• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1751-1758
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• 2017

This paper presents a fuzzy logic system with data preprocessing to make the accurate electric power load prediction system. The fuzzy logic system acceptably treats the hidden characteristic of the nonlinear data. The data preprocessing processes the original data to provide more information of its characteristics. Thus the combination of two methods can predict the given data more accurately. The former uses TSK fuzzy logic system to apply the linguistic rule base and the linear regression model while the latter uses the linear interpolation method. Finally, four regional electric power load data in taiwan are used to evaluate the performance of the proposed prediction system.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.573-586
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• 2022

Corrosion can cause dangerous and expensive damage and failures of ship hulls and equipment. Therefore, it is necessary to maintain the vessel by periodic corrosion inspections. During visual inspection, many corrosion locations are inaccessible for many reasons, especially safety's point of view. Including subjective decisions of inspectors is one of the issues of visual inspection. Automation of visual inspection is tried by many pieces of research. In this study, we propose image preprocessing methods by image patch segmentation and thresholding. YOLOv5 was used as an object detection model after the image preprocessing. Finally, it was evaluated that corrosion detection performance using the proposed method was improved in terms of mean average precision.

• 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.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.615-622
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• 2014

The Korean film market has rapidly achieved an international scale, and this has led to a need for decision-making based on analytical methods that are more precise and appropriate. In this modern era, a highly advanced information environment can provide an overwhelming amount of data that is generated in real time, and this data must be properly handled and analyzed in order to extract useful information. In particular, the preprocessing of large data, which is the most time-consuming step, should be done in a reasonable amount of time. In this paper, we investigated a big data preprocessing method for predicting movie box office success. We analyzed the movie data characteristics for specialized preprocessing methods, and used the Hadoop MapReduce framework. The experimental results showed that the preprocessing methods using big data techniques are more effective than existing methods.

• Journal of Astronomy and Space Sciences
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• v.19 no.2
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• pp.163-172
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• 2002

This article investigates the problem of data preprocessing for the precision orbit determination (POD) of low earth orbit satellite using GPS .aw data. Several data preprocessing algorithms have been developed to edit the GPS data automatically such that outlier deletion, cycle slip identification and correction, and time tag error correction. The GPS data are precisely edited for the accuracy of POD. Some methods of data preprocessing are restricted to the rate of the collections of the pseudorange and carrier phase measurements. This study considers the preprocessing efficiency varied with the rate, the quality of receiver and the altitude of the satellite's orbit. We also propose the proper methods in accordance with the rate for single frequency and dual frequency receivers.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.374-385
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• 2020

In this paper, we propose a preprocessing technique to solve the problems of action recognition with Equirectangular Projection (ERP) video. The preprocessing technique proposed in this paper assumes the person object as the subject of action, that is, the Object of Interest (OOI), and the surrounding area of the OOI as the ROI. The preprocessing technique consists of three modules. I) Recognize person object in the image with object recognition model. II) Create a saliency map from the input image. III) Select subject of action using recognized person object and saliency map. The subject boundary box of the selected action is input to the action recognition model in order to improve the action recognition performance. When comparing the performance of the proposed preprocessing method to the action recognition model and the performance of the original ERP image input method, the performance is improved up to 99.6%, and the action is obtained when only the OOI is detected. It can also see the effects of related video summaries.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.249-255
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• 2022

Potholes are an important clue to the structural defects of asphalt pavement and cause many casualties and property damage. Therefore, accurate pothole detection is an important task in road surface maintenance. Many machine learning technologies are being introduced for pothole detection, and data preprocessing is required to increase the efficiency of deep learning models. In this paper, we propose a preprocessing method that emphasizes important textures and shapes in pothole datasets. The proposed preprocessing method uses intensity transformation to reduce unnecessary elements of the road and emphasize the texture and shape of the pothole. In addition, the feature of the porthole is detected using Superpixel and Sobel edge detection. Through performance comparison between the proposed preprocessing method and the existing preprocessing method, it is shown that the proposed preprocessing method is a more effective method than the existing method in detecting potholes.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.585-594
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• 2009

Researchers of microarray experiment transpose processed images of raw data to possible data of statistical analysis: it is preprocessing. Preprocessing of microarray has image filtering, imputation and normalization. There have been studied about several different methods of normalization and imputation, but there was not further study on the order of the procedures. We have no further study about which things put first on our procedure between normalization and imputation. This study is about the identification of differentially expressed genes(DEG) on the order of the preprocessing steps using two-dye cDNA microarray in colon cancer and gastric cancer. That is, we check for compare which combination of imputation and normalization steps can detect the DEG. We used imputation methods(K-nearly neighbor, Baysian principle comparison analysis) and normalization methods(global, within-print tip group, variance stabilization). Therefore, preprocessing steps have 12 methods. We identified concordance measure of DEG using the datasets to which the 12 different preprocessing orders were applied. When we applied preprocessing using variance stabilization of normalization method, there was a little variance in a sensitive way for detecting DEG.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.789-793
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• 1990

Preprocessing technique of the measurement bearing data is presented to improve the tar-get estimation accuracy for the bearing-only target notion analysis (TMA). Computer simulation is performed to compare with respect to the extended Kalman filter. By computer simulation, the target filter estimator with preprocessing Is both stable and robust to the measurement bearing noise.