• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.971-976
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• 1994

A speech display system is developed for the evaluation and the training of speech utterance. The speech is analyzed by linear predictive technique every 5 ms and the frequencies of the lowest two spectral local peaks P1 and P2 are extracted. The vowel trakectory is displayed using those frequencies on th P1-P2 plane. In most cases, P1 and P2 correspond to the first and the second formants, but in the case of indistinct utterance, the correspondence between the local spectral peaks and the formants tends to fall into disorder. And the system is considered to be useful for the evaluation of speech quality. The examples of some words uttered by normal speakers and some patients with difficulty in utterance are compared each other for the discussion of the effectiveness of the system.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.137-147
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• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.419-424
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• 2000

The land cover classification by using remotely sensed image becomes necessary and useful for hydrologic and water quality related applications. The purpose of this study is to obtain land classification map by using remotely sensed data : Landsat TM and KOMPSAT-1 EOC. The classification was conducted by maximum likelihood method with training set and Tasseled Cap Transform. The best result was obtain from the Landsat TM merged by KOMPSAT EOC, that is, similar with statistical data. This is caused by setting more precise training set with the enhanced spatial resolution by using KOMPSAT EOC(6.6m${\times}$6.6m).

• Journal of Power Electronics
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• v.11 no.6
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• pp.922-930
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• 2011

In this paper, two radial basis function neural networks (RBFNNs) are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the types of harmonic content are identified over a wide operating range. Constant power and sinusoidal current compensation strategies are investigated in this paper. The RBFNN filtering training algorithm is based on a systematic and computationally efficient training method called the hybrid learning method. In this new methodology, the RBFNN is combined with the p-q theory to extract the harmonics content in converter waveforms. The small size and the robustness of the resulting network models reflect the effectiveness of the algorithm. The analysis is verified using MATLAB simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.555-561
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• 2009

The established mathematical modeling methods have limitation to know the hydraulic characteristics at the wastewater treatment plant which are complex and nonlinear systems. So, an artificial neural network (ANN) model based on hydraulic characteristics is applied for modeling wastewater quality of a full-scale wastewater treatment plant using DNR (Daewoo nutrient removal) process. ANN was trained using data which are influents (TSS, BOD, COD, TN, TP) and effluents (COD, TN, TP) components in a year, and predicted the effluent results based on the training. To raise the efficiency of prediction, inputs of ANN are added the influent and effluent information that are in yesterday and the day before yesterday. The results of training data tend to have high accuracy between real value and predicted value, but test data tend to have lower accuracy. However, the more hydraulic characteristics are considered, the results become more accuracy.

• International Journal of Industrial Entomology and Biomaterials
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• v.3 no.2
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• pp.117-120
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• 2001

In India, mulberry (Morus spp.), the sole food plant of the silkworm, Bombyx mori (Linn.), is prone to infestation by the pink mealybug, Maconellicoccus hirsutus (Green). Infestation by this pest causes apical shoot malformation, popularly known as 'tukra'. Occurrence of tukra causes an appreciable reduction in leaf yield and quality, leading to low silkworm cocoon productivity. For management of M. hirsutus (Tukra), an IPM package comprising mechanical, chemical and biological measures was demonstrated in the mulberry gardens of five Government Silk Farms in Mysore District (Karnataka, India) during 1995-96. A suppression of 76.0% in tukra incidence and 90.19% in mealybug population was recorded by employ the IPM package which led to an estimated 4,000 kg recovery in leaf yield/ha/year. The impact of IPM package in the management of M. hirsutus, the role of biocontrol agent (Cryptolaemus montrouzieri Muls.) in pest suppression and the cost-benefit analysis of the IPM package are discussed.

• Proceedings of the KSR Conference
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• 1999.11a
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• pp.259-266
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• 1999

A study on the static type train simulator will include the training of new drives requires that the environment of the cab, controls placement, etc. must highly realistic so that driver can readily transfer his training experience to the real world. The simulator computer sends video disc speed command to a Video PC processor. A video switcher select the output of the on-line player. This selection is done with loss of vertical synchronization, meaning the picture will not noticeable roll or jump as the simulation mover from disc to disc. The video image quality remain contestant through the simulated speed range from zero to 100km/h. Flicker is avoided in the scene by the use of a TBC(Time Base Corrector) which causes the display of one video field at a time. Thus, no interfield jitter is present when the scene is stopped.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.59-61
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• 2018

Highly compressed images typically not only have low resolution, but are also affected by compression artifacts. Performing image super-resolution (SR) directly on highly compressed image would simultaneously magnify the blocking artifacts. In this paper, a SR method based on deep learning is proposed. The method is an end-to-end trainable deep convolutional neural network which performs SR on compressed images so as to reduce compression artifacts and improve image resolution. The proposed network is divided into compression artifacts removal (CAR) part and SR reconstruction part, and the network is trained by three-step training method to optimize training procedure. Experiments on JPEG compressed images with quality factors of 10, 20, and 30 demonstrate the effectiveness of the proposed method on commonly used test images and image sets.

• Korea Trade Review
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• v.44 no.5
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• pp.45-63
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• 2019

The purpose of this study was to identify methods to effectively train experts in trade. In order to achieve the purpose of this study, the relevant literature was analyzed. Three experts from each of academia, the private sector and the government sector were interviewed. The study results found that the problems of cultivating trade experts were quantitative and qualitative mismatches, the disparity with the field, and the lack of government support for training trade experts, etc. Methods for fostering trade experts were suggested to resolve these mismatches, to secure a budget for training trade experts, to establish a body or council for coordination, to develop a trade expert roadmap, to introduce a program for certificated trade expert, and to improve the quality of education for trade experts.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.30-35
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• 2011

2D pollutant transport model was applied to the simulation of contaminant transport in the channel. At first, two kinds of virtual channels having different slopes were designed. The distribution of contaminant, which flows from one of the three drainages to the main channel, was simulated by each 2D model. Concentrations of 745 nodes were converted to input data of neural network model (Multi-perceptron) for training and verification using matrix. The first three cases (Case A-1, A-2, A-3) were used for training Multi-perceptron, the other three cases (Case B-1, B-2, B-3) were used for verification. As a result, Multi-perceptron reasonably divided the cases into the three characteristics which have different contaminant distributions due to the different input point of water pollution source. It can be a useful methodology for the water quality monitoring and backtracking.