• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.59-65
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• 1996

This paper proposes a 4 kbps PSI-VSELP(Pitch Synchronous Innovation-Vector Sum Excited Linear Prediction) speech coder which produces speech equivalent to that of the conventional 4.8 kbps VSELP. Since the 'half-rate' is differently defined from country to country, there may be a need to reduce the bit rate of conventional half-rate coder. To minimize the degradation of speech quality caused by bit-rate reduction, it is desirable to perform bit-allocation based on the carefull consideration of the effect of various transmission parameters. This paper adopts this analytical approach for bit-allocation at 4 kbps. To improve the quality of the VSELP coder at 4 kbps, basis vectors which play the most important role in the performance, are optimized by an iterative closed-loop training process and the PSI technique is employed in the VSELP performance, are optimized by an iterative closed-loop training process and the PSI technique is employed in the VSELP coder. To demonstrate the performance of the proposed speech coder, we peformed experiments under the noiseless and error free conditions. From experimental results, even though the proposed 4 kbps PSI-VSELP coder showed lower scores in the objective measure, higher scores in subjective measure was obtained compared with those of the conventional 4.8 kbps VSELp.

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.70-77
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• 2017

Bandwidth extension is a technique to improve speech quality, intelligibility and naturalness, extending from the 300 ~ 3,400 Hz narrowband speech to the 50 ~ 7,000 Hz wideband speech. In this paper, an Artificial Bandwidth Extension (ABE) module embedded in the Opus audio decoder is designed using the information of narrowband speech to reduce the computational complexity of LPC (Linear Prediction Coding) and LSF (Line Spectral Frequencies) analysis and the algorithm delay of the ABE module. We proposed a spectral envelope extension method using DBN (Deep Belief Network), one of deep learning techniques, and the proposed scheme produces better extended spectrum than the traditional codebook mapping method.

• Journal of the Korean Chemical Society
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• v.60 no.4
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• pp.225-234
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• 2016

The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1209-1223
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• 2022

Blood pressure is one of the key physiological parameters for determining human health, and can prove whether human cardiovascular function is healthy or not. In general, what we call blood pressure refers to arterial blood pressure. Blood pressure fluctuates greatly and, due to the influence of various factors, even varies with each heartbeat. Therefore, achievement of continuous blood pressure measurement is particularly important for more accurate diagnosis. It is difficult to achieve long-term continuous blood pressure monitoring with traditional measurement methods due to the continuous wear of measuring instruments. On the other hand, radar technology is not easily affected by environmental factors and is capable of strong penetration. In this study, by using machine learning, tried to develop a linear blood pressure prediction model using data from a public database. The radar sensor evaluates the measured object, obtains the pulse waveform data, calculates the pulse transmission time, and obtains the blood pressure data through linear model regression analysis. Confirm its availability to facilitate follow-up research, such as integrating other sensors, collecting temperature, heartbeat, respiratory pulse and other data, and seeking medical treatment in time in case of abnormalities.

• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Journal of Navigation and Port Research
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• v.35 no.1
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• pp.83-91
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• 2011

The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.

• Aerospace Engineering and Technology
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• v.13 no.2
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• pp.7-17
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• 2014

This technical paper deals with development of on-board orbit generation algorithm for GEO Satellite. This paper presents the research analysis results performed in order to improve the accuracy of the existing algorithm used for generating real-time orbit information for GEO satellite. The error impact on orbit accuracy due to the orbit error sources were analyzed with the algorithm suggested by this research. As a result of the analyses, it is found that the initial orbit should be determined with an accuracy of less than 50 m and the reference position angle error for the ground station and the satellite should be maintained within ${\pm}0.0025deg$ in order to meet the orbit accuracy specification. The development of on-board flight software based on the new algorithm was accomplished and the performance verification is ongoing by using a software based performance verification tool.

• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.75-80
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• 2014

The quality of real-time Voice over Internet Protocol (VoIP) network is affected by network impariments such as delays, jitters, and packet loss. This paper proposes a packet loss concealment algorithm based on voice classification for enhancing VoIP speech quality. In the proposed method, arriving packets are classified by an adaptive thresholding approach based on the analysis of multiple features of short signal segments. The excellent classification results are used in the packet loss concealment. Additionally, linear prediction-based packet loss concealment delivers high voice quality by alleviating the metallic artifacts due to concealing consecutive packet loss or recovering lost packet.

• Geomechanics and Engineering
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• v.20 no.4
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• pp.359-370
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• 2020

Prestressed ground anchors are important structural elements in geotechnical engineering. Despite their widespread usage, the design process is often significantly simplified. One of the major drawbacks of commonly used design methods is the assumption that skin friction is mobilized uniformly along an anchor's fixed length, one consequence of which is that a progressive failure phenomenon is neglected. The following paper introduces an alternative design approach - a computer algorithm employing the load-transfer method. The method is modified for the analysis of anchors and combined with a procedure for the derivation of load-transfer functions based on commonly available laboratory tests. The load-transfer function is divided into a pre-failure (hardening) and a post-failure (softening) segment. In this way, an aspect of non-linear stress-strain soil behavior is incorporated into the algorithm. The influence of post-grouting in terms of radial stress update, diameter enlargement, and grout consolidation is included. The axial stiffness of the anchor body is not held constant. Instead, it gradually decreases as a direct consequence of tensile cracks spreading in the grout material. An analysis of the program's operation is performed via a series of parametric studies in which the influence of governing parameters is investigated. Finally, two case studies concerning three investigation anchor load tests are presented.