. This method is more systematic than the previous trial and error method, and thus optimal solutions can be provided with less tuning. First, the initial values of the control parameters were determined through the plant model and the optimization algorithm. Then, designed experiments were performed in the region around the initial values, determining the optimal values of the control parameters which satisfy both the rise time and overshoot simultaneously.

• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.528-532
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• 2017

The Theo Jansen mechanism using 1 degree of freedom is special system of walking robot. The trajectory made by the point of ground position is similar to other walking robot using many degrees of freedom. Because of diversity of design parameter of the Jansen mechanism, it makes a lot of trajectory and takes possibilities of optimization. However this research doesn't focus on the optimization of trajectory, but it focused on comprehensive design of the robot using well-known trajectory and line tracer logic to go fast and accurate along the line. The logic to follow a line has many kinds of possibility of algorithm. To eliminate uncertainty about recognizing a line, I divide the case of line following situation and make optimized logic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1692-1711
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• 2016

Energy is a scarce resource in wireless sensor networks (WSNs). A variety of clustering protocols for WSNs, such as the zone-based stable election protocol-enhanced (ZSEP-E), have been developed for energy optimization. The ZSEP-E is a heterogeneous zone-based clustering protocol that focuses on unbalanced energy consumption with parallel formation of clusters in zones and election of cluster heads (CHs). Most ZSEP-E research has assumed probabilistic election of CHs in the zones by considering the maximum residual energy of nodes. However, studies of the diverse CH election parameters are lacking. We investigated the performance of the ZSEP-E in such scenarios using a fuzzy logic approach based on three descriptors, i.e., energy, density, and the distance from the node to the base station. We proposed an efficient ZSEP-E scheme to adapt and elect CHs in zones using fuzzy variables and evaluated its performance for different energy levels in the zones.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.93-101
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• 2022

Bioheavy oil, which is expanding its range of use as an alternative fuel to reduce environmental pollutant emissions, has a lot of difficulty in combustion due to its low emission of pollutants such as nitrogen oxide (NOx) and sulfur oxide (SOx), while its low dissipation and high oxygen content in fuel. many studies have been conducted on change in characteristics by mixing rate combustion characteristics and combustion reactions, but there have been no specific and effective studies on the composition of control system, optimization of control, development of logic for mixing and burning, minimizing environmental pollutantants discharge. In this study, we intend to consider systemmatic and empirical considerations on the composition, logic development, solve the problem of manual switching of bioler master due to excessive oxygen content and tuning of the control system for optimal combustion of bioheavy oil.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.192-194
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• 1996

This paper propose an optimal scaling gain tuning method of the fuzzy PI controller using Genetic Algorithm(GA). Scaling gains can reflect the control resolution and fuzziness of input/output variables. By the scaling gain method, the design of a fuzzy logic controller(FLC) can be simplified without affecting the system performance in comparison with multi-decision table method. In designing a fuzzy logic controller, the analytic approach method for the optimization is unavailable. Therefore GA is excellent optimization algorithms for scaling gain tuning. Using this optimal scaling gain tuning method, a good performance can be achieved both in transient and steady state.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.209-218
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• 2012

Recently, the Field Programmable Stateful Logic Array (FPSLA) was proposed as one of the most promising system integration technologies which will extend the life of the Moore's law. This work is the first proposal of the FPSLA design automation flow, and the approaches to logic synthesis, synchronization, physical mapping, and automatic placement of the FPSLA designs. The synchronization at each gate for pipelining determines the x-coordinates of cells, and reduces the placement to 1-dimensional problems. The objective function and its gradients for the non-linear optimization of the net length and placement density have been remodeled for the reduced global placement problem. Also, a recursive algorithm has been proposed to legalize the placement by relaxing the density overflow of bipartite bin groups in a top-down hierarchical fashion. The proposed model and algorithm are implemented, and validated by applying them to the ACM/SIGDA benchmark designs. The output state of a gate in an FPSLA needs to be duplicated so that each fanout gate can be connected to a dedicated copy. This property has been taken into account by merging the duplicated nets into a hyperedge, and then, splitting the hyperedge into edges as the optimization progresses. This yields additional 18.4% of the cell count reduction in the most dense logic stage. The practicality of the FPSLA can be further enhanced primarily by incorporating into the logic synthesis the constraint to avoid the concentrated fains of gates on some logic stages. In addition, an efficient algorithm needs to be devised for the routing problem which is based on a complicated graph. The graph models the nanowire crossbar which is trimmed to be embedded into the FPSLA fabric, and therefore, asymmetric. These CAD tools can be used to evaluate the fabric efficiency during the architecture enhancement as well as automate the design.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.48-57
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• 1997

This paper presents the automatic construction and parameter optimization technique for fuzzy logic controllers using genetic algorithm. In general. the design of fuzzy logic controllers has difficulties in the acq~lisition of expert's knowledge and relies to a great extent on empirical and heuristic knowledge which, in many cases, cannot be objectively justified. So, the performance of the controllers c:an be degraded in the case of plant parameter variations or unpredictable incident which a designer may have ignored, and the parameters of fuzzy logic controllers obtained by expert's control action may not be optirnal. Some of these problems can be resolved by the use of genetic algorithm. The proposed method can tune the parameters of fuzzy logic controllers including scaling factors and determine: the appropriate number of fuzzy rulcs systematically. Finally, we provides the second order dead time plant to evaluate the feasibility and generality of the proposed method. Comparison shows that the proposed method can produce fuzzy logic controllers with higher accuracy and a smaller number of fuzzy rules than manually tuned fuzzy logic controllers.