• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.2
• /
• pp.46-56
• /
• 1999

A predictive controller is designed based upon stochastic methods for compensation time-delay effect on a system which has inherent time-delay caused by the spatial separation between controllers and actuators. The predictive controller estimates current outputs through linear prediction methods and probability functions utilizing previous outputs, and minimizes the malicious phenomena caused by the time-delay in precision control systems. To demonstrate effectiveness of this control methodology, we applied this algorithm for the control of a tele-operated DC servomotor. The experimental results show that this predictive controller is superior to the PID controller in terms of convergence-characteristics, and show that this controller expands the maximum allowable time-delay for a system maintaining the stability. Since the proposed predictor does not require models of plants - it requires only stochastic information for outputs --, it is a general scheme which can be applied for the control of systems which are difficult to model or the compensator of PID control.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.6
• /
• pp.87-95
• /
• 1999

We propose a simulation model of 3-dimensional MPEG data over Asynchronous transfer Mode(ATM) networks. The model is based on a slice level and is named to Projected Vector Autoregressive(PVAR) model. The PVAR model is modeled using the Autoregressive(AR) model in order to meet the autocorrelation condition and fit the histogram, and maps real data by a projection function. For the projection function, we use the Cumulative Distribution Probability Function (CDPF), and the procedure is performed at each slice level. Our proposed model shows good performance in meeting the autocorrelation condition and fitting the histogram, and is found important in analyzing the performance of networks. In addiotion, we apply a smoothing method by which a periodic mean value. In general. the Quality of Service(QoS) depends on the Cell Loss Rate(CLR), which is related to the cell loss and a maximum delay in a buffer. Hence the proposed smoothing method can be used to improve the QoS.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.19-26
• /
• 2005

Image interpolation in the wavelet domain usually takes advantage of the probabilistic models for the intrascale statistics and the interscale dependency. In this paper, we adopt the linear model for the absolute values of wavelet coefficients of interpolated image across scale to estimate the variances of extrapolated bands. The proposed algorithm uses randomly generated wavelet coefficients based on the estimated parameters for probabilistic model. Random number generation according to the estimated probabilistic model may induce the 'salt and pepper' noise in subbands. We reduce the noise power by Wiener filtering. We observe that the proposed method generates the histogram of the subband coefficients similar to the that of original image. Experimental results show that our method outperforms the previous wavelet-domain interpolation method as well as the conventional bicubic method.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.2
• /
• pp.31-39
• /
• 2020

In this paper, we introduce a technique for generating synthetic benchmarks based on time series data. Many of the data measured on IoT devices have a time series characteristic that measures numerical changes over time. However, there is a problem that it is difficult to model the data measured over a long period as generalized time series data. To solve this problem, this paper introduces the BST-IGT model. The BST-IGT model separates the entire data into sections that can be easily time-series modeled, collects the generated data into templates, and produces new synthetic benchmarks that share or modify characteristics based on them. As a result of making a new benchmark using the proposed modeling method, we could create a benchmark with multiple aspects by mixing the composite benchmark with the statistical features of the existing data and other benchmarks.

• The Journal of Engineering Geology
• /
• v.12 no.2
• /
• pp.203-214
• /
• 2002

The statistical model was introduced to satisfy various experimental condition on the sorption of heavy metals (Pb, Cu, Cd, and Zn) by bentonite. The Box-Behnken model designed statistically was applied to determine relative impact among three variables such as pH, HCO$_3$ contents and heavy metal concentrations on the sorption. The SAS program was used to obtain the statistical solution. The statistical surface response analysis indicates that initial concentration of heavy metals and pH have an important effect on the sorption, and bicarbonate is not a serious variable. The sorption capability about heavy metals of bentonite is in the order of Pb>Cu>Zn>Cd. The precipitation as hydroxyl and carbonate complexes of heavy metals was thermodynamically analyzed as major mechanism of sorption under alkaline pHs and high bicarbonate solution. It was found that there is a little difference between the model prediction on the precipitation of heavy metals and the results of batch sorption experiment. The thermodynamic data of the programs have to revise to obtain the best fit condition between the model prediction and the experimental results.

• Phonetics and Speech Sciences
• /
• v.11 no.2
• /
• pp.57-64
• /
• 2019

In this paper, we construct a Korean text-to-speech system using the Merlin toolkit which is an open source system for speech synthesis. In the text-to-speech system, the HMM-based statistical parametric speech synthesis method is widely used, but it is known that the quality of synthesized speech is degraded due to limitations of the acoustic modeling scheme that includes context factors. In this paper, we propose an acoustic modeling architecture that uses deep neural network technique, which shows excellent performance in various fields. Fully connected deep feedforward neural network (DNN), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional LSTM (BLSTM) are included in the architecture. Experimental results have shown that the performance is improved by including sequence modeling in the architecture, and the architecture with LSTM or BLSTM shows the best performance. It has been also found that inclusion of delta and delta-delta components in the acoustic feature parameters is advantageous for performance improvement.

• Journal of Platform Technology
• /
• v.11 no.4
• /
• pp.62-70
• /
• 2023

This research is one of a series of studies to develop a system to help agricultural producers and sellers determine when and how much to ship products to the wholesale market to maximize their profit. The purpose of this research is to incorporate the time-varying quality change effect, which was not used in the previous agricultural and marine product shipping model. The researchers developed four models to measure the quality change effect: quality declining steadily over time, quality declining rapidly at first and then slowly, quality declining first slowly and then rapidly, and quality rising over time and then decreasing again. According to the results of an empirical analysis of the effect of each model's quality change effect on shipments for apples traded in the Garak Wholesale Market from 2014 to 2021, statistical significance was found in the quality change effect of all four models. And there was no significant difference in explanatory power between the four models. Therefore, any of the four models should be introduced into the decision-making system for shipping time for apples.

• Journal of the Korea Concrete Institute
• /
• v.21 no.6
• /
• pp.789-796
• /
• 2009

The randomness in the strength of a RC member is caused mainly by the variability of the mechanical properties of concrete and steel, the dimensions of concrete cross sections, and the placement of reinforcing bars and so on . Among those variations, the randomness and uncertainty of mechanical properties of concrete, such as compressive strength, tensile strength, and elastic modulus give the most significant influences and show relatively large statistical variations. In Korea, there has been little effort for the construction of its own statistical models for mechanical properties of concrete and steel, thus the foreign data have been utilized till now. In this paper, variability of compressive strength, tensile strength and elastic modulus of normal-weight structural concrete with various specified design compressive strength levels are examined based on the data obtained from a number of published and unpublished sources in this country and additional laboratory tests done by the authors. The inherent probabilistic models for compressive and tensile strength of normal-weight concrete are proposed as Gaussian distribution. Also, the relationships between compressive and splitting tensile strength and between compressive strength and elastic modulus in current KCI Code are verified and new ones are suggested based on local data.

• Journal of Korea Water Resources Association
• /
• v.49 no.8
• /
• pp.657-671
• /
• 2016

It is expected that water resources will be changed spatially and temporally due to the global climate change. The quantitative assessment of change in water availability and appropriate water resources management measures are needed for corresponding adaptation. However, there are large uncertainties in climate change impact assessment on water resources. For this reason, development of technology to evaluate the uncertainties quantitatively is required. The objectives of this study are to develop the climate change uncertainty assessment method and to apply it. The 5 RCMs (HadGEM3-RA, RegCM4, MM5, WRF, and RSM), 5 statistical post-processing methods (SPP) and 2 hydrological models (HYM) were applied for evaluation. The results of the uncertainty analysis showed that the RCM was the largest sources of uncertainty in Spring, Summer, Autumn (29.3~68.9%), the hydrological model was the largest source of uncertainty in Winter (46.5%). This method can be possible to analyze the changes in the total uncertainty according to the specific RCM, SPP, HYM model. And then it is expected to provide the method to reduce the total uncertainty.

• Korean Chemical Engineering Research
• /
• v.45 no.1
• /
• pp.32-38
• /
• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.