• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.815-822
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• 2023

Organic pollution is one of the most common forms of water contamination. Under the Water Quality Conservation Act, indicators for measuring organic substances include BOD, COD, and TOC. Analysis of BOD and COD is labor-intensive, and in the case of organic substances where biological decomposition is not feasible or toxic substances are present, the accuracy is often low. Therefore, the Ministry of Environment is shifting towards TOC-centric management. With advancements in sensor technology today, various parameters can be monitored using sensors. In this study, digital monitoring of river TOC using a spectrophotometer called Spectro::lyser V3 was conducted. Initially, experiments were carried out at the Andong River Experiment Center to assess the applicability of the measurement equipment. Subsequently, data collected at the confluence of the Nakdong River was analyzed for the spatial distribution of TOC using the Kriging technique. This research proposes the utilization of sensors for river TOC monitoring and spatial distribution analysis. Real-time monitoring of changes in river TOC concentration can serve as fundamental data for pollution monitoring and response. Sensor-based river monitoring offers advantages in terms of temporal resolution and real-time data acquisition. When various spatial information interpretation methods are applied, it is expected to contribute to diverse studies such as aquatic ecological health, river water source selection, and stratification analysis in the future.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.172-177
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• 2023

In this paper, 2-dimensional backtracking method for ultrasonic signals. Ultrasonic sensors are a common technology used in industrial fields as many studies have been conducted on distance measurement and indoor location tracking using transmission and reception devices in pairs. A method for tracking a signal of an arbitrary ultrasonic transmission device on a 2D plane using only a receiver of an ultrasonic signal is proposed. In order to track the ultrasonic signal, the receiver receives the signal by making at least three. The three receivers may calculate a direction and a distance using a time difference in which the ultrasound reception sound is reached. The existing method of tracking signal sources using ultrasonic waves has a problem of time synchronization of devices because the transceivers must be paired or installed independently for each sensor. In order to solve this problem, the distance of the ultrasonic receiver is minimized, and it is configured as one device. The sensor installed as one device may be processed by one operator, thereby solving the time synchronization problem. To increase time difference accuracy, high-speed 32-bit timers with high time resolution can be used to quickly calculate and track distances and directions.

• Restorative Dentistry and Endodontics
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• v.48 no.3
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• pp.22.1-22.8
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• 2023

Objectives: This study was conducted to evaluate and compare the tip and taper compatibility of accessory gutta-percha points (AGPs) with various rotary and reciprocating instruments. Materials and Methods: Using a profile analyzer, tip and taper measurements were taken of 10 AGPs of each of the 14 models available from Odous de Deus and the 4 models available from Dentsply-Maillefer. Diameter measurements were taken at 1-mm intervals, from 3 mm from the tip (D3) to 16 mm. Results: Based on the mean values obtained, 3-dimensional (3D) models of the AGPs were drawn in Autodesk Fusion 360 and superimposed on 3D models of each instrument selected (Mtwo, Reciproc, RaCe, K3, and ProDesign Logic) to determine the compatibility between the instrument and the AGP. Data corresponding to the tips and tapers of the various AGPs, as well as the tip and taper differences between the AGPs and the instruments, were analyzed using descriptive statistics. The tapers of the AGPs were subject to the American National Standards Institute/American Dental Association No. 57 standard. The Odous de Deus extra-long medium and extra-long extra-medium AGPs were shown to be compatible with Mtwo, K3, and ProDesign Logic instruments with taper 0.06 and tip sizes 25 and 30, while the Dentsply fine and fine medium cones were compatible with Mtwo, RaCe, and K3 instruments with conicity of 0.04 and tip sizes 35 and 40. Conclusions: Both the Odous de Deus and Dentsply commercial brands included 2 AGP models with tip (D3) and taper compatibility with Mtwo, RaCe, K3, and/or Prodesign Logic instruments.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.120-129
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• 2023

Hanger cable tension is a major response that can determine the integrity and safety of suspension bridges. In general, the vibration method is used to estimate hanger cable tension on operational suspension bridges. It measures natural frequencies from hanger cables and indirectly estimates tension using the geometry conditions of the hanger cables. This study estimated the hanger cable tension of the Palyeong Bridge using a vision-based system. The vision-based system used digital camcorders and tripods considering the convenience and economic efficiency of measurement. Measuring the natural frequencies for high-order modes required for the vibration method is difficult because the hanger cable response measured using the vision-based system is displacement-based. Therefore, this study proposed a back analysis technique for estimating tension using the natural frequencies of low-order modes. Optimization for the back analysis technique was performed by defining the difference between the natural frequencies of hanger cables measured in the field and those calculated using finite element analysis as the objective function. The direct search method that does not require the partial derivatives of the objective function was applied as the optimization method. The reliability and accuracy of the back analysis technique were verified by comparing the tension calculated using the method with that estimated using the vibration method. Tension was accurately estimated using the natural frequencies of low-order modes by applying the back analysis technique.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1777-1785
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• 2021

Objective: To investigate the accuracy of the Agatston score obtained with the ultra-high-pitch (UHP) acquisition mode using tin-filter spectral shaping (Sn150 kVp) and a kVp-independent reconstruction algorithm to reduce the radiation dose. Materials and Methods: This prospective study included 114 patients (mean ± standard deviation, 60.3 ± 9.8 years; 74 male) who underwent a standard 120 kVp scan and an additional UHP Sn150 kVp scan for coronary artery calcification scoring (CACS). These two datasets were reconstructed using a standard reconstruction algorithm (120 kVp + Qr36d, protocol A; Sn150 kVp + Qr36d, protocol B). In addition, the Sn150 kVp dataset was reconstructed using a kVp-independent reconstruction algorithm (Sn150 kVp + Sa36d, protocol C). The Agatston scores for protocols A and B, as well as protocols A and C, were compared. The agreement between the scores was assessed using the intraclass correlation coefficient (ICC) and the Bland-Altman plot. The radiation doses for the 120 kVp and UHP Sn150 kVp acquisition modes were also compared. Results: No significant difference was observed in the Agatston score for protocols A (median, 63.05; interquartile range [IQR], 0-232.28) and C (median, 60.25; IQR, 0-195.20) (p = 0.060). The mean difference in the Agatston score for protocols A and C was relatively small (-7.82) and with the limits of agreement from -65.20 to 49.56 (ICC = 0.997). The Agatston score for protocol B (median, 34.85; IQR, 0-120.73) was significantly underestimated compared with that for protocol A (p < 0.001). The UHP Sn150 kVp mode facilitated an effective radiation dose reduction by approximately 30% (0.58 vs. 0.82 mSv, p < 0.001) from that associated with the standard 120 kVp mode. Conclusion: The Agatston scores for CACS with the UHP Sn150 kVp mode with a kVp-independent reconstruction algorithm and the standard 120 kVp demonstrated excellent agreement with a small mean difference and narrow agreement limits. The UHP Sn150 kVp mode allowed a significant reduction in the radiation dose.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.3
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• pp.244-250
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• 2007

Membrane inlet mass spectrometer (MIMS) has been used to accurately quantify dissolved gases in liquid samples. In this study, the MIMS system was applied to measure dissolved methane in seawater and sediment porewater. To evaluate the accuracy of the measurement, liquid samples saturated with different methane partial pressure were prepared and the methane concentrations were quantified with the MIMS system. The measured values correspond well with the expected values calculated from solubility constants. The standard error of the measurements were $0.13{\sim}0.9%$ of the mean values. The distribution of dissolved methane concentration in seawater of the South Sea of Korea revealed that the physical parameters primarily control the methane concentration in sea water. The MIMS system was effective to resolve the small dissolved methane difference among water masses. The probe type inlet in MIMS system was proven to be effective to measure porewater methane concentration.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.96-109
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• 2024

The purpose of this study is to propose a method for evaluating the similarity of Show gardens using Deep Learning models, specifically VGG-16 and ResNet50. A model for judging the similarity of show gardens based on VGG-16 and ResNet50 models was developed, and was referred to as DRG (Deep Recognition of similarity in show Garden design). An algorithm utilizing GAP and Pearson correlation coefficient was employed to construct the model, and the accuracy of similarity was analyzed by comparing the total number of similar images derived at 1st (Top1), 3rd (Top3), and 5th (Top5) ranks with the original images. The image data used for the DRG model consisted of a total of 278 works from the Le Festival International des Jardins de Chaumont-sur-Loire, 27 works from the Seoul International Garden Show, and 17 works from the Korea Garden Show. Image analysis was conducted using the DRG model for both the same group and different groups, resulting in the establishment of guidelines for assessing show garden similarity. First, overall image similarity analysis was best suited for applying data augmentation techniques based on the ResNet50 model. Second, for image analysis focusing on internal structure and outer form, it was effective to apply a certain size filter (16cm × 16cm) to generate images emphasizing form and then compare similarity using the VGG-16 model. It was suggested that an image size of 448 × 448 pixels and the original image in full color are the optimal settings. Based on these research findings, a quantitative method for assessing show gardens is proposed and it is expected to contribute to the continuous development of garden culture through interdisciplinary research moving forward.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.21-30
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• 2024

In this paper, we propose a method to build a sample dataset of the features of eight sensor-only facilities built as infrastructure for autonomous cooperative driving. The feature extracted from point cloud data acquired by LiDAR and build them into the sample dataset for recognizing the facilities. In order to build the dataset, eight sensor-only facilities with high-brightness reflector sheets and a sensor acquisition system were developed. To extract the features of facilities located within a certain measurement distance from the acquired point cloud data, a cylindrical projection method was applied to the extracted points after applying DBSCAN method for points and then a modified OTSU method for reflected intensity. Coordinates of 3D points, projected coordinates of 2D, and reflection intensity were set as the features of the facility, and the dataset was built along with labels. In order to check the effectiveness of the facility dataset built based on LiDAR data, a common CNN model was selected and tested after training, showing an accuracy of about 90% or more, confirming the possibility of facility recognition. Through continuous experiments, we will improve the feature extraction algorithm for building the proposed dataset and improve its performance, and develop a dedicated model for recognizing sensor-only facilities for autonomous cooperative driving.