• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.1058-1068
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• 2011

Video FRUC (Frame Rate Up Conversion) is one of the main issues that have arisen in recent years with the explosive growth of video sources and display formats in consumer electronics. Most advanced FRUC algorithms adopt an efficient motion interpolation technique to determine the motion vector field of interpolated frames. But, in some application areas such as post processing in receiver side, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame was reconstructed. In order to achieve this aim, first, this paper introduces some cost functions to estimate the reliability of a block in the MCI frame. Then, by using these functions, this paper proposes two distortion estimation models for evaluating how much noise was produced in the MCI frame. Through computer simulations, it is shown that the proposed estimation methods perform effectively in estimating the noises of the MCI frame.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.65-86
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• 2000

The estimates from an EM when it is applied to a large causal model of 10 or more categorical variables are often subject to the initial values for the estimates. This phenomenon becomes more serious as the model structure becomes more serious as the model structure becomes more complicated involving more variables. In this regard Wu(1983) recommends among others that EMs are implemented several times with different sets of initial values to obtain more appropriate estimates. in this paper a new approach for initial values is proposed. The main idea is that we use initials that are calibrated to data. A simulation result strongly indicates that the calibrated initials give rise to the estimates that are far closer to the true values than the initials that are not calibrated.

• Speech Sciences
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• v.15 no.2
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• pp.67-77
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• 2008

In this paper, we implemented a robust speech recognizer using the TMS320VC33 DSP. For this implementation, we had built speech and noise database suitable for the recognizer using spectral subtraction method for noise removal. The recognizer has an explicit structure in aspect that a speech signal is enhanced through spectral subtraction before endpoints detection and feature extraction. This helps make the operation of the recognizer clear and build HMM models which give minimum model-mismatch. Since the recognizer was developed for the purpose of controlling car facilities and voice dialing, it has two recognition engines, speaker independent one for controlling car facilities and speaker dependent one for voice dialing. We adopted a conventional DTW algorithm for the latter and a continuous HMM for the former. Though various off-line recognition test, we made a selection of optimal conditions of several recognition parameters for a resource-limited embedded recognizer, which led to HMM models of the three mixtures per state. The car noise added speech database is enhanced using spectral subtraction before HMM parameter estimation for reducing model-mismatch caused by nonlinear distortion from spectral subtraction. The hardware module developed includes a microcontroller for host interface which processes the protocol between the DSP and a host.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.15-34
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• 2006

This paper introduces an empirical modeling technique. This technique uses a set of sample results which are collected from a few small scale simulations. Empirical models are developed by applying a couple of statistical estimation techniques to these samples. We built two types of models for cache miss rates in Symmetric Multiprocessor systems. One is for the changes of input data set size while the specification of target system is fixed. The other is for the changes of the number of processors in target system while the input data set size is fixed. To develop accurate models, we built individual model for every kind of cache misses for each shared data structure in a program. The final model is then obtained by integrating them. Besides, combined use of Least Mean Squares and Robust Estimations enhances the quality of models by minimizing the distortion due to outliers. Empirical modeling technique produces extremely accurate models without analysis on sample data. In addition, since only snail scale simulations are necessary, once a set of samples can be collected, empirical method can be adopted in any research areas. In 17 cases among 24 trials, empirical models present extremely low prediction errors below $1\%$. In the remaining cases, the accuracy is excellent, as well. The models sustain high quality even when the behavioral characteristics of programs are irregular and the number of samples are barely enough.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.163-168
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• 2008

Navigation system based on indoor location estimation is one of the core technologies in mobile robot systems. Wireless sensor network has great potential in the indoor location estimation due to its characteristics such as low power consumption, low cost, and simplicity. In this paper we present an algorithm to estimate the indoor location of mobile robot based on wireless sensor network and fuzzy modeling. ZigBee-based sensor network usually uses RSSI(Received Signal Strength Indication) values to measure the distance between two sensor nodes, which are affected by signal distortion, reflection, channel fading, and path loss. Therefore we need a proper correction method to obtain accurate distance information with RSSI. We develop the fuzzy distance models based on RSSI values and an efficient algorithm to estimate the robot location which applies to the navigation algorithm incorporating the time-varying data of environmental conditions which are received from the wireless sensor network.

• Nutrition Research and Practice
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• v.14 no.6
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• pp.667-678
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• 2020

BACKGROUND/OBJECTIVES: This study aimed to improve portion size estimation aids (PSEAs) used in the nutrition survey of the Korea National Health and Nutrition Examination Survey (KNHANES) and validate the accuracy and precision of the newly developed aids. SUBJECTS/METHODS: We conducted intensive interviews with survey experts in KNHANES and consulted with experts to collect opinions about improvement of PSEAs. Based on the results of the interviews, 5 types of PSEAs (rice bowl, earthen pots, mounds, measuring spoons, and thickness sticks) were newly developed using 3-dimensional (3D) modeling or modification of color or shape. Validation tests were conducted with 96 adults 20 years old or older. For the rice bowl and earthen pots, the participants were asked to select the more similar PSEA in size after being shown the real dishes. For the mounds, measuring spoons, and thickness sticks, the participants were presented with actual plates of food and asked to estimate the given portion sizes using the given PSEAs. RESULTS: The improved 2-dimensional (2D) picture aid for the rice bowl reflecting the size distortion by angle of view using 3D modeling was perceived more closely to the actual size than the current 2D picture (P < 0.001). The change of the color of 2D pictures and 3D models, the change of shape of the measuring spoons, and the 3-dimensionalization of the 2D mounds had no significant improvement in the subjects' perception. CONCLUSIONS: The currently used 2D PSEAs need to be fully redesigned using 3D modeling to improve subjects' perception. However, change of color or shape will not be necessary. For amorphous foods, it is suggested that more evaluation be performed before reaching a final conclusion in the use of PSEAs, or alternative ways to improve accuracy of estimation need to be explored.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.380-390
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• 2016

The field of Image Quality Measure (IQM) is growing rapidly in recent years. In particular, there was a significant progress in No-Reference (NR) IQM methods. In this paper, a general-purpose NR IQM algorithm is proposed based on the statistical characteristics of natural images in shearlet domain. The method utilizes a set of distortion-sensitive features extracted from statistical properties of shearlet coefficients. A complex version of the shearlet transform is employed to take advantage of phase and amplitude features in quality estimation. Furthermore, since shearlet transform can analyze the images at multiple scales, the effect of distortion on across-scale dependencies of shearlet coefficients is explored for feature extraction. For quality prediction, the features are used to train image classification and quality prediction models using a Support Vector Machine (SVM). The experimental results show that the proposed NR IQM is highly correlated with human subjective assessment and outperforms several Full-Reference (FR) and state-of-art NR IQMs.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.459-470
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• 2014

During ship design, welding-induced distortions are roughly estimated as a function of the size of the component as well as the welding process and residual stresses are assumed to be locally in the range of the yield stress. Existing welding simulation methods are very complex and time-consuming and therefore not applicable to large structures like ships. Simplified methods for the estimation of welding effects were and still are subject of several research projects, but mostly concerning smaller structures. The main goal of this paper is the application of a multi-layer welding simulation to the block joint of a ship structure. When welding block joints, high constraints occur due to the ship structure which are assumed to result in accordingly high residual stresses. Constraints measured during construction were realized in a test plant for small-scale welding specimens in order to investigate their and other effects on the residual stresses. Associated welding simulations were successfully performed with fine-mesh finite element models. Further analyses showed that a courser mesh was also able to reproduce the welding-induced reaction forces and hence the residual stresses after some calibration. Based on the coarse modeling it was possible to perform the welding simulation at a block joint in order to investigate the influence of the resulting residual stresses on the behavior of the real structure, showing quite interesting stress distributions. Finally it is discussed whether smaller and idealized models of definite areas of the block joint can be used to achieve the same results offering possibilities to consider residual stresses in the design process.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.11-20
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• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

• Journal of the Korean Geophysical Society
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• v.6 no.1
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• pp.1-11
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• 2003

There are numerous adjustment techniques that deal with the adjustment of geodetic networks but the least squares adjustment is the most common one. During the network adjustment procedure two techniques can be used, the free network adjustment technique and the constrained network adjustment technique. In order to determine the optimum technique for adjusting the geodetic networks, which used for the geodynamical purposes, data from two different geodetic networks "Sinai geodetic network, Egypt, and HGN network, South Korea" had been examined. The used networks had a different configuration and located in different areas with different seismic activity. The results show that both techniques have a high accuracy and no remarkable differences in terms of RMS. On the contrary, the resulted coordinates shows that the constrained network adjustment technique not only cause a remarkable distortion in the station final coordinates but also if the fixed points that define the datum parameters are changed different solutions for the coordinates will be determined. This distortion affect not only in the determination of point displacement but also in the estimation of the deformation parameters, which play a significant role in the geodynamical interpretation of results. Comparing the results which obtained from both techniques with the widely known geodynamical models of the area reviles that the free network adjustment technique results are clearly match with these models, while those obtained from the constrained technique didn’t match at all. By considering the results it seams to be that the free network adjustment technique is the optimum technique, which can be used for the geodetic network adjustment.