• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.2-5
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• 2022

Human organs in the body have a complex structure, and in particular, the small intestine is about 7m long, so endoscopy is not easy and the risk of endoscopy is high. Currently, the test is performed with a capsule endoscope, and the test time is very long. The doctor connects the removed storage device to the computer to store the patient's capsule endoscope image and reads it using a program, but the capsule endoscope test results in a long image length, which takes a lot of time to read. In addition, in the case of the small intestine, there are many curves due to villi, so the occlusion area or light and shade of the image are clearly visible during the examination, and there may be cases where lesions and abnormal signs are missed during the examination. In this paper, we provide a method of assisting small intestine capsule endoscopic lesion examination using artificial neural networks to shorten the doctor's image reading time and improve diagnostic reliability.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.290-294
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• 2023

Among various types of harmful gases, hydrogen sulfide is a strong toxic gas that is mainly generated during spillage and wastewater treatment at industrial sites. Hydrogen sulfide can irritate the conjunctiva even at low concentrations of less than 10 ppm, cause coughing, paralysis of smell and respiratory failure at a concentration of 100 ppm, and coma and permanent brain loss at concentrations above 1000 ppm. Therefore, rapid detection of hydrogen sulfide among harmful gases is extremely important for our safety, health, and comfortable living environment. Most hydrogen sulfide gas sensors that have been reported are electrical resistive metal oxide-based semiconductor gas sensors that are easy to manufacture and mass-produce and have the advantage of high sensitivity; however, they have low gas selectivity. In contrast, the electrochemical sensor measures the concentration of hydrogen sulfide using an electrochemical reaction between hydrogen sulfide, an electrode, and an electrolyte. Electrochemical sensors have various advantages, including sensitivity, selectivity, fast response time, and the ability to measure room temperature. However, most electrochemical hydrogen sulfide gas sensors depend on imports. Although domestic technologies and products exist, more research is required on their long-term stability and reliability. Therefore, this study includes the processes from electrode material synthesis to sensor fabrication and characteristic evaluation, and introduces the sensor structure design and material selection to improve the sensitivity and selectivity of the sensor. A sensor case was fabricated using a 3D printer, and an Ag reference electrode, and a Pt counter electrode were deposited and applied to a Polytetrafluoroethylene (PTFE) filter using PVD. The working electrode was also deposited on a PTFE filter using vacuum filtration, and an electrochemical hydrogen sulfide gas sensor capable of measuring concentrations as low as 0.6 ppm was developed.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.69-75
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• 2024

This study focuses on enhancing the understanding of the form and characteristics of traditional towers, or 'pagodas,' by utilizing WebXR technology to enable users to explore 3D models and experience them in virtual reality on the web. Traditional towers in Korea pose challenges for direct on-site verification due to their size, making it difficult to examine the structure and features of each level. To address these issues, this research aims to provide users with a WebXR service that allows them to remotely explore and analyze towers without geographical or temporal constraints. The research methodology involves utilizing WebAR to offer a web-based service where users can directly view the original form of the tower's 3D model using smart devices both online and on-site. However, outdoor conditions may affect performance, and to address this, a tower-outline detection and matching technique was employed. Consequently, we propose a remote support service for traditional towers, allowing users to remotely access information and features of various towers nationwide on the web. Meanwhile, on-site visits can involve experiencing augmented reality representations of towers using smart devices.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.3
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• pp.1-14
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• 2024

Gaining an accurate 3D stream geometry has become feasible with Unmanned Aerial Vehicle (UAV), which is crucial for better understanding stream hydrodynamic processes. The objective of this study was to investigate series of filters to remove stream vegetation and propose the best method for generating Digital Terrain Models (DTMs) using UAV-based point clouds. A stream reach approximately 500 m of the Bokha stream in Icheon city was selected as the study area. Point clouds were obtained in August 1st, 2023, using Phantom 4 multispectral and Zenmuse L1 for Structure from Motion (SfM) and Light Detection And Ranging (LiDAR) respectively. Three vegetation filters, two morphological filters, and six composite filters which combined vegetation and morphological filters were applied in this study. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used to assess each filters comparing with the two cross-sections measured by leveling survey. The vegetation filters performed better in SfM, especially for short vegetation areas, while the morphological filters demonstrated superior performance on LiDAR, particularly for taller vegetation areas. Overall, the composite filters combining advantages of two types of filters performed better than single filter application. The best method was the combination of Progressive TIN (PTIN) and Color Indicies of Vegetation Extraction (CIVE) for SfM, showing the smallest MAE of 0.169 m. The proposed method in this study can be utilized for constructing DTMs of stream and thus contribute to improving the accuracy of stream hydrodynamic simulations.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.308-317
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• 2009

In the present work, a sensor system composed of an oscillator sensor and a Lamb wave sensor is proposed for the purpose of structural health monitoring. The oscillator sensing system detecting the shift of a structural resonant frequency in proportion to the amount of defects in the structure is a pretty sensitive and simple device, but its detectable range is limited to its local zone. The Lamb wave sensor system, however, is applicable to global detection of the defects. This study is aimed at investigating the feasible combination of the two systems to exploits their merits simultaneously. The scheme to use PZT patches as the oscillator sensor as well as the Lamb wave sensor was proposed to identify the position, length and number of cracks by means of TOF and amplitude of signals, and its validity was confirmed through experiments.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.680-686
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• 2022

Intraluminal duodenal diverticulum (IDD) is a rare congenital abnormality, consisting of a saclike mucosal lesion in the duodenum. Cases of IDD can present with gastrointestinal bleeding, duodenal obstruction, or pancreatitis. Here, we report a rare case of a 25-year-old female presenting with IDD complicated by duodeno-duodenal intussusception and recurrent pancreatitis. The diagnosis was based on findings from radiologic examinations (CT and MRI), upper gastrointestinal series (barium swallow), and gastroduodenofiberscopy. Laparoscopic excision of the presumed duodenal duplication was performed. The subsequent histopathologic evaluation of the excised sac revealed normal mucosa on both sides, but the absence of a proper muscle layer confirmed the diagnosis of IDD. Radiologic detection of a saccular structure in the second portion of the duodenum can indicate IDD with duodeno-duodenal intussusception as the lead point.

• Journal of Radiation Protection and Research
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• v.49 no.1
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• pp.29-39
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• 2024

Background: The gamma emission tomography (GET) device has been reported a reliable technique to inspect partial defects within spent nuclear fuel (SNF) of pin-by-pin level. However, the existing GET devices have low accuracy owing to the high attenuation and scatter probability for SNF inspection condition. The purpose of this study is to design and optimize a Yonsei single-photon emission computed tomography version 2 (YSECT.v.2) for fast inspection of SNF in water storage by acquisition of high-quality tomographic images. Materials and Methods: Using Geant4 (Geant4 Collaboration) and DETECT-2000 (Glenn F. Knoll et al.) Monte Carlo simulation, the geometrical structure of the proposed device was determined and its performance was evaluated for the ¹³⁷Cs source in water. In a Geant4-based assessment, proposed device was compared with the International Atomic Energy Agency (IAEA)-authenticated device for the quality of tomographic images obtained for 12 fuel sources in a 14 × 14 Westinghouse-type fuel assembly. Results and Discussion: According to the results, the length, slit width, and septal width of the collimator were determined to be 65, 2.1, and 1.5 mm, respectively, and the material and length of the trapezoidal-shaped scintillator were determined to be gadolinium aluminum gallium garnet and 45 mm, respectively. Based on the results of performance comparison between the YSECT.v.2 and IAEA's device, the proposed device showed 200 times higher performance in gamma-detection sensitivity and similar source discrimination probability. Conclusion: In this study, we optimally designed the GET device for improving the SNF inspection accuracy and evaluated its performance. Our results show that the YSECT.v.2 device could be employed for SNF inspection.

• Analytical Science and Technology
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• v.37 no.4
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• pp.220-229
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• 2024

This study aims to developing a method for estimating pharmaceutical compounds within a monolith column using high-performance liquid chromatography (HPLC). The monolithic column was prepared using copolymerization of glycidyl methacrylate, co-ethylene dimethacrylate, and co-acrylic acid inside a borosilicate tube of specific dimensions a 60 mm borosilicate tube length with 1.5 mm and 3.5 mm inner and outer diameters, respectively. A UV Ultra violet source with a wavelength of 365 nm was used, and the polymerization process involved mixing glycidyl methacrylate, acrylic acid, ethylene dimethacrylate as a binder, and 2,2-dimethoxy-2-phenyl acetate phenone as an initiator in suitable solvents consisting of ethanol and 1-hexanol. The polymerization process formed the monolith column after 4 minutes, and subsequently, the epoxy groups were altered to diol groups using 0.2 M hydrochloric acid HCl, which were pumped through the column for 3 hours at a flow rate of 10 µL·min^-1. Various techniques, such as Scanning Electron Microscope SEM, Brunauer-Emmett-Teller BET, Fourier-transform infrared spectroscopy FT-IR and HNMR, were utilized to characterize and confirm the structure of the monolith. The prepared monolith was employed to estimate and identify the pharmaceutical compound of warfarin using high-performance liquid chromatography HPLC. The analytical curve of warfarin was linear in the range of 3 to 100 ㎍·mL^-1 with an r² value of 0.999. The detection and quantification limits were 0.932 and 2.788 ㎍·mL^-1, respectively. The molar absorptivity and Sandells sensitivity were 2.99138 × 10⁶ L·mol^-1·cm^-1 and 103.1 × 10^-3 ㎍·cm^-2, respectively.

• Structural Monitoring and Maintenance
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• v.11 no.2
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• pp.101-126
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• 2024

To avoid irregularities in buildings, design codes worldwide have introduced detailed guidelines for their check and rectification. However, the criteria used to define and identify each of the plan and vertical irregularities are specific and may vary between codes of different countries, thus making their implementation difficult. This short communication paper proposes a novel approach for quantifying different types of structural irregularities using a common parameter named as unified identification factor, which is exclusively defined for the columns based on their axial loads and tributary areas. The calculation of the identification factor is demonstrated through the analysis of rectangular and circular reinforced concrete models using ETABS v18.0.2, which are further modified to generate plan irregular (torsional irregularity, cut-out in floor slab and non-parallel lateral force system) and vertical irregular (mass irregularity, vertical geometric irregularity and floating columns) models. The identification factor is calculated for all the columns of a building and the range within which the value lies is identified. The results indicate that the range will be very wide for an irregular building when compared to that with a regular configuration, thus implying a strong correlation of the identification factor with the structural irregularity. Further, the identification factor is compared for different columns within a floor and between floors for each building model. The findings suggest that the value will be abnormally high or low for a column in the vicinity of an irregularity. The proposed factor could thus be used in the preliminary structural design phase, so as to eliminate the complications that might arise due to the geometry of the structure when subjected to lateral loads. The unified approach could also be incorporated in future revisions of codes, as a replacement for the numerous criteria currently used for classifying different types of irregularities.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.