• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.507-515
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• 2015

This paper suggests 6 cases of TFA parameters algorithm(Mean, VA, RS, SKEW, UN, EN) to search for the detection of recognition rates regarding breast disease using CAD on ultrasound images. Of the patients who visited a university hospital in Busan city from August 2013 to January 2014, 90 cases of breast ultrasound images based on the findings in breast US and pathology were selected. $50{\times}50$ pixel size ROI was selected from the breast US images. After pre-processing histogram equalization of the acquired test images(negative, benign, malignancy), we calculated results of TFA algorithm using MATLAB. As a result, in the TFA parameters suggested, the disease recognition rates for negative and malignancy was as high as 100%, and negative and benign was approximately 83~96% for the Mean, SKEW, UN, and EN. Therefore, there is the possibility of auto diagnosis as a pre-processing step for a screening test on breast disease. A additional study of the suggested algorithm and the responsibility and reproducibility for various clinical cases will determine the practical CAD and it might be possible to apply this technique to range of ultrasound images.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.689-699
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• 2022

Recently, high productivity capabilities have been improved due to the application of advanced technologies in various industries, but in the construction industry, productivity improvements have been relatively low. Research on advanced technology for the construction industry is being conducted quickly to overcome the current low productivity. Among advanced technologies, 3D scan technology is widely used for creating 3D digital terrain models at construction sites. In particular, the 3D digital terrain model provides basic data for construction automation processes, such as earthwork machine guidance and control. The quality of the 3D digital terrain model has a lot of influence not only on the performance and acquisition environment of the 3D scanner, but also on the denoising, registration and merging process, which is a preprocessing process for creating a 3D digital terrain model after acquiring terrain scan data. Therefore, it is necessary to improve the terrain scan data processing performance. This study seeks to solve the problem of density inhomogeneity in terrain scan data that arises during the pre-processing step. The study suggests a 'pixel-based point cloud comparison algorithm' and verifies the performance of the algorithm using terrain scan data obtained at an actual earthwork site.

• Korean Journal of Food Science and Technology
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• v.43 no.1
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• pp.98-104
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• 2011

The storage quality of fresh buckwheat sprouts, as influenced by pretreatment and packaging within processing steps, was investigated to establish appropriate postharvest handling treatment for the commodity. After harvest, the sprouts were dipped in chlorine water (100 ppm), rinsed twice with clean water, pre-cooled with iced water, de-watered, and packed in plastic trays. Sprout samples taken from each processing step were stored at $5^{\circ}C$ for 6 days to measure quality attributes. Viable cell counts of mesophilic aerobes and coliform bacteria were lower by about 1 log scale in the postharvest treated samples compared to an untreated control, although the initial microbial reduction due to the postharvest treatments was offset by cell growth during storage. All sprout samples showed a decrease of fresh weight by approximately 4% after 6 days of storage. However, moisture and soluble solid contents were maintained at the initial levels of the sprouts. No significant difference in surface color was observed among sample treatments. For sensory properties including discoloration, wilting, decay, and visual quality, there were no significant differences among sample treatments. The present results suggest that proper postharvest processing treatments can exert positive effects on extending the shelf-life of fresh buckwheat sprout.

• Journal of Mushroom
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• v.21 no.4
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• pp.222-227
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• 2023

Mushrooms have a unique taste and aroma, so in the processing of mushroom products with other ingredients, a separate pre-processing step is often taken to eliminate the mushroom aroma. In this study, we analyzed the changes in the concentration of volatile compounds according to drying conditions to promote the activation of processing using the fruiting bodies of yellow oyster mushrooms(Pleurotus citrinopileatus) and pink oyster mushrooms(P. djamor). The caps and stipes of yellow oyster and pink oyster mushrooms were separated and freeze-dried at -70℃ for 120 hours. Subsequently, they were hot air-dried at temperatures of 40, 50, 60, and 70℃ for 24, 24, 16, and 12 hours, respectively. The dried samples were pulverized and quantitatively analyzed by SPME-GC-MS. In the case of yellow oyster mushrooms, the concentration of t-2-nonenal in caps and stipes during freeze-drying was 164.43 ㎍/g d.w. and 174.80 ㎍/g d.w., respectively, whereas during hot air-drying, it significantly decreased to 0.35~3.41 ㎍/g d.w. and 0.98~59.88 ㎍/g d.w. In a similar manner, for pink oyster mushrooms, the concentration of 1-octen-3-ol during freeze-drying in caps and stipes was 31.05 ㎍/g d.w. and 176.17 ㎍/g d.w., respectively, whereas during hot air-drying, it significantly decreased to 1.59~9.66 ㎍/g d.w. and 1.96~15.77 ㎍/g d.w. Furthermore, most volatile compounds showed a tendency to decrease in concentration as the temperature during hot air-drying increased.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.177-187
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• 2013

Association rule mining is a popular data mining technique with a wide range of application domains, and aims to extract the cause-and-effect relations between the discrete items included in transaction data. However, analysts sometimes have trouble in interpreting and using the plethora of association rules extracted from a large amount of data. To address this problem, this paper aims to propose a novel approach called HTM for extracting the interesting association rules from given transaction data. The HTM approach consists of three main steps, hierarchical clustering, table-view, and mosaic plot, and each step provides the analysts with appropriate visual representation. For illustration, we applied our approach for analyzing the mass health examination data, and the result of this experiment reveals that the HTM approach help the analysts to find the interesting association rules in more effective way.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.1-10
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• 2010

In this paper, we propose a novel way of combining color demosaicing and the auto white balance (AWB) method, which are important parts of image processing. Performance of the AWB is generally affected by demosaicing results because most AWB algorithms are performed posterior to color demosaicing. In order to increase the performance and efficiency of the AWB algorithm, the color constancy problem is considered during the color demosaicing step. Initial estimates of the directional luminance and chrominance values are defined for estimating edge direction and calculating the AWB gain. We propose a modified edge-based AWB method that used a pre-defined achromatic region. The estimation of edge direction is performed region adaptively by using the local statistics of the initial estimates of the luminance and chrominance information. The proposed method shows significant improvements in terms of visual and numerical criteria when compared to conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.61-69
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• 2007

This paper proposes new preprocessing algorithm to extract minutiae in the process of fingerprint recognition. Fingerprint images quality enhancement is a topic phase to ensure good performance in a topic phase to ensure good performance in a Automatic Fingerprint Identification System(AFIS) based on minutiae matching. This paper proposes an algorithm to improve fingerprint image preprocessing to extract minutiae accurately based on directional filter. We improved the suitability of low quality fingerprint images to better suit fingerprint recognition by using valid ridge vector and ridge probability of fingerprint images. With the proposed fingerprint improvement algorithm, noise is removed and presumed ridges are more clearly ascertained. The algorithm is based on five step: computation of effective ridge vector, computation of ridge probability, noise reduction, ridge emphasis, and orientation compensation and frequency estimation. The performance of the proposed approach has been evaluated on two set of images: the first one is self collected using a capacitive semiconductor sensor and second one is DB3 database from Fingerprint Verification Competition (FVC).

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.10
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• pp.499-506
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• 2001

Terrain Following means that a mobile object, such a user's avatar, must follow terrain, remaining in contact with the ground at all times in virtual environments. This makes a virtual environment have the effects of gravity. Terrain Following is often done using collision detection: however this is inefficient, because general collision detection solves a problem that is inherently more complex than merely determining terrain contact points. Many virtual environments avoid the expense by utilizing a flat terrain with a constant altitude everywhere. This makes a terrain following trivial, but lacks realism. This paper provides as algorithm and a data structure for a terrain following using a neighboring area search as a way to search neighboring polygons. Because this algorithm uses a pre-processing step that stores the terrain polygons for calculating, it results in reducing overheads to workstations that is used to construct and maintain a virtual environment. Consequently, workstation can be used to apply not only a terrain following but also other things.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.465-472
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• 2003

For an effective pre-treatment process of a face input image, it is necessary to detect each of face components, calculate the face area, and estimate the rotary angle of the face. A proposed method of this study can estimate an robust result under such renditions as some different levels of illumination, variable fate sizes, fate rotation angels, and background color similar to skin color of the face. The first step of the proposed method detects the estimated face area that can be calculated by both adapted skin color Information of the band-wide HSV color coordinate converted from RGB coordinate, and skin color Information using histogram. Using the results of the former processes, we can detect a lip area within an estimated face area. After estimating a rotary angle slope of the lip area along the X axis, the method determines the face shape based on face information. After detecting eyes in face area by matching a partial template which is made with both eyes, we can estimate Y axis rotary angle by calculating the eye´s locations in three dimensional space in the reference of the face area. As a result of the experiment on various face images, the effectuality of proposed algorithm was verified.