• Smart Structures and Systems
• /
• 제21권4호
• /
• pp.389-395
• /
• 2018

This paper presents the application of a semi-active fuzzy based control system for seismic response reduction of a single degree-of-freedom (SDOF) framed structure using a Magnetorheological (MR) damper. Semi-active vibration control with MR dampers has been shown to be a viable approach to protect building structures from earthquake excitation. Moreover, intelligent damping systems based on soft-computing techniques such as fuzzy logic models have the inherent robustness to deal with typical uncertainties and non-linearities present in civil engineering structures. Thus, the proposed semi-active control system uses fuzzy logic based models to simulate the behavior of MR damper and also to develop the control algorithm that computes the required control signal to command the actuator. The results of the numerical simulations show the effectiveness of the suggested semi-active control system in reducing the response of the SDOF structure.

• Journal of Electrical Engineering and Technology
• /
• 제8권5호
• /
• pp.1049-1055
• /
• 2013

DC motors are widely used in industries like cement, paper manufacturing, etc., even today. Early fault identification in dc motors significantly improves its life time and reduces power consumption. Many conventional and soft computing techniques for fault identification in DC motors including a recent work using model based analysis with the help of fuzzy logic are available in literature. In this paper fuzzy logic and norm based wavelet analysis of startup transient current are proposed to identify and quantify the armature winding fault and bearing fault in DC motors, respectively. Results obtained by simulation using Matlab and Simulink are presented in this paper to validate the proposed work.

• 한국정보시스템학회지:정보시스템연구
• /
• 제19권1호
• /
• pp.19-33
• /
• 2010

생산 수율은 비선형관계를 지닌 여러 요인들에 의해 영향을 받기 때문에 반도체 생산의 경우 예측이 어렵다. 본 논문에서 저자들은 사례기반추론과 자기조직화신경망 기반의 데이터마이닝 기법을 활용하여 수율의 높고 낮음을 밝히는 지능화된 수율예측시스템을 제시한다. 이 시스템은 자기조직회신경망을 사용하여 생산 로트의 공정파라미터 패턴을 파악하고 속성가중치 기반의 사례기반추론을 통해 신규 로트의 수율 수준을 예측한다. 이때 속성가중치는 역전파인공신경망을 통해 계산된다. 웹기반 시스템이 개발되고, 반도체 생산 기업의 실제 자료를 적용하여 본 시스템의 효율을 검증하고 평가한다.

• Computers and Concrete
• /
• 제7권3호
• /
• pp.221-237
• /
• 2010

In this study 72 different high strength concrete (HSC) mixes were produced according to the Taguchi design of experiment method. The specimens were divided into four groups based on the range of their compressive strengths 40-60, 60-80, 80-100 and 100-125 MPa. Each group included 18 different concrete mixes. The slump and air-content values of each mix were measured at the production time. The compressive strength, splitting tensile strength and water absorption properties were obtained at 28 days. Using this data the Genetic Programming technique was used to construct models to predict mechanical properties of HSC based on its constituients. These models, together with the cost data, were then used with a Genetic Algorithm to obtain an HSC mix that has minimum cost and at the same time meets all the strength and workability requirements. The paper describes details of the experimental results, model development, and optimization results.

• Computers and Concrete
• /
• 제11권6호
• /
• pp.603-620
• /
• 2013

This paper improves genetic programming (GP) and weight genetic programming (WGP) and proposes soft-computing polynomials (SCP) for accurate prediction and visible polynomials. The proposed genetic programming system (GPS) comprises GP, WGP and SCP. To represent confined compressive strength and strain of circular concrete columns in meaningful representations, this paper conducts sensitivity analysis and applies pruning techniques. Analytical results demonstrate that all proposed models perform well in achieving good accuracy and visible formulas; notably, SCP can model problems in polynomial forms. Finally, concrete compressive strength and lateral steel ratio are identified as important to both confined compressive strength and strain of circular concrete columns. By using the suggested formulas, calculations are more accurate than those of analytical models. Moreover, a formula is applied for confined compressive strength based on current data and achieves accuracy comparable to that of neural networks.

• Computers and Concrete
• /
• 제21권4호
• /
• pp.463-470
• /
• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• 경영과정보연구
• /
• 제3권
• /
• pp.15-45
• /
• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• ETRI Journal
• /
• 제40권3호
• /
• pp.318-329
• /
• 2018

Femtocell (FC) technology envisaged as a cost-effective approach to attain better indoor coverage of mobile voice and data service. Deployment of FCs over macrocell forms a heterogeneous network. In urban areas, the key factor limits the successful deployment of FCs is inter-cell interference (ICI), which severely affects the performance of victim users. Autonomous FC transmission power setting is one straightforward way for coordinating ICI in the downlink. Application of intelligent control using soft computing techniques has not yet explored well for wireless networks. In this work, autonomous FC transmission power setting strategy using Adaptive Neuro Fuzzy Inference System is proposed. The main advantage of the proposed method is zero signaling overhead, reduced computational complexity and bare minimum delay in performing power setting of FC base station because only the periodic channel measurement reports fed back by the user equipment are needed. System level simulation results validate the effectiveness of the proposed method by providing much better throughput, even under high interference activation scenario and cell edge users can be prevented from going outage.

• Advances in environmental research
• /
• 제4권2호
• /
• pp.83-104
• /
• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

• Steel and Composite Structures
• /
• 제39권4호
• /
• pp.471-491
• /
• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.