• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.40-45
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• 2018

Intelligent monitoring and diagnosis of production processes based on multivariate statistical methods has been one of important tasks for safety and quality issues. This is due to the fact that faults and unexpected events may have serious impacts on the operation of processes. This study proposes a diagnostic scheme based on effective representation of process measurement data and is evaluated using simulation process data. The effects of utilizing a preprocessing step and nonlinear statistical methods are also tested using fifteen faults of the simulation process. Results show that the proposed scheme produced more reliable results and outperformed other tested schemes with none of the filtering step and nonlinear methods. The proposed scheme is expected to be robust to process noises and easy to develop due to the lack of required rigorous mathematical process models or expert knowledge.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.359-368
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• 1996

This paper introduces PGD(Preprocessed Givens Downdating)and PHD(Preprocessed Hyperbolic Downdating) algorithms, wherein a multiple-row observation matrix $Z^T$ is factorized into a partial Cholesky factor Rz, such that $Z^T$ = $Q_zR_z, Q_zQ^T_z=I$, and then Rz is recursively downdated by using GD(Givens Downdating)and HD(Hyperbolic Dondating), respectively. Time complexities of PGD and PHD algorithms are $pn^2$ ＋ $5n^3/6$ 및 $pn^2$＋$n^3/3$ flops, respectively, if p$\geq$n, while those of the existing GD and HD are known to be $5pn^2/2$ and $2pn^2$ flops,, respectively. This concludes that the factorization of observation matrices, which we call preprocessing, would improve the overall performance of the downdating process. Benchmarks on the Sun SPARC/2 system also show that preprocessing would shorten the required downdating times compared to those of downdatings without preprocessing. Furthermore, benchmarks also show that PHD provides better performance than PGD.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.687-692
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• 2024

The energy crisis is emerging as a serious problem around the world. In the case of Korea, there is great interest in energy efficiency research related to industrial complexes, which use more than 53% of total energy and account for more than 45% of greenhouse gas emissions in Korea. One of the studies is a study on saving energy through sharing facilities between factories using the same utility in an industrial complex called a virtual energy network plant and through transactions between energy producing and demand factories. In such energy-saving research, data collection is very important because there are various uses for data, such as analysis and prediction. However, existing systems had several shortcomings in reliably collecting time series data. In this study, we propose an intelligent IIoT platform to improve it. The intelligent IIoT platform includes a preprocessing system to identify abnormal data and process it in a timely manner, classifies abnormal and missing data, and presents interpolation techniques to maintain stable time series data. Additionally, time series data collection is streamlined through database optimization. This paper contributes to increasing data usability in the industrial environment through stable data collection and rapid problem response, and contributes to reducing the burden of data collection and optimizing monitoring load by introducing a variety of chatbot notification systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.217-220
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• 2007

In this paper, we present a new anchor shot detection system which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) anchor shot detection module using a support vector data description. According to our computer experiments, the proposed system shows not only the comparable accuracy to the recent other results, but also more faster detection rate than others.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.147-152
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• 2003

Aerial Images are usually used to extract 3-D coordinates of various urban features. In this process, the stereo matching of images should be performed precisely to extract these information from aerial Images. In this research, we proposed a matching technique based on geometric features of lanes. We extracted lanes from aerial images and grouped into 4 lane's types. They are lane lines, dotted lines, arrow lane, safety zone. After preprocessing, We will match them by spatial relationships, for example, the distance and orientation between the extracted features. In the future, we will obtain lane coordinates and reconstruct 3-d coordinates of roads.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.580-582
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• 1999

본 논문에서는 리던던트 웨이블릿 변환을 이용한 지형메쉬(terrain mesh)의 간략화 방법을 제안한다. 기존의 MRA(Multi-Resolution Analysis) 기법을 이용한 지형메쉬의 간략화 기법은 처리과정에서 다운샘플링이 일어나게 되어 지형 데이터 중요한 특성점을 추출할 때 부분적으로 손실된 데이터를 이용한다는 단점을 갖는다. 이 논문은 전처리단계(preprocessing process)에서 지형메쉬의 간략화를 목표로 하여, 지형의 중요점 추출과정에서 리던던트 웨이블릿 변환기법을 이용하며 지형의 중요점 추출의 정확도를 높이고, 지형데이터가 갖는 중복성 (redundancy)을 제거하여 방대한 지형데이터를 간략화하는 방법을 제안한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.59-63
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• 2013

In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.143-148
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• 1999

This paper presents a method which enables raster maps to be used like vector maps in the wed environment and develops a raster map viewer which can be executed in the wed browser. Through the preprocessing process the coordinates attached to each object in the raster map can be used as vector information. The raster map viewer makes the spatial analysis possible using the attribute database connected to the coordinates of each object, This map viewer makes the Web GIS can be constructed at the lower cost because the viewer uses the characteristics of raster map. And the map viewer has merit point of easily developing a component for spatial analysis.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.193-198
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• 2006

Among 242 Hyperion bands, there are 46 bands that contain completely no information and some other bands with various kinds of noise. It is mainly due to the atmosphenc absorption and the low signal-to-noise ratio. The visual inspection for selecting clean and stable bands is a simple practice, but is a manual, inefficient, and subjective Process. Though uncalibrated, overlapping, and all deep water absorption bands are removed, there still exist noisy bands. In this paper, we propose that the extrema ratio be measured for noise estimation and the unsupervised band selection be performed using the Expectation-Maximization algorithm. The Hyperion data were classified into 5 categories according to the image quality by visual inspection, and used as the reference data. The accuracy of the proposed method was compared with signal-to-noise ranking and entropy ranking. As a result, the proposed mettled was effective as preprocessing step for band selection.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.709-712
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• 2005

Ground processing for geostationary weather satellite such as GOES, MTSAT includes the process called image navigation. Image navigation means the retrieval of satellite navigational parameters from images and requires landmark detection by matching satellite images against landmark chips. For an automated preprocessing, a matching must be performed automatically. However, if match results contain errors, the accuracy of image navigation deteriorates. To overcome this problem, we propose the use of a robust estimation technique, called Random Sample Consensus (RANSAC), to automatically detect mismatches. We tested GOES-9 satellite images with 30 landmark chips. Landmark chips were extracted from the world shoreline database. To them, matching was applied and mismatch results were detected automatically by RANSAC. Results showed that all mismatches were detected correctly by RANSAC with a threshold value of 2.5 pixels.