• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.4-49
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• 2001

Digit recognition based on backpropagation neural networks, as an important application of pattern recognition, was attracted much attention. Although it has the advantages of parallel calculation, high error-tolerance, and learning capability, better recognition effects can only be achieved with some specific fixed format input of the digit image. Therefore, digit image preprocessing ability directly affects the accuracy of recognition. Here using Matlab software, the digit image was enhanced by resizing and neutral-rotating the extracted digit image, which improved the digit recognition capability of the backpropagation neural network under practical conditions. This method may also be helpful for recognition of other patterns with backpropagation neural networks.

• 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.

• Annual Conference of KIPS
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• 2017.04a
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• pp.787-790
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• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.

• Journal of the Korea Society of Computer and Information
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• v.24 no.4
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• pp.51-56
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• 2019

In this paper, we propose an algorithm that can quickly generate and synthesize city maps in racing games. Racing games are characterized by moving a wide map rather than a fixed map, but designing and developing a wide map requires a lot of production time. This problem can be mitigated by creating a large map in the preprocessing step, but a fixed map makes the game tedious. It is also inefficient to process all the various maps in the preprocessing step. In order to solve this problem, we propose a technique to create a terrain pattern in the preprocessing process, to generate a map in real time, and to synthesize various maps randomly. In addition, we reduced unnecessary rendering computations by integrating view-dependent techniques into the proposed framework. This study was developed in Unity3D and can be used for various contents as well as racing game.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.69-76
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• 1998

Due to the drastic improvement of computer hardware and operating system, it is easy to break through the main defects of limited computer memory and processing time, etc. To keep up with this situation, this paper is focused on developing the preprocessor program with the input method based on vector graphic editor and the preprocessing technique including automatic node generation algorithm for the $\rho$-version finite element program. To develop this preprocessor program, the special data structure and the OOP(Object Oriented Programming) have been used by the Visual Basic 4.0. The Special data structure is proposed to describe the geometric data of node numberings and coordinates suitable for the $\rho$-version finite element program, which are quite different from the comvential h-version finite element program.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.745-747
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• 1995

A new digital method of anti-aliasing is presented and is applied to the development of an electric vehicle controller. A layered processing structure and some finite-bit approximation technique, devised in this paper, are the key attributions to the design and implementation of the anti-aliasing filter. The performance of the implemented preprocessing system is tested with several experimental results.

• The Journal of Bigdata
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• v.8 no.1
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• pp.61-71
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• 2023

With the development of data-driven decision-making and sophisticated big data processing technique, there is a growing demand for information on how to process data. However, recent studies with data preprocessing mentioned only as a means to achieve a result. Therefore, in this study, we aimed to write in detail about the data processing pipeline, include preprocessing data. In particular, we shares the context and domain knowledge to aid fluent understand of the research.

• Journal of Biosystems Engineering
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• v.27 no.4
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• pp.341-348
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• 2002

Quality evaluation of red ginsengs is determined by outer shape and inner qualities. Especially, the inner qualities are main grading criteria. Currently, red ginsengs are classified into 3-grades; heaven, earth and good. The best heaven grade must not include inner holes and sponge tissues. This study was conducted to develop a red ginseng sorting system using x-ray image processing technique. Because of lens characteristic, gray values of the central region in the x-ray image are higher and gradually decreased towards the edge regions. This difference of gray values gives trouble in segmentation and detection of inner holes in red ginseng image, so preprocessing technique is necessary. The preprocessing was done by subtracting source image from an empty background image. But, simple subtraction was not quite appropriate because of too small contrast between inner holes and sound part. Scaled subtraction images were obtained by multiplying all gray values by some numbers. However this method could not help to set threshold value because the gray values of root part are generally lower than body part when red ginseng is exposed to the x-ray. To determine threshold value for detecting inner holes, an algorithm was developed by increasing overall gray values of less clear images.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.79-85
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• 2019

Recently, small radar require the development of small millimeter wave radar with high distance resolution to disable the target's system with a single strike. Small millimeter wave radar with high distance resolution need to process large amounts of data in real time to acquire and track target. In this paper, we summarized the real-time data preprocessing method to process the large amount of data required for small millimeter wave radar. In addition, the digital IF(Intermediate Frequency) receiver, Window processing, and, DFT(Discrete Fourier Transform) functions presented by real-time data preprocessing are implemented using FPGA(Field Programmable Gate Array). Finally the implemented real-time data preprocessing module was applied to the signal processor for small millimeter wave radar and verified by performance test related to the real-time preprocessing function.

• 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.