• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.28-34
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• 1993

An algorithm for MMADF(modified multiplication-free adaptive filter) which need not to multiplication arithmatic operation is proposed to improve the performance of FSE (fractionally spaced equalizer) which reduce the ISI(intersymbol interference) in signal transfer channel. The input signals are quantized using DPCM and the reference signals is processed using a first-order linear prediction filter. The convergence properties of Sign. MADF and M-MADF algorithm for updating of the coefficients of a FIR digital filter of the fractionally spaced equalizer (FSE) are investigated and compared with one another. The convergence properties are characterized by the steady state error and the convergence speed. It is shown that the convergence speed of M-MADF is almost same as Sign algorithm and is faster than MADF in the condition of same steady state error. Especially it is very useful for high correlated signals.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.118-125
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• 2004

The assessment of fatigue property is one of the most indispensable factors to design mechanical structures or parts. For accurately assessing fatigue property, it is necessary to precisely identify the loading condition and material property of the objective structure. However, there are many kind of problems in conducting predictive activity for a design concerned with variable factor such as fatigue phenomenons and environments. Therefore, most of the fatigue problems have been assessed from exiting experiment data and prediction method. In this study, expert system is developed for simply conducting performance assessment of weldments based on Finite element Analysis carrying out performance improvement and safety assessment of welded structures.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.1 no.1
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• pp.55-63
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• 1989

A one dimensional analytic model for the prediction of the stratification coefficient of a liquid-based solar heating system is developed. The stratification coefficient, $K_s$, is defined as the ratio of the actual useful energy gain to the energy gain that would be achieved if there were no thermal stratification in the storage tank. Previous studies incorporated only collector-side effects, but in this study both the collector and load-side effects are included for the overall performance evaluation.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.771-776
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• 2001

By matching a well established fast throughflow code, with standard loss correlations, and an efficient optimization algorithm, a new design system has been developed, which optimizes inlet and exit flow-field parameters for each blade row of a multistage axial flow turbine. The compressible steady state inviscid throughflow code based on streamline curvature method is suitable for fast and accurate flow calculation and performance prediction of a multistage axial flow turbine. A general purpose hybrid constrained optimization package, iSIGHT has been used, which includes the following modules: genetic algorithm, simulated annealing, modified method of feasible directions. The design system has been demonstrated using an example of a 5-stage low pressure steam turbine for 800MW thermal power plant previously designed by HANJUNG. The comparison of computed performance of initial and optimized design shows significant improvement in the turbine efficiency.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.126-129
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• 1998

In order to control the flow of traffics in ATM networks and optimize the usage of network resources, an efficient control mechanism is necessary to cope with congestion and prevent the degradation of network performance caused by congestion. This paper proposes a new UPC(Usage Parameter Control) mechanism that varies the token generation rate and the buffer threshold of leaky bucket by using a Neural Network controller observing input buffers and token pools, thus achieving the improvement of performance. Simulation results show that the proposed adaptive algorithm uses of network resources efficiently and satisfies QoS for the various kinds of traffics.

• Journal of Sasang Constitutional Medicine
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• v.33 no.4
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• Atmosphere
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• v.34 no.1
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• pp.23-34
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• 2024

The Korea Meteorological Administration has improved medium-range weather forecasts by implementing post-processing methods to minimize numerical model errors. In this study, we employ a statistical correction technique known as the minimum continuous ranked probability score (CRPS) to refine medium-range forecast guidance. This technique quantifies the similarity between the predicted values and the observed cumulative distribution function of the Unified Model Ensemble Prediction System for Global (UM EPSG). We evaluated the performance of the medium-range forecast guidance for surface air temperature and relative humidity, noting significant enhancements in seasonal bias and root mean squared error compared to observations. Notably, compared to the existing the medium-range forecast guidance, temperature forecasts exhibit 17.5% improvement in summer and 21.5% improvement in winter. Humidity forecasts also show 12% improvement in summer and 23% improvement in winter. The results indicate that utilizing the minimum CRPS for medium-range forecast guidance provide more reliable and improved performance than UM EPSG.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.588-590
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• 2006

Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto-associative kernel regression by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.83-91
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• 1997

DELVAUX is an inductive learning environment that learns Bayesian classification rules from a set o examples. In DELVAUX, a genetic a, pp.oach is employed to learn the best rule-set, in which a population consists of rule-sets and rule-sets generate offspring by exchanging some of their rules. We have explored a meta-learning a, pp.oach in the DELVAUX learning environment to improve the classification performance of the DELVAUX system. The meta-learning a, pp.oach learns the bias of a classifier so that it can evaluate the prediction made by the classifier for a given example and thereby improve the overall performance of a classifier system. The paper discusses the meta-learning a, pp.oach in details and presents some empirical results that show the improvement we can achieve with the meta-learning a, pp.oach.

• Journal of KIISE
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• v.44 no.11
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• pp.1156-1164
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• 2017

Existing studies on automatic bug triage were mostly used the method of designing the prediction system based on the machine learning algorithm. Therefore, it can be said that applying a high-performance machine learning model is the core of the performance of the automatic bug triage system. In the related research, machine learning models that have high performance are mainly used, such as SVM and Naïve Bayes. In this paper, we apply Deep Learning, which has recently shown good performance in the field of machine learning, to automatic bug triage and evaluate its performance. Experimental results show that the Deep Learning based Bug Triage system achieves 48% accuracy in active developer experiments, un improvement of up to 69% over than conventional machine learning techniques.