• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.4
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• pp.389-394
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• 2023

In recent years, use of machine automation is rising in the industry. Ships also obtain machine condition information from sensor as digital information. However, on ships, crew members regularly surveil the engine room to check the condition of equipment and their information through analog gauges. This is a time-consuming and tedious process and poses safety risks to the crew while on surveillance. To address this, engine room surveillance using an autonomous mobile robot is being actively explored as a solution because it can reduce time, costs, and the safety risks for crew. Analog gauge reading using an autonomous mobile robot requires digitization for the robot to recognize the gauge value. In this study, image processing techniques were applied to achieve this. Analog gauge images were subjected to image preprocessing to remove noise and highlight their features. The center point, indicator point, minimum value and maximum value of the analog gauge were detected through image processing. Through the straight line connecting these points, the angle from the minimum value to the maximum value and the angle from the minimum value to indicator point were obtained. The obtained angle is digitized as the value currently indicated by the analog gauge through a formula. It was confirmed from the experiments that the digitization of the analog gauge using image processing was successful, indicating the equivalent current value shown by the gauge. When applied to surveillance robots, this algorithm can minimize safety risks and time and opportunity costs of crew members for engine room surveillance.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.83-89
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• 2010

Purpose: The Wide Beam Reconstruction (WBR) algorithms that UltraSPECT, Ltd. (U.S) has provides solutions which improved image resolution by eliminating the effect of the line spread function by collimator and suppression of the noise. It controls the resolution and noise level automatically and yields unsurpassed image quality. The aim of this study is WBR of whole body bone scan in usefulness of clinical application. Materials and Methods: The standard line source and single photon emission computed tomography (SPECT) reconstructed spatial resolution measurements were performed on an INFINA (GE, Milwaukee, WI) gamma camera, equipped with low energy high resolution (LEHR) collimators. The total counts of line source measurements with 200 kcps and 300 kcps. The SPECT phantoms analyzed spatial resolution by the changing matrix size. Also a clinical evaluation study was performed with forty three patients, referred for bone scans. First group altered scan speed with 20 and 30 cm/min and dosage of 740 MBq (20 mCi) of $^{99m}Tc$-HDP administered but second group altered dosage of $^{99m}Tc$-HDP with 740 and 1,110 MBq (20 mCi and 30 mCi) in same scan speed. The acquired data was reconstructed using the typical clinical protocol in use and the WBR protocol. The patient's information was removed and a blind reading was done on each reconstruction method. For each reading, a questionnaire was completed in which the reader was asked to evaluate, on a scale of 1-5 point. Results: The result of planar WBR data improved resolution more than 10%. The Full-Width at Half-Maximum (FWHM) of WBR data improved about 16% (Standard: 8.45, WBR: 7.09). SPECT WBR data improved resolution more than about 50% and evaluate FWHM of WBR data (Standard: 3.52, WBR: 1.65). A clinical evaluation study, there was no statistically significant difference between the two method, which includes improvement of the bone to soft tissue ratio and the image resolution (first group p=0.07, second group p=0.458). Conclusion: The WBR method allows to shorten the acquisition time of bone scans while simultaneously providing improved image quality and to reduce the dosage of radiopharmaceuticals reducing radiation dose. Therefore, the WBR method can be applied to a wide range of clinical applications to provide clinical values as well as image quality.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.