• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Computers and Concrete
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• v.22 no.2
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• Korean Journal of Ophthalmology
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• v.32 no.6
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• pp.497-505
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• 2018

Purpose: To evaluate and compare published methods of calculating intraocular lens (IOL) power following myopic laser refractive surgery. Methods: We performed a retrospective review of the medical records of 69 patients (69 eyes) who had undergone myopic laser refractive surgery previously and subsequently underwent cataract surgery at Samsung Medical Center in Seoul, South Korea from January 2010 to June 2016. None of the patients had pre-refractive surgery biometric data available. The Haigis-L, Shammas, Barrett True-K (no history), Wang-Koch-Maloney, Scheimpflug total corneal refractive power (TCRP) 3 and 4 mm (SRK-T and Haigis), Scheimpflug true net power, and Scheimpflug true refractive power (TRP) 3 mm, 4 mm, and 5 mm (SRK-T and Haigis) methods were employed. IOL power required for target refraction was back-calculated using stable post-cataract surgery manifest refraction, and implanted IOL power and formula accuracy were subsequently compared among calculation methods. Results: Haigis-L, Shammas, Barrett True-K (no history), Wang-Koch-Maloney, Scheimpflug TCRP 4 mm (Haigis), Scheimpflug true net power 4 mm (Haigis), and Scheimpflug TRP 4 mm (Haigis) formulae showed high predictability, with mean arithmetic prediction errors and standard deviations of $-0.25{\pm}0.59$, $-0.05{\pm}1.19$, $0.00{\pm}0.88$, $-0.26{\pm}1.17$, $0.00{\pm}1.09$, $-0.71{\pm}1.20$, and $0.03{\pm}1.25$ diopters, respectively. Conclusions: Visual outcomes within 1.0 diopter of target refraction were achieved in 85% of eyes using the calculation methods listed above. Haigis-L, Barrett True-K (no history), and Scheimpflug TCRP 4 mm (Haigis) and TRP 4 mm (Haigis) methods showed comparably low prediction errors, despite the absence of historical patient information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.962-977
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• 2019

The human voice is a convenient method of information transfer between different objects such as between men, men and machine, between machines. The development of information and communication technology, the voice has been able to transfer farther than before. The way to communicate, it is to convert the voice to another form, transmit it, and then reconvert it back to sound. In such a communication process, a vocoder is a method of converting and re-converting a voice and sound. The CELP (Code-Excited Linear Prediction) type vocoder, one of the voice codecs, is adapted as a standard codec since it provides high quality sound even though its transmission speed is relatively low. The EVRC (Enhanced Variable Rate CODEC) and QCELP (Qualcomm Code-Excited Linear Prediction), variable bit rate vocoders, are used for mobile phones in 3G environment. For the real-time implementation of a vocoder, the reduction of sound quality is a typical problem. To improve the sound quality, that is important to know the size and shape of noise. In the existing sound quality improvement method, the voice activated is detected or used, or statistical methods are used by the large mount of data. However, there is a disadvantage in that no noise can be detected, when there is a continuous signal or when a change in noise is large.This paper focused on finding a better way to decrease the reduction of sound quality in lower bit transmission environments. Based on simulation results, this study proposed a preprocessor application that estimates the SNR (Signal to Noise Ratio) using the spectral SNR estimation method. The SNR estimation method adopted the IMBE (Improved Multi-Band Excitation) instead of using the SNR, which is a continuous speech signal. Finally, this application improves the quality of the vocoder by enhancing sound quality adaptively.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.293-301
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• 2019

This study was done to estimate the effect of progeny numbers and pedigree depth on the accuracy of the estimated breeding value (EBV) using best linear unbiased prediction (BLUP) method in Hanwoo. The experiment groups (sire = 100, 200, and 300; progeny = 4 and 8) were made by random sampling and by genetic evaluation of the following traits: Body weight (BW), carcass weight (CW), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS9). As a result of the genetic evaluation, the accuracy of the EBV was roughly 30 - 60% with 4 progenies, and the accuracy of the EBV increased by about 50 - 75% with 8 progenies. In the other words, when the number of progenies increased from 4 to 8, the accuracy of the EBV simultaneously increased by about 15 - 20%. Moreover, when the number of sires was higher, variations in the accuracy of the EBV within the groups for each trait decreased. Therefore, this result indicates that not only the number of progeny but also the number of sires can affect the accuracy of the EBV. Consequently, collecting information on the progeny and careful management of that information are very important things in the Hanwoo breeding system. Therefore, the EBV can show more precise results when conducting genetic evaluations.

• Advances in concrete construction
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• v.10 no.6
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• pp.479-488
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• 2020

The experimental and numerical works were carried out on high performance fiber reinforced concrete (HPFRC) with w/cm ratios ranging from 0.25 to 0.40, fiber volume fraction (Vf)=0-1.5% and 10% silica fume replacement. Improvements in compressive and flexural strengths obtained for HPFRC are moderate and significant, respectively, Empirical equations developed for the compressive strength and flexural strength of HPFRC as a function of fiber volume fraction. A relation between flexural strength and compressive strength of HPFRC with R=0.78 was developed. Due to the complex mix proportions and non-linear relationship between the mix proportions and properties, models with reliable predictive capabilities are not developed and also research on HPFRC was empirical. In this paper due to the inadequacy of present method, a back propagation-neural network (BP-NN) was employed to estimate the 28-day compressive strength of HPFRC mixes. BP-NN model was built to implement the highly non-linear relationship between the mix proportions and their properties. This paper describes the data sets collected, training of ANNs and comparison of the experimental results obtained for various mixtures. On statistical analyses of collected data, a multiple linear regression (MLR) model with R2=0.78 was developed for the prediction of compressive strength of HPFRC mixes, and average absolute error (AAE) obtained is 6.5%. On validation of the data sets by NNs, the error range was within 2% of the actual values. ANN model has given the significant degree of accuracy and reliability compared to the MLR model. ANN approach can be effectively used to estimate the 28-day compressive strength of fibrous concrete mixes and is practical.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.389-399
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• 2022

Observations on the influence of the fluid infiltration on the breakdown pressure during laboratory hydraulic fracturing tests, along with an analysis of the applicability of the breakdown pressure prediction for cylindrical samples using Quasi-static and Linear Elastic Fracture Mechanics approaches were carried out. These approaches consider fluid infiltration through the so-called radius of fluid infiltration or crack radius, a parameter that is not a material property. Two sets of tests under pressurization rate controlled and injection rate controlled tests were used to evaluate the applicability of these methods. The difficulty of the estimation of the radius of fluid infiltration was solved by back calculating this parameter from an initial set of tests, and later, the obtained relationships were used to predict breakdown pressures for a second set of tests. The results showed better predictions for the injection rate than for the pressurization rate tests, with average errors of 3.4% and 18.6%, respectively. The larger error was attributed to differences in the testing conditions for the pressurization rate tests, which had different applied vertical pressures. On the other hand, for the tests carried out under constant injection rate, the Linear Elastic Fracture Mechanics solution reported lower errors compared to the Quasi-static solution, with values of 3% and 3.8%, respectively. Moreover, a sensitivity analysis illustrated the influence of the radius of fluid penetration or crack radius and the tensile strength on the breakdown pressure, suggesting a need for a careful estimation of these values. Then, the calculation of breakdown pressure considering fluid infiltration in cylindrical samples under triaxial conditions is possible, although larger data sets are desirable to validate and derive better relations.

• Smart Media Journal
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• v.11 no.3
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• pp.90-101
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• 2022

Buses are a popular means of transportation. As such, thorough preparation is needed for passenger safety management. However, the safety system is insufficient because there are accidents such as a death accident occurred when the bus departed without recognizing the elderly approaching to get on in 2018. There is a safety system that prevents pinching accidents through sensors on the back door stairs, but such a system does not prevent accidents that occur in the process of getting on and off like the above accident. If it is possible to predict the intention of bus passengers to get on and off, it will help to develop a safety system to prevent such accidents. However, studies predicting the intention of passengers to get on and off are insufficient. Therefore, in this paper, we propose a 1×1 CNN3D-based getting on and off intention prediction model using skeleton keypoints of passengers extracted from the camera image attached to the bus through UDP-Pose. The proposed model shows approximately 1~2% higher accuracy than the RNN and LSTM models in predicting passenger's getting on and off intentions.

• International Journal of Highway Engineering
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• v.8 no.1 s.27
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• pp.131-138
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• 2006

Methods of back calculation for either design procedures or elastic moduli obtained from FWD(Falling Weight Deflectometer) tests have widely been used to predict remaining life of airfield concrete pavements. Since the variation of the elastic modulus obtained from the FWD test depends on the back calculation methods, prediction of remaining life of airfield pavement using the back calculation method has not been reliable. In addition, the FWD method only concentrates on the structural integrity of the pavement without considering functional distress. In this study, a newly developed remaining life estimation procedure is proposed. This methodology includes both structural and functional consideration and suggests models and decision criteria for each stage. In order to improve the estimation procedure on remaining life of pavement, conducted the several tests on an old airfield concrete pavement. As a result, it is concluded that the load transfer efficiency on joint is better for predicting remaining life of pavement than the elastic modulus, which is commonly used. In order to verify applicability of the newly developed estimation procedure and detailed models, investigation and analysis were conducted according to the new methodology on C-airfield pavement. Finally, it is confirmed that the efficiency of the proposed method for practical application was good enough.