• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.84-92
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• 2011

The automated segmentation technique of cell region in Bio Images helps biologists understand complex functions of cells. It is mightly important in that it can process the analysis of cells automatically which has been done manually before. The conventional methods for segmentation of cell and nuclei from multi-channel images consist of two steps. In the first step nuclei are extracted from DNA channel, and used as initial contour for the second step. In the second step cytoplasm are segmented from Actin channel by using Active Contour model based on intensity. However, conventional studies have some limitation that they let the cell segmentation performance fall by not considering inhomogeneous intensity problem in cell images. Therefore, the paper consider correlation between DNA and Actin channel, and then proposes the preprocessing algorithm by which the brightness of cell inside in Actin channel can be compensated homogeneously by using DNA channel information. Experiment result show that the proposed preprocessing method improves the cell segmentation performance compared to the conventional method.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3902-3909
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• 2021

SACOS series of subchannel analysis codes have been developed by XJTU-NuTheL for many years and are being used for the thermal-hydraulic safety analysis of various reactor cores. To achieve fine whole core pin-level analysis, the input preprocessing and parallel capabilities of the code have been developed in this study. Preprocessing is suitable for modeling rectangular and hexagonal assemblies with less error-prone input; parallelization is established based on the domain decomposition method with the hybrid of MPI and OpenMP. For domain decomposition, a more flexible method has been proposed which can determine the appropriate task division of the core domain according to the number of processors of the server. By performing the calculation time evaluation for the several PWR assembly problems, the code parallelization has been successfully verified with different number of processors. Subsequent analysis results for rectangular- and hexagonal-assembly core imply that the code can be used to model and perform pin-level core safety analysis with acceptable computational efficiency.

• Journal of Information Technology Services
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• v.21 no.4
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• pp.91-103
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• 2022

This study proposes a model for predicting contracted power using electric power data collected in real time from convenience stores nationwide. By optimizing the prediction model using machine learning, it will be possible to predict the contracted power required to renew the contract of the existing convenience store. Contracted power is predicted through the XGBoost regression model. For the learning of XGBoost model, the electric power data collected for 16 months through a real-time monitoring system for convenience stores nationwide were used. The hyperparameters of the XGBoost model were tuned using the GridesearchCV, and the main features of the prediction model were identified using the xgb.importance function. In addition, it was also confirmed whether the preprocessing method of missing values and outliers affects the prediction of reduced power. As a result of hyperparameter tuning, an optimal model with improved predictive performance was obtained. It was found that the features of power.2020.09, power.2021.02, area, and operating time had an effect on the prediction of contracted power. As a result of the analysis, it was found that the preprocessing policy of missing values and outliers did not affect the prediction result. The proposed XGBoost regression model showed high predictive performance for contract power. Even if the preprocessing method for missing values and outliers was changed, there was no significant difference in the prediction results through hyperparameters tuning.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.90-94
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• 2013

Research for Manchu character digitalization is at an early stage. This paper proposes a preprocessing algorithm for Manchu character recognition. This algorithm improves the existing Hilditch thinning algorithm so that it corrects thinning error for Manchu characters. The existing algorithm separates the characters into the left-hand side and right-hand side, while our alogorithm uses the central point between the points that strokes exist when it classifies each of characters. The experimentation results show that this method is valid for thinning and classification of Manchu characters.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.625-631
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• 2010

This paper proposes lane recognition algorithm using lane prominence technique to extract lane candidate. The lane prominence technique is combined with embossing effect, lane thickness check, and lane extraction using mask. The proposed lane recognition algorithm consists of preprocessing, lane candidate extraction and lane recognition. First, preprocessing is executed, which includes gray image acquisition, inverse perspective transform and gaussian blur. Second, lane candidate is extracted by using lane prominence technique. Finally, lane is recognized by using hough transform and least square method. To evaluate the proposed lane recognition algorithm, this algorithm was applied to the detection of lanes in the rainy and night day. The experiment results showed that the proposed algorithm can recognize lane in various environment. It means that the algorithm can be applied to lane recognition to drive unmanned vehicles.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.27-37
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• 1991

A recognition algorithm for Hangul is developed by structural analysis to Hangul in this theses. Four major procedures are proposed : preprocessing, type classification, separation of consonant and vowel, recognition. In the preprocessing procedure, the thinning algorithm proposed by CHEN & HSU is applied. In the type classification procedure, thinned Hangul image is classified into one of six formal types. In the separation of consonant and vowel procedure, starting from branch-points which are existed in a vowel, character elements are separated by means of tracing branch-point pixel by pixel and comparison with proposed templates. In the same time, the vowels are recognized. In the recognition procedure, consonants are extracted from the separated Hangul character and recognized by modified Crossing method. Recognized characters are converted into KS-5601-1989 codes. The experiments show that correct recognition rate is about 80%-90% and recognition speed is about 2-3 character persecond in three types of different input data on computer with 80386 microprocessor.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.959-961
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• 1995

The objective of most image proccessing applications is to extract meaningful information from one or more pictures. It is accomplished efficiently using neural networks, which is used in image classification and image recognition. In neural networks, background and meaningful information are processed with same weight in input layer. In this paper, we propose the image classification method using neural networks, especially EBP(Error Back Propagation). Preprocessing is needed. In preprocessing, background is compressed and meaningful information is emphasized. We use the quadtree approach, which is a hierarchical data structure based on a regular decomposition of space.