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

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.626-631
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• 1997

This paper represents the design of a real-time image preprocessing system. The preprocessing system performs hardware-wise mask operations and thresholding operations at the speed of camera output single rate. The preprocessing system consists of the preprocessing board and the main processing board. The preprocessing board includes preprocessing unit that includes a $5\times5$ mask processor and LUT, and can perform mask and threshold operations in real-time. To achieve high-resolution image input data($20485\timesn$), the preprocessing board has a linescan camera interface. The main processing board includes the image processor unit and main processor unit. The image processor unit is equipped with TI's TMS320C32 DSP and can perform image processing algorithms at high speed. The main processor unit controls the operation of total system. The proposed system is faster than the conventional CPU based system.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.417-424
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• 2009

For a construction project to progress smoothly, effective cost estimation is vital, particularly in the conceptual and schematic design stages. In these early phases, despite the fact that initial estimates are highly sensitive to changes in project scope, owners require accurate forecasts which reflect their supplying information. Thus, cost estimators need effective estimation strategies. Practically, parametric cost estimates are the most commonly used method in these initial phases, which utilizes historical cost data (Karshenas 1984, Kirkham 2007). Hence, compilation of historical data regarding appropriate cost variance governing parameters is a prime requirement. However, precedent practice of data mining (data preprocessing) for denoising internal errors or abnormal values is needed before compilation. As an effort to deal with this issue, this research proposed a statistical methodology for data preprocessing and verified that data preprocessing has a positive impact on the enhancement of estimate accuracy and stability. Moreover, Statistically Preprocessed data Based Parametric (SPBP) cost models are developed based on multiple regression equations and verified their effectiveness compared with conventional cost models.

• Journal of Distribution Science
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• v.16 no.12
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• pp.95-104
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• 2018

Purpose - In order to gain the upper hand in competition between container terminals, efforts to improve container terminal productivity continue. Export containers arrive randomly in the container terminal and are carried in the container terminal yard according to the arrival order. On the other hand, containers are carried out of the container terminal yard in order based on container weight, not in order of arrival. Because the carry-in order and the carry-out order are different, rehandling may occur, which reduces the performance of the container terminals. In order to reduce rehandling number, containers can be moved in advance when they arrive, which is called preprocessing. This paper proposes an effective preprocessing algorithm and analyzes the factors that affect the productivity of the container terminals. It also provides a way to choose the best factors for preprocessing for a variety of situations. Research design, data, and methodology - To analyze the impact of factors affecting the performance of preprocessing algorithms presented in this paper, simulations are performed. The simulations are performed for two types of bays, 12 stacks with 8 tiers, and 8 stacks with 6 tiers. Results - The results of the factor analysis that affects the performance of the preprocessing algorithm were as follows. (1) As the LMF increased, preprocessing number increases and rehandling number decreased. (2) The LML effect was greatest when the LML changed from 0 to 1, and that the effect decreased when it changed above 1. (3) The sum of preprocessing number and rehandling number was then shown to be increased after decrease, as the LMF increased. (4) In the case of NCI, a decrease in NCI showed that the containers would become more grouped and thus the performance was improved. (5) There was a positive effect in the case of EFS. Conclusion - In this paper, preprocessing algorithm was proposed and it was possible to choose the best factors for preprocessing for a variety of situations through simulations. Further research related to this study needs to be carried out in the following topic : a study on the improvement of container performance by connecting the preprocessing with remarshalling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5377-5391
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• 2017

In this paper, we propose a new scheme to suppress the narrowband interference (NBI) in OFDM-based systems. The scheme utilizes code division multiple access (CDMA) and weighted-type fractional Fourier transform (WFRFT) domain preprocessing technologies. Through setting the WFRFT order, the scheme can switch into a single carrier (SC) or a multi-carrier (MC) frequency division multiple access block transmission system. The residual NBI can be eliminated to the maximum extent when the WFRFT order is selected properly. Final simulation results show that the proposed system can outperform MC and SC with CDMA and frequency domain preprocessing in terms of the narrowband interference suppression.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.275-276
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• 2007

In this paper, we propose an efficient illumination preprocessing algorithm for face recognition. One of the best known illumination preprocessing method, based on anisotropic smoothing, enhances the edge information, but instead deteriorates the contrast of the original image. Our proposed method reduces the deterioration of the contrast while enhancing the edge information, and thus the preprocessed image does not lose features like Gabor features of the original images much.. The effectiveness of the proposed illumination preprocessing method is verified through experiments of face recognition.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.257-264
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• 2014

This paper is to construction of the data mining preprocessing tool for efficient database marketing. We compare and evaluate the often used data mining tools based on the access method to local and remote databases, and on the exchange of information resources between different computers. The evaluated preprocessing of data mining tools are Answer Tree, Climentine, Enterprise Miner, Kensington, and Weka. We propose a design principle for an efficient system for data preprocessing for data mining on the distributed networks. This system is based on Java technology including EJB(Enterprise Java Beans) and XML(eXtensible Markup Language).

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.1
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• pp.117-129
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• 2008

Make-to-order manufacturers with multiple plants at multiple sites need to have the ability to quickly determine which plant will produce which customer order to meet the due date and minimize the transportation cost from the plants to the customer. Balancing the work loads and minimizing setups and make-span are also of great concern. Solving such scheduling problems usually takes a long time. We propose a new approach, which we call 'preprocessing', for resolving such complex problems. In preprocessing scheme, a 'good' a priori schedule is prepared and maintained using unconfirmed order information. Upon the confirmation of orders. the preprocessed schedule is quickly modified to obtain the final schedule. We present a preprocessing solution algorithm for multi-site constraint scheduling problem (MSCSP) using genetic algorithm; and conduct computational experiments to evaluate the performance of the algorithm.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.100-106
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• 2004

In this paper, we present a novel, rapid approach for the detection of brain tumors and deformity boundaries in medical images using a genetic algorithm with wavelet based preprocessing. The contour detection problem is formulated as an optimization process that seeks the contour of the object in a manner of minimizing an energy function based on an active contour model. The brain tumor segmentation contour, however, cannot be detected in case that a higher gradient intensity exists other than the interested brain tumor and deformities. Our method for discerning brain tumors and deformities from unwanted adjacent tissues is proposed. The proposed method can be used in medical image analysis because the exact contour of the brain tumor and deformities is followed by precise diagnosis of the deformities.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.287-292
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• 2012

In this paper, user personalized services through the emotion perception required to support location-based sensing data preprocessing techniques and emotion data preprocessing techniques is studied for user's emotion data building and preprocessing in V-A emotion model. For this purpose the granular context tree and string matching based emotion pattern matching techniques are used. In addition, context-aware and personalized recommendation services technique using probabilistic reasoning is studied for personalized services based on context awareness.