• Journal of Korea Multimedia Society
• /
• v.20 no.2
• /
• pp.144-152
• /
• 2017

Among the technologies for implementing autonomous vehicles, advanced driver assistance system is a key technology to support driver's safe driving. In the technology using the vision sensor having a high utility, various preprocessing methods are used prior to feature extraction for lane detection. However, in the existing methods, the unnecessary lane candidates such as cars, lawns, and road separator in the road area are false positive. In addition, there are cases where the lane candidate itself can not be extracted in the area under the overpass, the lane within the dark shadow, the center lane of yellow, and weak lane. In this paper, we propose an efficient preprocessing method using k-means clustering for image division and the HSV color model. When the proposed preprocessing method is applied, the true positive region is maximally maintained during the lane detection and many false positive regions are removed.

• Journal of Information Technology Services
• /
• v.7 no.2
• /
• pp.127-135
• /
• 2008

The concept of preprocessing is widely used in various computer science area such as compiler and software engineering for the purpose of macro processing and optimization. In addition, preprocessing is also used in SAT solvers in order to eliminate redundant literals and clauses to speed up its solving time before searching the state space. However, there is an unexpected run-time error such as stack-overflow during this step, in case the size of a given set of clauses is huge which impedes SAT solvers. In this case, the preprocessing should be applied at the encoding time to optimize its size. In this paper this idea is applied to several Sudoku problems. As a result, significant improvements are obtained with respect to the number of variables and clauses as well as the solving time compared to the previous works.

• Speech Sciences
• /
• v.10 no.4
• /
• pp.125-136
• /
• 2003

Speech coding techniques have been studied to reduce the complexity and bit rate but also to improve the sound quality. CELP type vocoder, has used as a one of standard, supports the great sound quality even low bit rate. In this paper, the preprocessing of input speech to reduce the bit rate is the different with the conventional vocoder. The different kinds of parameter are used for the preprocessing so this paper is compared with theses parameters for finding the more appropriate parameter for the vocoder. The parameters are used to synthesize the speech not to encode or decode for coding technique so we proposed the simple algorithm not to have the influence on the processing time or the computation time. The parameters in used the preprocessing step are speaking rate, duration and PSOLA technique.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.7 no.2 s.17
• /
• pp.22-31
• /
• 2004

In this paper, the measurements preprocessing algorithm for the fading of bearing and frequency measurements is proposed, which can improve the performance of BFTMA(Bearing and Frequency Target Motion Analysis). The fading and detection relation between bearing and frequency are rigorously established for measurements preprocessing, and BFTMA can be carried out the estimation of target motion by using measurements preprocessing. Batch estimation with bearing and frequency using the proposed algorithm can be applied to estimate the initial target states despite of the fading of frequency measurement. Simulation results show that BFTMA using the proposed measurements preprocessing has superior estimation performance, compared with batch estimation using only bearing measurements.

• Journal of the Korean Society of Industry Convergence
• /
• v.26 no.5
• /
• pp.785-792
• /
• 2023

Because hyperspectral imaging provides detailed spectral information across a broad range of wavelengths, it can be utilized in numerous applications, including environmental monitoring, food quality inspection, medical diagnosis, material identification, art authentication, and crime scene analysis. However, hyperspectral images often contain various types of distortions due to the environmental conditions during image acquisition, which necessitates the proper removal of these distortions through a data preprocessing process. In this study, a preprocessing method was investigated to effectively correct the distortion caused by artificial light sources used in indoor hyperspectral imaging. For this purpose, a halogen-tungsten artificial light source was installed indoors, and hyperspectral images were acquired. The acquired images were then corrected for distortion using a preprocessing that does not require complex auxiliary equipment. After the corrections were made, the results were analyzed. According to the analysis, a statistical transformation technique using mean and standard deviation with reference to a reference signal was found to be the most effective in correcting distortions caused by artificial light sources.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.1
• /
• pp.15-27
• /
• 2013

While the vehicle detector is collected from a variety of information was mainly used as a real-time data. Recently scheme of application for archived traffic data has become increasingly important. In this background, this research were conducted on the improvement of the preprocessing for archived traffic data application. The purpose of improving specific preprocessing was reflect transportation phenomena by traffic data. As evaluation result, improvement preprocessing was close to the actual value than exist preprocessing.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.105-108
• /
• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 1990.10a
• /
• pp.285-288
• /
• 1990

• Journal of the Korean Society for Nondestructive Testing
• /
• v.22 no.4
• /
• pp.361-368
• /
• 2002

The aim of this study was to investigate the effect of preprocessing the transmitted energy spectrum data on development of a robust model to predict the sugar content in intact apples. The spectrum data were measured from 120 Fuji apple samples conveying at the speed of 2 apples per second. Computer algorithms of preprocessing methods such as MSC, SNV, first derivative, OSC and their combinations were developed and applied to the raw spectrum data set. The results indicated that correlation coefficients between the transmitted energy values at each wavelength and sugar contents of apples were significantly improved by the preprocessing of MSC and SNV in particular as compared with those of no-preprocessing. SEPs of the prediction models showed great difference depending on the preprocessing method of the raw spectrum data, the largest of 1.265%brix and the smallest of 0.507% brix. Such a result means that an appropriate preprocessing method corresponding to the characteristics of the spectrum data set should be found or developed for minimizing the prediction errors. It was observed that MSC and SNV are closely related to prediction accuracy, OSC is to number of PLS factors and the first derivative resulted in decrease of the prediction accuracy. A robust calibration model could be d3eveloped by the combined preprocessing of MSC and OSC, which showed that SEP=0.507%brix, bias=0.0327 and R2=0.8823.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.15 no.6
• /
• pp.1-7
• /
• 2006

This paper presents the study in the development of optimal working method for an outer-hull preprocessing robot using a quaternion. The out-hull preprocessing robot consists of feathering and cleaning parts. This robot should be controlled correctly for feathering work because it is to be worked on a curved plate that can result in the errors of orientation. In this paper, we propose a control algorithm between given two orientations of the out-hull preprocessing robot by using a quaternion with spherical linear interpolation. The proposed control algorithm is shown to be effective in terms of motor angles and torques when compared to a conventional Euler angle interpolation, by using both $MATLAB^{\circledR}$ and $VisualNastran4D^{\circledR}$.