• 전기전자학회논문지
• /
• 제23권2호
• /
• pp.529-533
• /
• 2019

오늘날 복합 화력 발전소는 전력 생산을 위해 많이 사용되고 있고, 최근에는 운전 매개 변수를 기반으로 발전 출력을 예측하는 것이 주요 관심사이다. 본 논문에서는 복합 화력 발전소의 출력을 예측하기 위해 컴퓨터 지능 기법을 이용하는 방법을 제시한다. 컴퓨터 지능 기술은 지속적으로 발전되어 많은 실제 문제에 적용되어 왔다. 본 논문에서는 트리 구조의 퍼지 뉴럴 네트워크를 이용하여 발전 출력을 예측하고자 한다. 트리 구조의 퍼지 뉴럴 네트워크는 퍼지 뉴런을 노드로 선택하고 관련 입력을 최적으로 선택하여 규칙 수를 줄이는 장점이 있다. 네트워크의 최적화를 위해 2 단계 최적화 방법이 사용된다. 유전 알고리즘은 최적의 노드와 리프를 선택하여 네트워크의 이진 구조를 최적화 한 다음 랜덤 신호 기반 학습을 수행하여 최적화 된 이진 연결을 단위 구간에서 미세 학습한다. 제안 된 방법의 유용성을 검증하기 위해 UCI Machine Learning Repository Database에서 얻은 복합 화력 발전소 데이터를 사용한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권4호
• /
• pp.1317-1341
• /
• 2021

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제7권2호
• /
• pp.132-137
• /
• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
• /
• pp.209-216
• /
• 2005

The controller that can control the smart base isolation system consisting of M damper and friction pendulum systems(FPS) is developed in this study. A fuzzy logic controller (FLC) is used to modulate the M damper force because the FLC has an inherent robustness and ability to handle non-linearities and uncertainties. A genetic algorithm (GA) is used for optimization of the FLC. When earthquake excitations are applied to the structures equipped with smart base isolation system, the relative displacement at the isolation level as well as the acceleration of the structure should be regulated under appropriate level. Thus, NSGA-II(Non-dominated Sorting Genetic Algorithm) is employed in this study as a multi-objective genetic algorithm to meet more than two control objectives, simultaneously. NSGA-II is used to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. Effectiveness of the proposed method for optimal design of the FLC is judged based on computed responses to several historical earthquakes. It has been shown that the proposed method can efficiently find Pareto optimal sets that can reduce both structural acceleration and base drift from numerical studies.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제16권4호
• /
• pp.281-292
• /
• 2016

The proposed scheme is based on the modified perturb and observe (P&O) algorithm combined with the sliding mode technique. A modified P&O algorithm based sliding mode controller is developed to study the effects of partial shade, temperature, and insolation on the performance of maximum power point tracking (MPPT) used in photovoltaic (PV) systems. Under partially shaded conditions and temperature, the energy conversion efficiency of a PV array is very low, leading to significant power losses. Consequently, increasing efficiency by means of MPPT is particularly important. Conventional techniques are easy to implement but produce oscillations at MPP. The proposed method is applied to a model to simulate the performance of the PV system for solar energy usage, which is compared to the conventional methods under non-uniform insolation improving the PV system utilization efficiency and allowing optimization of the system performance. The modified perturb and observe sliding mode controller successfully overcomes the issues presented by non-uniform conditions and tracks the global MPP. Compared to MPPT techniques, the proposed technique is more efficient; it produces less oscillation at MPP in the steady state, and provides more precise tracking.

• ETRI Journal
• /
• 제39권4호
• /
• pp.582-591
• /
• 2017

To address the wire complexity problem in large-scale globally asynchronous, locally synchronous systems, a current-mode ternary encoding scheme was devised for a two-phase asynchronous protocol. However, for data transmission through a very long wire, few studies have been conducted on reducing the long propagation delay in current-mode circuits. Hence, this paper proposes a current steering logic (CSL) that is able to minimize the long delay for the devised current-mode ternary encoding scheme. The CSL creates pulse signals that charge or discharge the output signal in advance for a short period of time, and as a result, helps prevent a slack in the current signals. The encoder and decoder circuits employing the CSL are implemented using $0.25-{\mu}m$ CMOS technology. The results of an HSPICE simulation show that the normal and optimal mode operations of the CSL achieve a delay reduction of 11.8% and 28.1%, respectively, when compared to the original scheme for a 10-mm wire. They also reduce the power-delay product by 9.6% and 22.5%, respectively, at a data rate of 100 Mb/s for the same wire length.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제2권2호
• /
• pp.159-165
• /
• 2002

This paper proposes a GA and Gradient Descent Method-based method for choosing an appropriate set of fuzzy rules for classification problems. The aim of the proposed method is to fond a minimum set of fuzzy rules that can correctly classify all training patterns. The number of inference rules and the shapes of the membership functions in the antecedent part of the fuzzy rules are determined by the genetic algorithms. The real numbers in the consequent parts of the fuzzy rules are obtained through the use of the descent method. A fitness function is used to maximize the number of correctly classified patterns, and to minimize the number of fuzzy rules. A solution obtained by the genetic algorithm is a set of fuzzy rules, and its fitness is determined by the two objectives, in a combinatorial optimization problem. In order to demonstrate the effectiveness of the proposed method, computer simulation results are shown.

• 한국정밀공학회지
• /
• 제32권12호
• /
• pp.1011-1016
• /
• 2015

In a roll-to-roll continuous system, winding is one of the most important processes since it determines the quality of the final manufactured products such as flexible film and printed electronic devices. Since an adequate winding tension can reduce the incidence of the defects that are derived from the inner stress of the wound roll such as starring and telescoping, it is necessary to determine the optimal taper-tension profile. In this study, an algorithm for the setting of an optimal taper-tension profile in consideration of the residual stress in the wound roll is suggested; furthermore, the algorithm was adjusted for the determination of an optimal taper-tension profile regarding the winding process of $10{\mu}m$ polypropylene (PP) film. As a result of the algorithm-generated, optimal taper-tension profile, the residual stress and radial stress in a PP wound roll were decreased to 27.37 % and 40.05 % (mean value), respectively.

• Journal of Power Electronics
• /
• 제14권2호
• /
• pp.351-361
• /
• 2014

The discrete torque generation mechanism and inherently nonlinear magnetic characterization of switched reluctance motors lead to unacceptable torque ripples and limit the application of these motors. In this study, a phase current profiling technique and torque sharing function are proposed in consideration of magnetic saturation effects and by minimizing power loss in the commutation area between the adjacent phases. Constant torque trajectories are considered in incoming and outgoing phase current planes based on nonlinear T-i-theta curves obtained from experimental measurements. Optimum points on constant torque trajectories are selected by improving drive efficiency and minimizing copper loss in each rotor position. A novel analytic invertible function is introduced to express phase torque based on rotor position and its corresponding phase current. The optimization problem is solved by the proposed torque function, and optimum torque sharing functions are derived. A modification method is also introduced to enhance the torque ripple-free region based on simple logic rules. Compared with conventional torque sharing functions, the resultant reference current from the proposed method has less peak and effective values and exhibits lower copper loss. Experimental and simulation results from a four-phase 4 KW 8/6 SRM validate the effectiveness of the proposed method.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2009년도 제40회 하계학술대회
• /
• pp.1098_1099
• /
• 2009

In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.