• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.69-78
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• 2006

The rainfall depth-area-duration analysis which is used to characterize precipitation extremes for specification of so-called design storms, provides a basis for evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. So we propose a method for constructing drought severity-area-duration curves in this study. Monthly precipitation data over the whole Korea are used to compute SPI. Such SPIs are abstracted to several independent spatial components from EOF analysis. Using Kriging method, these spatial components are used to constitute grid-based SPI data set over the whole Korea including Jeju island with $6km{\times}6km$ resolution. After identifying main drought events, the drought severity-area-duration curves for these events over 32-year period of record are finally constructed. As a result, such curves show the similar shape with storm-based curves in the sense that the drought severity (or rainfall depth) is inversely proportional to drought area from the curves, but drought-based curves are different from storm-based curves in the sense that the drought severity decreasing rate with respect to drought area is much less than depth decreasing rate.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.911-917
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• 2023

The method of observing nodular changes on the liver surface using clinical ultrasonography is useful for diagnosing cirrhosis. However, the speckle noise that inevitably occurs in ultrasound images makes it difficult to identify changes in the liver surface and echo patterns, which has a negative impact on the diagnosis of cirrhosis. The purpose of this study is to model the median modified Wiener filter (MMWF), which can efficiently reduce noise in cirrhotic ultrasound images, and confirm its applicability. Ultrasound images were acquired using an ACR phantom and an actual cirrhotic patient, and the proposed MMWF algorithm and conventional noise reduction algorithm were applied to each image. Coefficient of variation (COV) and edge rise distance (ERD) were used as quantitative image quality evaluation factors for the acquired ultrasound images. We confirmed that the MMWF algorithm improved both COV and ERD values compared to the conventional noise reduction algorithm in both ACR phantom and real ultrasound images of cirrhotic patients. In conclusion, the proposed MMWF algorithm is expected to contribute to improving the diagnosis rate of cirrhosis patients by reducing the noise level and improving spatial resolution at the same time.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.3
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• pp.121-129
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• 2024

The rapid advancement of digital technology and the COVID-19 pandemic have significantly accelerated the growth of online commerce, highlighting the need for support mechanisms that enable small business owners to effectively respond to these market changes. In response, this paper presents a foundational technology leveraging the Online to Offline (O2O) strategy to automatically capture products displayed on retail shelves and utilize these images to create virtual stores. The essence of this research lies in precisely identifying and recognizing the location and names of displayed products, for which a single-class-targeted, lightweight model based on YOLOv8, named ESD-YOLOv8, is proposed. The detected products are identified by their names through feature-point-based technology, equipped with the capability to swiftly update the system by simply adding photos of new products. Through experiments, product name recognition demonstrated an accuracy of 74.0%, and position detection achieved a performance with an F2-Score of 92.8% using only 0.3M parameters. These results confirm that the proposed method possesses high performance and optimized efficiency.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.4
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• pp.244-252
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• 2023

The timeliness and accuracy of test results are crucial factors for clinicians to decide and promptly administer effective and targeted antimicrobial therapy, especially in life-threatening infections or when vital organs and functions, such as sight, are at risk. Further research is needed to refine and optimize matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based assays to obtain accurate and reliable results in the shortest time possible. MALDI-TOF MS-based bacterial identification focuses primarily on techniques for isolating and purifying pathogens from clinical samples, the expansion of spectral libraries, and the upgrading of software. As technology advances, many MALDI-based microbial identification databases and systems have been licensed and put into clinical use. Nevertheless, it is still necessary to develop MALDI-TOF MS-based antimicrobial-resistance analysis for comprehensive clinical microbiology characterization. The important applications of MALDI-TOF MS in clinical research include specific application categories, common analytes, main methods, limitations, and solutions. In order to utilize clinical microbiology laboratories, it is essential to secure expertise through education and training of clinical laboratory scientists, and database construction and experience must be maximized. In the future, MALDI-TOF mass spectrometry is expected to be applied in various fields through the use of more powerful databases.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.70-81
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• 2024

In this study, Tridimensional Hybrid Isolation System(THIS) was proposed as a vibration isolator for traffic loads, combining vertical and horizontal isolation systems. Its efficacy in improving serviceability for vertical vibration was analytically evaluated. Firstly, for the analysis, the major vibration modes of the existing apartment were identified through eigenvalue analysis for the system and pulse response analysis for the bedroom slab using commercial structural analysis software. Subsequently, a 16-story model with horizontal, vertical and rotational degrees of freedom for each slab was numerically organized to represent the achieved modes. The dynamic analysis for the measured acceleration from an adjacent ground to high-speed railway was performed by state-space equations with the stiffness and damping ratio of THIS as variables. The result indicated that as the vertical period ratio increased, the threshold period ratio where the slab response started to be suppressed varied. Specifically, when the period ratio is greater than or equal to 5, the acceleration levels of all slabs decreased to approximately 70% or less compared to the non-isolated condition. On the other hand, it was ascertained that the influence of damping ratios on the response control of THIS is inconsequential in the analysis. Finally, the improvement in vertical vibration performance of THIS was evaluated according to design guidelines for floor vibration of AIJ, SCI and AISC. It was confirmed that, after the application of THIS, the residential performance criteria were met, whereas the non-isolated structure failed to satisfy them.

• Pediatric Infection and Vaccine
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• v.31 no.1
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• pp.1-11
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• 2024

Despite improvements in vaccine coverage, a resurgence of measles has been reported, especially in the infant and adult populations in recent years. We conducted a systematic review of seroprevalence studies conducted in the Western Pacific Region (WPR) to provide insights into seropositivity trends in different countries. This systematic review aimed to collect data from all available measles seroprevalence studies to characterize the differences in population immunity against measles in different countries. We searched the online databases PubMed and Embase to identify: 1) observational studies that investigated seroprevalence in all age groups, and 2) results reported as antibody levels. The following variables were extracted from different study arms: paper identification (title, first author, publication year), inclusion and exclusion criteria, study site, age of subjects, number of subjects, country/area, population, methods, and seropositivity (%). The search yielded a total of 69 studies included in the review. Among the 1-6-year-old group, seropositivity remained relatively high, at 81-100% in China, 86-94% in Korea, and 77-91% in Australia. In adolescents aged 7-18-years old, seropositivity was relatively constant in China and Australia over time; however, a decreasing trend was noted in Korea in 2011 (66%), 2014 (69%), and 2014 (50%) in this age group. A similar downward trend was observed among Korean adults aged 19-39 years in 2011 (74%), 2019 (71%), and 2019 (64%). Children are likely to be protected by universal vaccination programs in WPR countries and regions. However, susceptible individuals with waned immunity may be present among the adult population.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.