• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.432-433
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• 2013

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1088-1091
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• 1994

This paper presents a method to predict in statistics the coded signal propagation in fading channel with the help of the ray theory. This method features its high speed and efficiency. The predictions of received signal envelope and pulse width can be give out quickly.

• Journal of KIISE
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• v.42 no.2
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• pp.145-154
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• 2015

This paper proposes an enhanced projection candidate selection algorithm to improve the performance of the existing path prediction algorithm. Various user path prediction algorithms have previously been developed, but those algorithms are inappropriate for a real-time and close user path prediction environment. To resolve this issue, a new prediction algorithm has been proposed, but several problems still remain. In particular, this algorithm should be enhanced to provide much faster processing performance. The major cause of the high processing time of the previous path prediction algorithm is the high time complexity of its projection candidate selection. Therefore, this paper proposes a new path fragment management structure and an improved projection candidate selection algorithm to improve the processing speed of the existing projection candidate selection algorithm. This paper also shows the effectiveness of the algorithm herein proposed through a comparative performance evaluation.

• Journal of the Korean earth science society
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• v.38 no.1
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• pp.11-23
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• 2017

The short term predictability of wind resources is an important factor in evaluating the economic feasibility of a wind power plant. As a method of improving the predictability, a Bayesian Kalman filter is applied as the model data postprocessing. At this time, a statistical training period is needed to evaluate the correlation between estimated model and observation data for several Kalman training periods. This study was quantitatively analyzes for the prediction characteristics according to different training periods. The prediction of the temperature and wind speed with 3-day short term Bayesian Kalman training at Taebaek area is more reasonable than that in applying the other training periods. In contrast, it may produce a good prediction result in Ieodo when applying the training period for more than six days. The prediction performance of a Bayesian Kalman filter is clearly improved in the case in which the Weather Research Forecast (WRF) model prediction performance is poor. On the other hand, the performance improvement of the WRF prediction is weak at the accurate point.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.27-32
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• 2005

As a large quantity of Genome sequencing has happened to be done a very much a surprising speed in short period, an automatic genome annotation process has become prerequisite. The most difficult process among with this kind of genome annotation works is to finding out the protein-coding genes within a genome. The main 2 subjects of gene prediction are Eukaryotes and Prokaryotes ; their genes have different structures, therefore, their gene prediction methods will also obviously varies. Until now, it is found that among of the 231 genome sequenced species, 200 have been found to be prokaryotes, therefore, for study of biotechnology studies, through comparative genomics, prokaryotes, rather than eukaryotes could may be more appropriate than eukaryotes. Even more, prokaryotes does not have the gene structure called an intron, so it makes the gene prediction easier. Former prokaryotes gene predictions have been shown to be 80%～ to 90% of accuracy. A recent study is aiming at 100% of gene prediction accuracy. In this paper, especially in the case of the E. coli K-12 and S. typhi genomes, gene prediction accuracy which showed 98.5% and 98.7% was more efficient than previous GLIMMER.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• Fibers and Polymers
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• v.7 no.4
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• pp.404-413
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• 2006

This study examines multiple quality optimization of the injection molding for Polyether Ether Ketone (PEEK). It also looks into the dimensional deviation and strength of screws that are reduced and improved for the molding quality, respectively. This study applies the Taguchi method to cut down on the number of experiments and combines grey relational analysis to determine the optimal processing parameters for multiple quality characteristics. The quality characteristics of this experiment are the screws' outer diameter, tensile strength and twisting strength. First, one should determine the processing parameters that may affect the injection molding with the $L_{18}(2^1{\times}3^7)$ orthogonal, including mold temperature, pre-plasticity amount, injection pressure, injection speed, screw speed, packing pressure, packing time and cooling time. Then, the grey relational analysis, whose response table and response graph indicate the optimum processing parameters for multiple quality characteristics, is applied to resolve this drawback. The Taguchi method only takes a single quality characteristic into consideration. Finally, a processing parameter prediction system is established by using the back-propagation neural network. The percentage errors all fall within 2%, between the predicted values and the target values. This reveals that the prediction system established in this study produces excellent results.

• Journal of KSNVE
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• v.10 no.3
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• pp.444-450
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• 2000

Wheel-rail noise is normally classified into three catagories : rolling impact and squeal noise. In this paper rolling noise caused by the irregularity between a wheel and a rail is analysed as follows: The irregularity between the wheel and the rail is assumed as linear superposition of sinusoidal profiles. Wheel-rail contact stiffness is linearized by using Hertzian contact theory and then contact force between the wheel and the rail is calculated. vibration of the rail and the wheel is calculated theoretically by receptance method or FEM depending on the geometry of the wheel or the rail for the frequency range of 100-500 Hz important for noise generation. The radiation noise caused by those vibration response is computed by BEM To verify this analysis tools rolling noise is calculated by proposed analysis steps using typical roughness data and these results are compared with experimental rolling noise data. This analysis tools show reasonable results and finally used for the prediction of the Korean high speed train rolling noise.

• Bulletin of the Korean Space Science Society
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• 2003.10a
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• pp.26-26
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• 2003

During fabrication process for the large space optical surfaces, the traditional bound abrasive grinding with bronze bond cupped diamond wheel tools leaves the machine marks and the subsurface damage to be removed by subsequent loose abrasive lapping. We explored a new grinding technique for efficient quantitative control of precision CNC grinding for space optics materials such as Zerodur. The facility used is a NANOFORM-600 diamond turning machine with a custom grinding module and a range of resin bond diamond tools. The machining parameters such as grit number, tool rotation speed, work-piece rotation speed, depth of cut and feed rate were altered while grinding the work-piece surfaces of 20-100 mm in diameter. The input grinding variables and the resulting surface quality data were used to build grinding prediction models using empirical and multi-variable regression analysis methods. The effectiveness of the grinding prediction model was then examined by running a series of precision CNC grinding operation with a set of controlled input variables and predicted output surface quality indicators. The experiment details, the results and implications are presented.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.1 s.139
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• pp.57-63
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• 2005

This paper introduces basic research of optimum routing assessment system as voyage support purpose which can obtain safe and efficient route. In view point of safety, the prediction of ship motion should be evaluated in the condition of rough weather This part includes general seakeeping estimation based on 3 dimensional panel method and parametric roil prediction. For increasing voyage efficiency, ETA(Estimated Time of Arrival) and fuel consumption should be calculated considering speed reduction and power increase due to wave effects based on added resistance calculation and ship performance characteristics. Basically, the weather forecast is assumed to be prepared previously to operate this system. The idea of these factors in this system will be helpful to escape from dangerous voyage situation by wave conditions and to make optimum route planning based on ETA and fuel consumption.