• The Journal of Korean Academy of Sensory Integration
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• v.19 no.1
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• pp.69-82
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• 2021

Objective : This recent work intended to provide basic information for researchers and practitioners related to occupational therapy about Developmental Coordination Disorder (DCD) in South Korea. The previous research of screening DCD and the effects of intervention programs were reviewed. Methods : Peer-reviewed papers relating to DCD and published in Korea from January 1990 to December 2020 were systematically reviewed. The search terms "developmental coordination disorder," "development coordination," and "developmental coordination" were used to identify previous Korean research in this area from three representation database, the Research Information Sharing Service, Korean Studies Information Service System, and Google Scholar. We found a total of 4,878 articles identified through the three search engines and selected seventeen articles for analysis after removing those that corresponded to the overlapping or exclusion criteria. We adopted "the conceptual model" to analyze the selected articles about DCD assessment and intervention. Results : We found that twelve of the 17 studies showed the qualitative level of Level 2 using non-randomized approach between the two groups. The Movement Assessment Battery for Children and its second edition were the most frequently used tools in assessing children for DCD. Among the intervention studies, the eight articles (47%) were adopted a dynamic systems approach; a normative functional skill framework and cognitive neuroscience were each used in 18% of the pieces; and 11% of the articles were applied neurodevelopmental theory. Only one article was used a combination approach of normative functional skill and general abilities. These papers were mainly focused on the movement characteristics of children with DCD and the intervention effect of exercise or sports programs. Conclusion : Most of the reviewed studies investigated the movement characteristics of DCD or explore the effectiveness of particular intervention programs. In the future, it would be useful to investigate the feasibility of different assessment tools and to establish the effectiveness of various interventions used in rehabilitation for better motor performance in children with DCD.

• Clean Technology
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• v.28 no.1
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• pp.24-31
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• 2022

Petroleum-based plastics are used for various purposes and pose a significant threat to the earth's environment and ecosystem. Many efforts have been taken globally in different areas to find alternatives. As part of these efforts, this study manufactured alginate-based polyvinyl alcohol (PVA) blended films by casting from an aqueous solution prepared by mixing 10 wt% petroleum-based PVA with biodegradable, marine biomass-derived alginate. Glutaraldehyde was used as a cross-linking agent, and cardanol, an alkyl phenol-based bio-oil extracted from cashew nut shell, was added in the range of 0.1 to 2.0 wt% to grant antibacterial activity to the films. FTIR and TGA were performed to characterize the manufactured blended films, and the tensile strength, degree of swelling, and antibacterial activity were measured. Results obtained from the FTIR, TGA, and tensile strength test showed that alginate, the main component, was well distributed in the PVA by forming a matrix phase. The brittleness of alginate, a known weakness as a single component, and the low thermal durability of PVA were improved by cross-linking and hydrogen bonding of the functional groups between alginate and PVA. Addition of cardanol to the alginate-based PVA blend significantly improved the antibacterial activity against S. aureus and E. coli. The antibacterial performance was excellent with a death rate of 98% or higher for S. aureus and about 70% for E. coli at a contact time of 60 minutes. The optimal antibacterial activity of the alginate-PVA blended films was found with a cardanol content range between 0.1 to 0.5 wt%. These results show that cardanol-containing alginate-PVA blended films are suitable for use as various antibacterial materials, including as food packaging.

• Journal of Food Hygiene and Safety
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• v.37 no.2
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• pp.46-54
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• 2022

Known for its effectiveness in weight loss and diabetes prevention, Gymnema sylvestre products can be found in the US, Japanese, and Indian markets. However, the recommended dosage or safety of these products has not yet been proven. Therefore, development of an analytical method for detecting the content of Gymnema sylvestre in food products is required. Accordingly, this study proposes an analysis method that can examine Gymnema sylvestre in food using LC-ESI-MS/MS and KASP (Kompetitive Allele-Specific PCR) markers. In LC-ESI-MS/MS, a simultaneous analysis method for gymnemic acid and deacylgymnemic acid was optimized using negative ionization mode, and its validation test was completed for solid and liquid samples. In addition, KASP markers were prepared by finding the specific SNP of G. sylvestre in ITS2 and matK through DNA barcodes. The two KASP markers returned positive FAM fluorescence result when combined with G. sylvestre, and this aspect was confirmed in raw G. sylvestre as well. The applicability of the method was tested on 21 different food and healthy functional products containing G. sylvestre purchased on the internet. As a result, although there was a difference in the ratios of gymnemic acid and deacylgymnemic acid in LC-ESI-MS/MS, the index component was detected in all 21 products samples. In the KASP analysis, 9 products returned positive FAM result, and the rest of the products were found to be containing G. sylvestre extract. This study is the first study to use the dual system of LC-ESI-MS/MS and KASP for the analysis of G. sylvestre. The study has confirmed that these two methods are applicable to the examine G. sylvestre content in food products.

• Korean Journal of Optics and Photonics
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• v.33 no.4
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• pp.167-176
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• 2022

Adaptive optics (AO) systems compensate for atmospheric disturbance, especially phase distortion, by introducing counter-wavefront deformation calculated from real-time wavefront sensing or prediction. Because AO system implementations are time-consuming and costly, it is highly desirable to estimate the system's performance during the development of the AO system or its parts. Among several techniques, we mostly apply statistical analysis, computational simulation, and optical-bench tests. Statistical analysis estimates performance based on the sum of performance variances due to all design parameters, but ignores any correlation between them. Computational simulation models every part of an adaptive optics system, including atmospheric disturbance and a closed loop between wavefront sensor and deformable mirror, as close as possible to reality, but there are still some differences between simulation models and reality. The optical-bench test implements an almost identical AO system on an optical bench, to confirm the predictions of the previous methods. We are currently developing an AO system for a 1.6-m ground telescope using a deformable mirror that was recently developed in South Korea. This paper reports the results of the statistical analysis and computer simulation for the system's design and confirmation. For the analysis, we apply the Strehl ratio as the performance criterion, and the median seeing conditions at the Bohyun observatory in Korea. The statistical analysis predicts a Strehl ratio of 0.31. The simulation method similarly reports a slightly larger value of 0.32. During the study, the simulation method exhibits run-to-run variation due to the random nature of atmospheric disturbance, which converges when the simulation time is longer than 0.9 seconds, i.e., approximately 240 times the critical time constant of the applied atmospheric disturbance.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.1
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• pp.73-80
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• 2022

In this study, details of NRC beam-column connections were developed in which beam and columns pre-assembled in factories using steel angles were bolted on site. The developed joint details are NRC-J type and NRC-JD type. NRC-J type is a method of tensile joining with TS bolts to the side and lower surfaces of the side plate of the NRC column and the end plate of the NRC beam. NRC-JD type has a rigid joint with high-strength bolts between the NRC beam and the side of the NRC column for shear, and with lap splices of reinforcing bar penetrating the joint and the beam main reinforcement for bending. For the seismic performance evaluation of the joint, three specimens were tested: an NRC-J specimen and NRC-JD specimen with NRC beam-column joint details, and an RC-J specimen with RC beam-column joint detail. As a result of the repeated lateral load test, the final failure mode of all specimens was the bending fracture of the beam at the beam-column interface. Compared to the RC-J specimen, the maximum strength of the specimen by the positive force was 10.1% and 29.6% higher in the NRC-J specimen and the NRC-JD specimen, respectively. Both NRC joint details were evaluated to secure ductility of 0.03 rad or more, the minimum total inter-story displacement angle required for the composite intermediate moment frame according to the KDS standard (KDS 41 31 00). At the slope by relative storey displacemet of 5.7%, the NRC-J specimen and the NRC-JD specimen had about 34.8% and 61.1% greater cumulative energy dissipation capacity than the RC specimen. The experimental strength of the NRC beam-column connection was evaluated to be 30% to 53% greater than the theoretical strength according to the KDS standard formula, and the standard formula evaluated the joint performance as a safety side.

• Economic and Environmental Geology
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• v.55 no.2
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• pp.209-217
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• 2022

The purpose of this study was to confirm the dissolution of arsenic from the stabilized soil around abandoned coal mines by cultivation activities. Experimental soils were collected from the agricultural field around Okdong and Buguk coal mines, and the concentration of arsenic in the soil and the geochemical mobility were confirmed. The average arsenic concentration was 20 mg/kg. The soil with relatively high geochemical mobility of arsenic in the soil was used in the batch and column experiment. The limestone was mixed with soil for soil stabilization, and the mixing ratio was 3% of limestone, based on the soil weight. The phosphoric acid fertilizer (NH₄H₂PO₄) was added to the soil to simulate a cultivation condition according to the Rural Development Administration's rules. Comparative soil without mixing limestone was prepared and used as a control group. The arsenic extraction from soil was increased following the fertilizer mixing amount and it shows a positive relationship. The concentration of phosphate in the supernatant was relatively low under the condition of mixing limestone, which is determined to be result of binding precipitation of phosphate ions and calcium ions dissolved in limestone. Columns were set to mix phosphoric acid fertilizers and limestone corresponding to cultivation and stabilization conditions, and then the column test was conducted. The variations of arsenic extraction from the soil indicated that the stabilization was effectible until 10 P.V.; however, the stabilization effect of limestone decreased with time. Moreover, the geochemical mobility of arsenic has transformed by increasing the mobile fractions in soil compared to initial soil. Therefore, based on the arsenic extraction results, the cultivation activities using phosphoric fertilizer could induce a decrease in the stabilization effect.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.651-662
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• 2022

The electrochemical impedance spectroscopy(EIS) method was used to evaluate the concrete deterioration process related to chloride-induced steel corrosion with various corrosion levels(initiation, rust propagation and acceleration periods). The impressed current technique, with four total current levels of 0C, 13C, 65C and 130C, was used to accelerate steel corrosion in concrete cylinder samples with w/c ratio of 0.4, 0.5, and 0.6, immersed in a 0.5M NaCl solution. A series of EIS measurements was performed to monitor concrete deterioration during the accelerated corrosion test in this study. Some critical parameters of the equivalent circuit were obtained through the EIS analysis. It was observed that the charge transfer resistance(R_c) dropped sharply as the impressed current increased from 0C to 13C, indicating a value of approximately 10kΩcm². However, the sensitivity of R_c significantly decreased when the impressed current was further increased from 13C to 130C after corrosion of steel had been initiated. Meanwhile, the double-layer capacitance value(C_dl) linearly increased from 50×10^-6μF/cm² to 250×10^-6μF/cm² as the impressed current in creased from 0C to 130C. The results in this study showed that monitoring C_dl is an effective measurement parameter for evaluating the progress of internal concrete damages(de-bonding between steel and concrete, micro-cracks, and surface-breaking cracks) induced by steel corrosion. The findings of this study provide a fundamental basis for developing an embedded sensor and signal interpretation method for monitoring concrete deterioration due to steel corrosion at various corrosion levels.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.