• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.3
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• pp.227-235
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• 2003

We propose an algorithm for the automatic measurement of concentricity and roundness using image processing technique. The proposed measuring method consists of the preprocessing process and the measuring process. In the measuring process, two types of concentricity measurement algorithm and one type of roundness measurement algorithm are proposed. We could measure the concentricity and roundness using input image from CCD camera, without using special measurement equipment. From the experimental results, we could find that the required measurement accuracy specification is sufficiently satisfied using our proposed method.

• Journal of Industrial Technology
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• v.15
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• pp.235-244
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• 1995

In this paper, we propose a bulk-load algorithm for $B^+-trees$, the most widely used index structures in database systems. The main characteristic of our algorithm is to simultaneously process all the keys to be placed on each $B^+-trees$ page when accessing the page. This avoids the overhead for accessing the same page multiple times, which results from applying the $B^+-trees$ insertion algorithm repeatedly. For performance evaluation, we analyze our algorithm in terms of the number of disk accesses. The results show that the number of disk accesses excluding those in the redistribution process in identical to the number of $B^+-trees$ pages. Considering that the redistribution process is an unavoidable preprocessing step for bulk-loading, our algorithm requires just one disk access per $B^+-trees$ page, and therefore turns out to be optimal. We also present performance tendancy according to the changes of parameter values via simulation.

• Korean Chemical Engineering Research
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• v.50 no.6
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• pp.988-993
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• 2012

Energy and cost analysis of the preprocessing for carbon capture and storage transportation such as supercritical compression and liquefaction is done using chemical simulation model. Direct compression to supercritical phase (process 1-1), liquefaction and pumping (process 1-2), carbon dioxide compression and expansion as a refrigerant itself (process 2), usage of other refrigerant with compression and expansion (process 3-1), with absorption chiller (process 3-2), cascade refrigeration (process 3-2) have been simulated and evaluated. The specific cost is about 4 to 7 $/ton.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.217-224
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• 2012

Location-Based Services(LBS) is a service that provide location information by using communication network or satellite signal. In order to provide LBS precisely and efficiently, we studied how we can reduce the error on location determination of objects such people and things. We focus on using the least square method and triangulation positioning method to improves the accuracy of the existing location determination method. Above two methods is useful if the distance between the AP and the tags can be find. Though there are a variety of ways to find the distance between the AP and tags, least squares and triangulation positioning method are wildely used. In this thesis, positioning method is composed of preprocessing and calculation of location coordinate and detail of methodology in each stage is explained. The distance between tag and AP is adjusted in the preprocessing stage then we utilize least square method and triangulation positioning method to calculate tag coordinate. In order to confirm the performance of suggested method, we developed the test program for location determination with Labview2010. According to test result, triangulation positioning method showed up loss error than least square method by 38% and also error reduction was obtained through adjustment process and filtering process. It is necessary to study how to reduce error by using additional filtering method and sensor addition in the future and also how to improve the accuracy of location determination at the boundary location between indoor and outdoor and mobile tag.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1222-1230
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• 2022

As the number of signal sources with low detectability by using various modulation techniques increases, research to classify signal modulation methods is steadily progressing. Recently, a Convolutional Neural Network (CNN) deep learning technique using FFT as a preprocessing process has been proposed to improve the performance of received signal classification in signal interference or noise environments. However, due to the characteristics of the FFT in which the window is fixed, it is not possible to accurately classify the change over time of the detection signal. Therefore, in this paper, we propose a CNN model that has high resolution in the time domain and frequency domain and uses wavelet transform as a preprocessing process that can express various types of signals simultaneously in time and frequency domains. It has been demonstrated that the proposed wavelet transform method through simulation shows superior performance regardless of the SNR change in terms of accuracy and learning speed compared to the FFT transform method, and shows a greater difference, especially when the SNR is low.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.465-475
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• 2023

In this study, we demonstrated a silica nanofibrous membrane based on the electrospinning process and evaluated its DNA isolation and purification performance in PCR pretreatment. Generally, silica membranes made of non-woven fabric are used for PCR pretreatment, but this study aimed to improve the efficiency of the pretreatment process by developing a nanofiber-type silica membrane with high specific surface area and porosity. In order to manufacture a nanofiber-shaped silica film while maintaining the original physical properties of silica, nanofiber membranes produced by adding various concentrations of PEO (5 wt%, 8 wt%, and 10 wt%) to silica prepared by the sol-gel method were compared. In terms of nanofiber membrane production, the higher the PEO concentration, the more effective it was in producing nanofiber membranes. The produced silica nanofiber membrane was inserted to a pretreatment device used in commercial PCR equipment, and the pretreatment performance was compared and verified using Salmonella bacteria. When Salmonella was used, samples containing 5 wt% PEO showed superior PCR efficiency compared to samples containing 8 wt% and 10 wt% PEO. These results show that adding 5 wt% of PEO can effectively improve DNA purification and separation by producing a nanofiber-shaped silica film while maintaining the physical properties of silica. We expect that this study will contribute to the development of effective PCR pretreatment technology essential for various molecular biology applications.

• Journal of Food Hygiene and Safety
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• v.27 no.2
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• pp.117-126
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• 2012

This study was conducted to assess the levels of microbiological hazards of preprocessed Namuls, which were served at the school foodservice. 19 preprocessed ground or root vegetables were collected from 21 schools in May to June of 2011. Heavy contamination of aerobic plate counts (from 3.39 to 8.42 logCFU/g) and total coliform groups (from 3.16 to 7.84 logCFU/g), enterobacteriaceaes (from 2.53 to 7.55 logCFU/g) were detected in preprocessed Namuls. In addition, the detection rates of Escherichia coli, Staphylococcus aureus and Bacillus cereus (emetic form) were 4.3%, 11.7% and 2.1%, respectively. In addition, sanitary indicative bacterium at preprocessing steps of root vegetables (lotus root, burdock root, bellflower root) and blanched Namuls (bracken, sweet potato vine, chinamul) were analyzed. Aerobic plate counts, coliform groups, and enterobacteriaceaes were not effectively removed during preprocessing including washing and soaking steps. In the case of blanched Namuls (bracken, sweet potato vine, chinamul), contamination levels increased more after drying process and no significant reduction effect on the levels of microbial contamination was observed during preprocessing steps. Thus, effect of preprocessing steps on the microbiological hazards in Namuls must be reevaluated to improve the microbiological quality of preprocessed Namuls at the school foodservice and retail markets.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.481-490
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• 2002

Off-line handwritten character recognition is in difficulty of incomplete preprocessing because it has not dynamic information has various handwriting, extreme overlap of the consonant and vowel and many error image of stroke. Consequently off-line handwritten character recognition needs to study about preprocessing of various methods such as binarization and thinning. This paper considers running time of watershed algorithm and the quality of resulting image as preprocessing for off-line handwritten Korean character recognition. So it proposes application of effective watershed algorithm for segmentation of character region and background region in gray level character image and segmentation function for binarization by extracted watershed image. Besides it proposes thinning methods that effectively extracts skeleton through conditional test mask considering routing time and quality of skeleton, estimates efficiency of existing methods and this paper's methods as running time and quality. Average execution time on the previous method was 2.16 second and on this paper method was 1.72 second. We prove that this paper's method removed noise effectively with overlap stroke as compared with the previous method.