• Korean Journal of Fisheries and Aquatic Sciences
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• v.52 no.5
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• pp.461-467
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• 2019

Several studies investigating the prevention and treatment of external parasites in farmed olive flounder Paralichthys olivaceus have found that the anticoccidial agent toltrazuril sulfone is an effective antiparasitic. Prior to undertaking a full-scale study, we developed analytical methods to detect the levels of toltrazuril and toltrazuril sulfone in farmed flounder samples using liquid chromatography-electrospray ionization tandem mass spectrometry (LC-MS/MS). This analysis showed that LC-MS/MS changed the mobile phase and collision energy of toltrazuril and toltrazuril sulfone. This was validated using established conditions. Sample pre-treatment for this process involved extraction with dichloromethane and purification by liquid-liquid extraction in formic acid, acetonitrile, and h-hexane, followed by determination of all compounds by LC-MS/MS. Separation was achieved within 10 min by gradient elution using a Capcell Pak C18 ($3.0{\mu}m$, $100{\times}2.0mm$) analytical column (Shiseido UG 120V) with a mixture of 0.1% (v/v) formic acid and acetonitrile. Multiple reaction monitoring was used for selective detection of toltrazuril and toltrazuril sulfone. This method yields satisfactory results for linearity, precision, and limits of quantification. Therefore, the method established in our study will serve as a basis for further research on parasite control by toltrazuril and toltrazuril sulfone.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.475-491
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• 2021

With the recent increase in available high-resolution (< ~1 m) satellite SAR images, the demand for precise registration of SAR images is increasing in various fields including change detection. The registration between high-resolution SAR images acquired in different look angle is difficult due to speckle noise and geometric distortion caused by the characteristics of SAR images. In this study, registration is performed in two stages, coarse and fine, using the x-band SAR data imaged at staring spotlight mode of TerraSAR-X. For the coarse registration, a method combining the adaptive sampling method and SAR-SIFT (Scale Invariant Feature Transform) is applied, and three rigid methods (NCC: Normalized Cross Correlation, Phase Congruency-NCC, MI: Mutual Information) and one non-rigid (Gefolki: Geoscience extended Flow Optical Flow Lucas-Kanade Iterative), for the fine registration stage, was performed for performance comparison. The results were compared by using RMSE (Root Mean Square Error) and FSIM (Feature Similarity) index, and all rigid models showed poor results in all image combinations. It is confirmed that the rigid models have a large registration error in the rugged terrain area. As a result of applying the Gefolki algorithm, it was confirmed that the RMSE of Gefolki showed the best result as a 1~3 pixels, and the FSIM index also obtained a higher value than 0.02~0.03 compared to other rigid methods. It was confirmed that the mis-registration due to terrain effect could be sufficiently reduced by the Gefolki algorithm.

• Journal of Advanced Navigation Technology
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• v.22 no.2
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• pp.49-56
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• 2018

As the energy generated by earthquake, tsunami, etc. propagates through the air and disturbs the electron density in the ionosphere, the perturbation can be detected by analyzing the ionospheric delay in satellite signal. The electron density in the ionosphere is affected by various factors such as solar activity, latitude, season, and local time. To distinguish from the anomaly, therefore, it is required to inspect the normal trend of the ionosphere. Also, as the perturbation magnitude diminishes by distance it is necessary to develop an appropriate algorithm to detect long-distance disturbances. In this paper, normal condition ionosphere trend is analyzed via IONEX data. We selected monitoring value that has no tendency and developed an algorithm to effectively detect the long-distance ionospheric disturbances by using the lasting characteristics of the disturbances. In the end, we concluded the $2^{nd}$ derivative of ionospheric delay would be proper monitoring value, and the false alarm with the developed algorithm turned out to be 1.4e-6 level. It was applied to 2011 Tohoku earthquake case and the ionospheric disturbance was successfully detected.

• Journal of Environmental Health Sciences
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• v.48 no.2
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• pp.86-95
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• 2022

Background: As high concentrations of uranium and radon have been detected in some areas in Korea, it is considered necessary to investigate natural radioactive materials in the Gwangju area. Objectives: This study aimed to identify the hydrochemical characteristics of groundwater in Gwangju and investigate the distribution characteristics of uranium and radon, which are naturally radioactive substances. Methods: To determine the uranium and radon concentrations in groundwater according to the geology of the Gwangju area, we measured 62 groundwater wells. A geological distribution map of uranium and radon content was prepared for this study. Results: The groundwater type, defined using a Piper diagram, was mainly Ca-HCO₃. The concentration of uranium in the groundwater ranged from 0 to 29.3 ㎍/L, with a mean of 3.3 ㎍/L and a median of 0.9 ㎍/L. The median concentration of uranium in groundwater was highest in alluvium, granitic gneiss, and biotite granite (classified by geological unit), in that order. The concentration of radon in the groundwater ranged from 4.8 to 313.2 Bq/L, with a mean of 75.6 Bq/L and a median of 59.6 Bq/L. The median concentration of radon in groundwater was highest in biotite granite, alluvium, and granitic gneiss, in that order. As a result of the correlation analysis of groundwater in the study area, there was no significant correlation between uranium and radon. Conclusions: In this study area, uranium was shown to be far below the concentrations allowed by drinking water quality standards, but radon concentrations exceeded drinking water quality monitoring standards in 11% of the samples. It was judged that appropriate measures, such as the installation of radon reduction facilities, will be required after a thorough review of high-concentration radon detection sites of in the research area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.91-108
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• 2022

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.67-67
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• 2003

It is crucial to minimize setup errors of a cancer treatment machine using a high energy and to perform precise radiation therapy. Usually, port film has been used for verifying errors. The Korea Cancer Center Hospital (KCCH) has manufactured digital electronic portal imaging device (EPID) system to verify treatment machine errors as a Quality Assurance (Q.A) tool. This EPID was consisted of a metal/fluorescent screen, 45$^{\circ}$ mirror, a camera and an image grabber and could display the portal image with near real time KIRAMS has also made the acrylic phantom that has lead line of 1mm width for ligh/radiation field congruence verification and reference points phantom for using as an isocenter on portal image. We acquired portal images of 10$\times$10cm field size with this phantom by EPID and portal film rotating treatment head by 0.3$^{\circ}$, 0.6$^{\circ}$ and 0.9$^{\circ}$. To check field size, we acquired portal images with 18$\times$18cm, 19$\times$19cm and 20$\times$20cm field size with collimator angle 0$^{\circ}$ and 0.5$^{\circ}$ individually. We have performed Flatness comparison by displaying the line intensity from EPID and film images. The 0.6$^{\circ}$ shift of collimator angle was easily observed by edge detection of irradiated field size on EPID image. To the extent of one pixel (0.76mm) difference could be detected. We also have measured field size by finding optimal threshold value, finding isocenter, finding field edge and gauging distance between isocenter and edge. This EPID system could be used as a Q.A tool for checking field size, light/radiation congruence and flatness with a developed video based EPID.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.196-201
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• 2023

In this study, a CNT fiber flexible electrode grafted with CuO nanoparticles (CuO NPs) and polyaniline (PANI) was developed and applied to a nonenzymatic electrochemical sensor for H₂O₂ detection. CuO NPs/PANI/CNT fiber electrode was fabricated through the synthesis and deposition of PANI and CuO NPs on the CNT fiber surface using an electrochemical method. Surface morphology and elemental composition of the CuO NPs/PANI/CNT fiber electrode were characterized by scanning electron microscope with energy dispersive X-ray spectrometry. And its electrochemical characteristics were investigated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA). Compared with a bare CNT fiber as a control group, the CuO NPs/PANI/CNT fiber electrode showed a 4.78-fold increase in effective surface area and a 8.33-fold decrease in electron transfer resistance, which leads to excellent electrochemical properties such as a good electrical conductivity and an efficient electron transfer. These improved characteristics were due to the synergistic effect through the grafting of CNT fiber, PANI and CuO NPs. As a result, this electrode enhanced the H₂O₂ sensing performance.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.383-391
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• 2017

In image processing, image segmentation has been studied by various methods in a long time. Image segmentation is the process of partitioning a digital image into multiple objects and face detection is a typical image segmentation field being used in a variety of applications that identifies human faces in digital images. In this paper, we propose a method for extracting the contours of faces included in images. Using the Viola-Jones algorithm, to do this, we detect the approximate locations of faces from images. But, the Viola-Jones algorithm could detected the approximate location of face not the correct position. In order to extract a more accurate face region from image, we use skin color in this paper. In details, face region would be extracted using the analysis of horizontal and vertical histograms on the skin area. Finally, the face contour is extracted using snake algorithm for the extracted face area. In this paperr, a modified snake energy function is proposed for face contour extraction based snake algorithm proposed by Williams et al.[7]

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.61-73
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• 2009

In this paper, hybrid health monitoring techniques using acceleration-impedance features are newly proposed to detect two damage-type in steel plate-girder bridges, which are girder's stiffness-loss and support perturbation. The hybrid techniques mainly consists of three sequential phases: 1) to alarm the occurrence of damage in global manner, 2) to classify the alarmed damage into subsystems of the structure, and 3) to estimate the classified damage in detail using methods suitable for the subsystems. In the first phase, the global occurrence of damage is alarmed by monitoring changes in acceleration features. In the second phase, the alarmed damage is classified into subsystems by recognizing patterns of impedance features. In the final phase, the location and the extent of damage are estimated by using modal strain energy-based damage index method and root mean square deviation (RMSD) method. The feasibility of the proposed hybrid technique is evaluated on a laboratory-scaled steel plate-girder bridge model for which hybrid acceleration-impedance signatures were measured for several damage scenarios. Also, the effect of temperature on the accuracy of the impedance-based damage monitoring results are experimentally examined from combined scenarios of support damage cases and temperature changes.