• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.173-181
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• 2017

As the industries have developed, a myriad of big data have been produced and the inherent uncertainty in the data has also increased accordingly. In this paper, we propose an interval type-2 fuzzy clustering method to deal with the inherent uncertainty in the data and, using this method, design and optimize the fuzzy neural network. Fuzzy rules using the proposed clustering method are designed and carried out the learning process. Genetic algorithms are used as an optimization method and the model parameters are optimally explored. Experiments were performed with two pattern classification, both of the experiments show the superior pattern recognition results. The proposed network will be able to provide a way to deal with the uncertainty increasing.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.8
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• pp.462-467
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• 2009

Researches and developments on BEMS are performed world-widely through sustainable management in various conditions. However, there are many obstacles to adapt the system in existing buildings because it needs highly expensive equipments, which are designed for newly built buildings, to install. Therefore, there are numerous limits exist when applying the BEMS in established buildings. The purpose of this study estimates the optimization of RF output power in WSN(Wireless Sensor Networks), which is the essential technology to develop PEMS. The results of this study is as follows ; 1) Applying WSN technique in buildings was possible. 2) As RF output power increases, the number of relay node reduced, therefore, the WSN showed more stability. 3) When estimating optimal RF output power in school, it should be considered between the number of relay node and RF output power. 4) Considering battery consumption and possibility of reception, the best suited RF output power is -20dbm in apartment house.

• Journal of Sensor Science and Technology
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• v.17 no.1
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• pp.15-22
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• 2008

When designing image sensors including a CMOS vision chip for edge detection, resolution is a significant factor to evaluate the performance. It is hard to improve the resolution of a bio-inspired CMOS vision using a resistive network because the vision chip contains many circuits such as a resistive network and several signal processing circuits as well as photocircuits of general image sensors such as CMOS image sensor (CIS). Low resolution restricts the use of the application systems. In this paper, we improve the resolution through layout and circuit optimization. Furthermore, we have designed a printed circuit board using FPGA which controls the vision chip. The vision chip for edge detection has been designed and fabricated by using $0.35{\mu}m$ double-poly four-metal CMOS technology, and its output characteristics have been investigated.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.4
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• pp.17-29
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• 1999

This paper presents a high level synthesis method targeting low power consumption for data-dominated CMOS circuits (e.g., DSP). The high level synthesis is divided into three basic tasks: scheduling, resource and register allocation. For lower power scheduling, we increase the possibility of reusing an input operand of functional units. For a scheduled data flow graph, a compatibility graph for register and resource allocation is formed, and then a special weighted network is then constructed from the compatibility graph and the minimum cost flow algorithm is performed on the network to obtain the minimum power consumption data path assignment. The formulated problem is then solved optimally in polynomial time. This method reduces both the switching activity and the capacitance in synthesized data path. Experimental results show 15% power reduction in benchmark circuits.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.6
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• pp.51-57
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• 1980

Any switching function can be constructed with universal building block of MAND gate. Threelevel AND-NOT logic networks with only true inputs are called TANT networks. Systematic approach to TANT minimization starts from the UF type minterm with the smallest subscript and ends when UF type minterms are all covered. Optinal PEI is composed of CPPI or EPPi without C-C table. The algorithm in this work is usful in solving TANT optimization porblem of four or five variables by hand solution. When variable are six or more, it is required to be solved by computer, A CAD software package of this algorithm with FORTRAN IV language is made to solve such problems.

• Structural Engineering and Mechanics
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• v.46 no.5
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• pp.673-693
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• 2013

This paper presents a systematic design and training procedure for the feed-forward back-propagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output, an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where the observed modeling errors for the inverse model can be related to knocking effects in the measured force due to the bearing plays between hydraulic piston and MR damper rod. Finally, the validated models are used to emulate pure viscous damping. Comparison of numerical and experimental results demonstrates good agreement in the post-yield region of the MR damper, while the main error of the inverse NN occurs in the pre-yield region where the inverse NN overestimates the current to track the desired viscous force.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.286-290
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• 2011

This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network(ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location for the installation of the offshore wind jam, and to evaluate the wind resources of the proposed site.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.1
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• pp.1-11
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• 2020

Wireless sensor networks consist of a large number of tiny sensor nodes which have limited battery life. In order to maximize the network life span, we propose an optimal transmission radius, R, for control messages. We analyze the transmission radius as a function of the energy consumption of cluster head nodes and the energy consumption of member nodes to find the optimal value of R. In simulations we apply our proposed optimization of transmission range to LEACH-based single-hop and multi-hop networks to show that our proposed scheme outperforms other existing routing algorithms in terms of network life span.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4220-4241
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• 2017

For the downlink energy-harvesting small cell network, this paper proposes an interference management algorithm based on distributed coalitional game. The cooperative interference management problem of the energy-harvesting small cells is modeled as a coalitional game with transfer utility. Based on the energy harvesting strategy of the small cells, the time sharing mode of the small cells in the same coalition is determined, and an optimization model is constructed to maximize the total system rate of the energy-harvesting small cells. Using the distributed algorithm for coalition formation proposed in this paper, the stable coalition structure, optimal time sharing strategy and optimal power distribution are found to maximize the total utility of the small cell system. The performance of the proposed algorithm is discussed and analyzed finally, and it is proved that this algorithm can converge to a stable coalition structure with reasonable complexity. The simulations show that the total system rate of the proposed algorithm is superior to that of the non-cooperative algorithm in the case of dense deployment of small cells, and the proposed algorithm can converge quickly.