• Structural Engineering and Mechanics
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• 제13권2호
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• pp.187-209
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• 2002

The infrastructure system in the United States has been aging faster than the resource available to restore them. Therefore decision for allocating the resources is based in part on the condition of the structural system. This paper proposes to use neural network to predict the overall rating of the structural system because of the successful applications of neural network to other fields which require a "symptom-diagnostic" type relationship. The goal of this paper is to illustrate the potential of using neural network in civil engineering applications and, particularly, in bridge evaluations. Data collected by the Tennessee Department of Transportation were used as "test bed" for the study. Multi-layer feed forward networks were developed using the Levenberg-Marquardt training algorithm. All the neural networks consisted of at least one hidden layer of neurons. Hyperbolic tangent transfer functions were used in the first hidden layer and log-sigmoid transfer functions were used in the subsequent hidden and output layers. The best performing neural network consisted of three hidden layers. This network contained three neurons in the first hidden layer, two neurons in the second hidden layer and one neuron in the third hidden layer. The neural network performed well based on a target error of 10%. The results of this study indicate that the potential for using neural networks for the evaluation of infrastructure systems is very good.

• Wind and Structures
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• 제21권4호
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• pp.443-460
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• 2015

In this paper, an integrator based method to estimate the effective wind speed in wind turbine systems is proposed. First, the aerodynamic torque was accurately estimated through a proportional gain based observer where the generator speed is the measured output of the system. The torque signal contains not only useful frequencies of the wind, but also high frequencies and the ones due to structural vibration. The useful information of the wind signal is low frequency. A spectral analysis permitted the determination of the useful frequencies. The high frequencies were then filtered before introducing the torque signal in the wind speed observer. The desired effective wind speed was extracted through an integrator based observer using the previously estimated aerodynamic torque. The strength of the method is to avoid numerical solutions used in literature of the wind speed estimation. The effectiveness of the proposed wind speed estimator and its use to control the generator speed has been tested under turbulent situations using the FAST software (Fatigue, Aerodynamics, Structures, and Turbulence), for large scale Megawatt turbine.

• 한국광학회지
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• 제14권6호
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• pp.583-587
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• 2003

본 논문에서는 피드포워드(feedforward) 방식의 2차 편광모드분산 보상에 있어서 보상 장치에서 발생시켜야 할 최적의 보상 벡터를 펄스 폭 증가율을 최소화함으로써 이론적으로 유도하였다. 최적의 2차 보상 벡터는 전송 선로의 편광모드분산 벡터의 단순한 Taylor전개식과는 달리, 편광모드분산 벡터와 출력 신호 편광의 주파수 평균들의 선형 조합으로 표현됨을 밝혔다. 최적의 2차 편광모드분산 보상에서의 펄스 폭 증가율을 해석적으로 계산하였으며, 계산 결과를 전산 모의 실험에 의한 결과와 비교하여 일치함을 확인하였다. 본 논문에서 유도한 최적의 2차 보상 벡터에 관한 공식은 피드포워드 방식의 2차 편광모드분산 보상에 반드시 필요한 정보를 제공한다.

• Structural Engineering and Mechanics
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• 제71권5호
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• pp.545-552
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• 2019

The analysis and design of complex structures like sandwich-panel elements are difficult; the use of finite element method for the analysis is complicated and time consuming when non-linear effects are considered. On the other hand, artificial neural network (ANN) models can capture the non-linear effects and its application requires lesser computational demand. Two ANN models were trained, tested and validated to compute the force for a given displacement of a sandwich-type roof element; 2555 force and element deformation pairs were used for training the ANN models. For the models trained without considering the damping effect, there were two values in the input layer: maximum displacement and current displacement, and for the model considering damping, displacement from the previous step was used as an additional input. Totally, 400 ANN models were trained. Results show that there is a good agreement between the experimental and simulated data, and the models showed a good performance with a mean square error value of 4548.85. Both the ANN models could simulate the inelastic behaviour, loss of rigidity, and evolution of permanent displacements. The models could also interpolate and extrapolate, which enables them to be used as an analysis and design tool for such complex elements.

• Structural Engineering and Mechanics
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• 제70권6호
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.374-385
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• 2019

This paper presents an optimal implementation of a Daubechies-based pipelined discrete wavelet packet transform (DWPT) processor using finite impulse response (FIR) filter banks. The feed-forward pipelined (FFP) architecture is exploited for implementation of the DWPT on the field-programmable gate array (FPGA). The proposed DWPT is based on an efficient transpose form structure, thereby reducing its computational complexity by half of the system. Moreover, the efficiency of the design is further improved by using a canonical-signed digit-based binary expression (CSDBE) and advanced functional sharing (AFS) methods. In this work, the AFS technique is proposed to optimize the convolution of FIR filter banks for DWPT decomposition, which reduces the hardware resource utilization by not requiring any embedded digital signal processing (DSP) blocks. The proposed AFS and CSDBE-based DWPT system is embedded on the Virtex-7 FPGA board for testing. The proposed design is implemented as an intellectual property (IP) logic core that can easily be integrated into DSP systems for sub-band analysis. The achieved results conclude that the proposed method is very efficient in improving hardware resource utilization while maintaining accuracy of the result of DWPT.

• ETRI Journal
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• 제41권2호
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• pp.235-241
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• 2019

In this paper, we propose a rank-weighted reconstruction feature to improve the robustness of a feed-forward deep neural network (FFDNN)-based acoustic model. In the FFDNN-based acoustic model, an input feature is constructed by vectorizing a submatrix that is created by slicing the feature vectors of frames within a context window. In this type of feature construction, the appropriate context window size is important because it determines the amount of trivial or discriminative information, such as redundancy, or temporal context of the input features. However, we ascertained whether a single parameter is sufficiently able to control the quantity of information. Therefore, we investigated the input feature construction from the perspectives of rank and nullity, and proposed a rank-weighted reconstruction feature herein, that allows for the retention of speech information components and the reduction in trivial components. The proposed method was evaluated in the TIMIT phone recognition and Wall Street Journal (WSJ) domains. The proposed method reduced the phone error rate of the TIMIT domain from 18.4% to 18.0%, and the word error rate of the WSJ domain from 4.70% to 4.43%.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2019년도 전력전자학술대회
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• pp.71-73
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• 2019

The output current of the Grid Connected Inverter (GCI) can be polluted with harmonics mainly due to i) dead time in switches, ii) non-linearity of switches, iii) grid harmonics, and iv) DC link fluctuation. Therefore, it is essential to design the robust Harmonic Compensation (HC) technique for the improvement of output current quality and fulfill the IEEE 1547 Total harmonics Distortion (THD) limit i.e. <5%. The conventional harmonic techniques often are complex in implementation due to their i) additional hardware needs, ii) complex structure, iii) difficulty in tuning of parameters, iv) current controller compatibility issues, and v) higher computational burden. In this paper, to eliminate the harmonics from the GCI output current, a novel Digital Lock-In Amplifier (DLA) based harmonic detection is proposed. The advantage of DLA is that it extracts the harmonic information accurately, which is further compensated by means of PI controller in feed forward manner. Moreover, the proposed HC method does not require additional hardware and it works with any current controller reference frame. To show the effectiveness of the proposed HC method a 5kW GCI prototype built in laboratory. The output current THD is achieved less than 5% even with 10% load, which is verified by simulation and experiment.

• Environmental Engineering Research
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• 제24권3호
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.