• Smart Structures and Systems
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• 제32권1호
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• pp.37-47
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• 2023

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2002년도 추계학술대회
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• pp.289-312
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• 2002

This study suggests the Link weight analysis approach to choose input variables and an integrated model to make more accurate bankruptcy prediction model. the Link weight analysis approach is a method to choose input variables to analyze each input node's link weight which is the absolute value of link weight between an input nodes and a hidden layer. There are the weak-linked neurons elimination method, the strong-linked neurons selection method in the link weight analysis approach. The Integrated Model is a combined type adapting Bagging method that uses the average value of the four models, the optimal weak-linked-neurons elimination method, optimal strong-linked neurons selection method, decision-making tree model, and MDA. As a result, the methods suggested in this study - the optimal strong-linked neurons selection method, the optimal weak-linked neurons elimination method, and the integrated model - show much higher accuracy than MDA and decision making tree model. Especially the integrated model shows much higher accuracy than MDA and decision making tree model and shows slightly higher accuracy than the optimal weak-linked neurons elimination method and the optimal strong-linked neurons selection method.

• 콘크리트학회논문집
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• 제11권6호
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• pp.47-56
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• 1999

In this paper, a nonlinear finite element procedure is presented for the analysis of reinforced concrete shell structures. The 4-node quadrilateral flat shell finite element with drilling rotational stiffness is developed. The layered approach is used to discretize behavior of concrete and reinforcement through the thickness. Material nonlinearity is taken into account by comprising tensile, compressive and shear models of cracked concrete and a model of reinforcing steel. The smeared crack approach is incorporated. The steel reinforcement is assumed to be in a uniaxial stress state and to be a smeared in a layer. The proposed numerical method for nonlinear analysis of reinforce concrete shells will be verified by comparison with reliable experimental results.

• 한국환경과학회지
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• 제8권2호
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• pp.211-219
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• 1999

Surface complexation models(SCMs) have been performed to predict metal ion adsorption behavior onto the mineral surface. Application of SCMs, however, requires a self-consistent approach to determine model parameter values. In this paper, in order to determine the metal ion adsorption parameters for the triple layer model(TLM) version of the SCM, we used the zeta potential data for Zeolite and Kaolinite, and the metal ion adsorption data for Pb(II) and Cd(II). Fitting parameters determined for the modeling were as follows ; total site concentration, site density, specific surface area, surface acidity constants, etc. Zeta potential as a new approach other than the acidic-alkalimetric titration method was adopted for simulation of adsorption phenomena. Some fitting parameters were determined by the trial and error method. Modeling approach was successful in quantitatively simulating adsorption behavior under various geochemical conditions.

• 한국정밀공학회지
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• 제21권8호
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• pp.106-111
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• 2004

Micro-stereolithography technology has made it possible to fabricate any form of three-dimensional microstructures. It makes a 3D structure by dividing the shape into many slices of relevant thickness along horizontal surface, hardening each layer of slice with a laser, and stacking them up to a desired shape. Until now, however, the micro-stereolithography device was not designed systematically because the key factors governing the device were not considered. In this paper, we designed micro-stereolithography device using axiomatic approach. This paper contains an overview and an analysis of a new proposed system for development of micro-stereolithegraphy device, and detailed descriptions of the activities in this system. The newly designed system offers reduced machine size by minimizing of optical components and decoupled design matrix.

• Advances in aircraft and spacecraft science
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• 제4권5호
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• pp.555-571
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• 2017

The description of transitional flows by means of RANS equations is sometimes based on non-local approaches which require the computation of some boundary layer properties. In this work a non-local Laminar Kinetic Energy model is used to predict transitional and separated flows. Usually the non-local term of this model is evaluated along the grid lines of a structured mesh. An alternative approach, which does not rely on grid lines, is introduced in the present work. This new approach allows the use of fully unstructured meshes. Furthermore, it reduces the grid-dependence of the predicted results. The approach is employed to study the transitional flows in the T106c turbine cascade and around a NACA0021 airfoil by means of a discontinuous Galerkin method. The local nature of the discontinuous Galerkin reconstruction is exploited to implement an adaptive algorithm which automatically refines the mesh in the most significant regions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.196-198
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• 2002

This paper represents a new approach for the protective relay of power transmission lines using a Artificial Neural Network(ANN). A different fault on transmission lines need to be detected, classified and located accurately and cleared as fast as possible. However, The protection range of the distance relay is always designed on the basis of fixed settings, and unfortunately these approach do not have the ability to adapt dynamically to the system operating condition. ANN is suitable for the adaptive relaying and the detection of complex faults. The backpropagation algorithm based multi-layer perceptron is utilized for the learning process. It allows to make control to various protection functions. As expected, the simulation result demonstrate that this approach is useful and satisfactory.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문초록집
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• pp.388.2-388
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• 2002

An equivalent electroacoustic circuit approach of estimating the sound absorption coefficient for parallel perforated plate system is proposed. The proposed approach is validated by comparing the calculated absorption coefficients of a parallel single layer perforated plate system with the values measured by the two-microphone impedance tube method for various porosity and cavity depth. (omitted)

• Wind and Structures
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• 제18권6호
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• pp.619-631
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• 2014

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1679-1691
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• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.