• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1289-1297
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• 2023

In this study, NF Complex of 0%, 0.5%, 2.5%, 12.5%, and 100% was prepared by complexing and extracting Indian gooseberry, Rosa multiflora Thunberg roots, and saw palmetto fruit in a ratio of 5:1:1. The manufactured NF Complex was applied to bleached sample hair and then compared and analyzed with damaged hair. To confirm the improvement effect, tensile strength, gloss, absorbance, and brightness were measured. As a result of the measurement, the tensile strength increased. The gloss content decreased by 100%, but the remaining content increased. The change in absorbance was minimal. There was also a change in brightness, but it was minimal. It was confirmed that there is a significant difference in the average value of NF Complex, and it is judged necessary to study various ratios in the future.

• Journal of KIBIM
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• v.14 no.2
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• pp.25-34
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• 2024

This study proposes a new methodology to increase the flexibility and efficiency of the design data evaluation system by combining Building Information Modeling (BIM) technology in the architectural industry, OpenAI's interactive artificial intelligence, and ChatGPT. BIM technology plays an important role in digitally modeling and managing architectural information. Since architectural information is included, research and development are underway to review and evaluate BIM data according to conditions through program development. However, in the process of reviewing BIM design data, if the review criteria or evaluation criteria according to design change occur frequently, it is necessary to update the program anew. In order for designers or reviewers to apply the changed criteria, requesting a program developer will delay time. This problem was studied by using ChatGPT to modify and update the design data evaluation program code in real time. In this study, it is aimed to improve the changing standards and accuracy by enabling programming non-professionals to change the design regulations and calculation standards of the BIM evaluation program system using ChatGPT. In this study, in the BIM-based design certification automation evaluation program, a program in which the automation evaluation method is being studied based on the design certification evaluation manual was first used. In the design certification automation evaluation program, the programming non-majors checked the automation evaluation code by linking ChatGPT, and the changed calculation criteria were created and modified interactively. As a result of the evaluation, the change in the calculation standard was explained to ChatGPT and the applied result was confirmed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.237-244
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• 2024

Pavement management systems (PMS) provide procedures to quantify road serviceability based on pavement conditions such as cracks and plastic deformation and suggest proper maintenance methods. The deterioration of the road pavement is relevant to the time although the quantifications on road serviceability in PMS present road surface conditions at the evaluation. More systematic evaluation on road serviceability may need time-dependent factors of road environments and that can improve PMS. Rainfall, temperature and vehicle loads can be environmental factors for road serviceability evaluation. As no data are avablie that can link between road conditions and environmental road factors, we conducted experiments to suggest economical devices and methods to obtain relevant data. We used temperature sensors and accelerometers with Arduino to measure road surface temperature and dynamic loads and provide data to improve pavement serviceability evaluation.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.497-508
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• 2023

Agricultural reservoirs are crucial structures for water resources monitoring especially in Korea where the resources are seasonally unevenly distributed. Optical and Synthetic Aperture Radar (SAR) satellites, being utilized as tools for monitoring the reservoirs, have unique limitations in that optical sensors are sensitive to weather conditions and SAR sensors are sensitive to noises and multiple scattering over dense vegetations. In this study, we tried to improve water body detection accuracy through optical-SAR data fusion, and quantitatively analyze the complementary effects. We first detected water bodies at Edong, Cheontae reservoir using the Compact Advanced Satellite 500(CAS500), Kompsat-3/3A, and Sentinel-2 derived Normalized Difference Water Index (NDWI), and SAR backscattering coefficient from Sentinel-1 by K-means clustering technique. After that, the improvements in accuracies were analyzed by applying K-means clustering to the 2-D grid space consists of NDWI and SAR. Kompsat-3/3A was found to have the best accuracy (0.98 at both reservoirs), followed by Sentinel-2(0.83 at Edong, 0.97 at Cheontae), Sentinel-1(both 0.93), and CAS500(0.69, 0.78). By applying K-means clustering to the 2-D space at Cheontae reservoir, accuracy of CAS500 was improved around 22%(resulting accuracy: 0.95) with improve in precision (85%) and degradation in recall (14%). Precision of Kompsat-3A (Sentinel-2) was improved 3%(5%), and recall was degraded 4%(7%). More precise water resources monitoring is expected to be possible with developments of high-resolution SAR satellites including CAS500-5, developments of image fusion and water body detection techniques.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.94-101
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• 2023

In this study, the development performance of the head bars, which is SD700, was experimentally evaluated at the RC (reinforced concrete) or SFRC (steel fiber reinforced concrete external beam-column joint. A total of 10 specimens were tested, and variables such as steel fibers, length of settlement, effective depth of the beam, and stirrups of the column were planned. As a result of the experiment, the specimens showed side-face blowout, concrete breakout, and shear failure depending on the experimental variables. In the RC series experiments with development length as a variable, it was confirmed that the development strength increased by 26.5~42.2% as the development length increased by 25-80%, which was not proportional to the development length. JD-based experiments with twice the effective depth of beams showed concrete breakout failure, reducing the maximum strength by 31.5% to 62% compared to the reference experiment. The S-series experiment, in which the spacing of the shear reinforcement around the enlarged head reinforcement was 1/2 times that of the reference experiment, increased the maximum strength by 8.4 to 9.7%. The concrete compressive strength of SFRC was evaluated to be 29.3% smaller than the concrete compressive strength of RC, but the development strength of SFRC specimens increased by 7.3% to 12.2%. Accordingly it was confirmed that the development performance of the head bar was greatly improved by reinforcing the steel fiber. Considering the results of 92% and 99% of the experimental maximum strength of the experiment arranged with 92% and 110% of the KDS-based settlement length, it is judged that the safety rate needs to be considered even more. In addition, it is required to present a design formula that considers the effective depth of the beam compared to the development length.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.76-81
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• 2018

The Internet of Things (IoT), which is emerging as a future economic growth engine, has been actively introduced in areas close to our daily lives. However, there are still IoT security threats that need to be resolved. In particular, with the spread of smart homes and smart cities, an explosive amount of closed-circuit televisions (CCTVs) have been installed. The Internet protocol (IP) information and even port numbers assigned to CCTVs are open to the public via search engines of web portals or on social media platforms, such as Facebook and Twitter; even with simple tools these pieces of information can be easily hacked. For this reason, a big-data analytics system is needed, capable of supporting quick responses against data, that can potentially contain risk factors to security or illegal websites that may cause social problems, by assisting in analyzing data collected by search engines and social media platforms, frequently utilized by Internet users, as well as data on illegal websites.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.368-386
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• 2017

The purpose of this study is to present the technology value model based on profit approach and IITP practical guide for Ethernet network technology, which is the core technology of autonomous vehicles and connected cars in the hyper-connected industry. In-vehicle network, Ethernet technology, Ethernet port count, port pricing, and application data for technology assessment are sources of global market research organizations. The data on the company's COGS (Cost of Goods Sold), operating capital requirement, capital expenditure, and income statement data are used by the Bank of Korea's Business Analysis Report. According to the results of the study, the product market size was estimated to be US $470.3 billion and the technology market size was $52.1 billion over the seven years of economic life cycle of technology. The market value of the technology was estimated to be $260 million reflecting the possibility of entry into the market. In the case of the corporate management analysis report, the average value of the IITP and the top 25% were $0.7 million and $40.2 million, respectively. -27.8 million, and -73.6 million dollars respectively. This implies that government support for policy support is needed when conducting corporate R&D with high cost-to-sales ratio. The results of this study can be used as a reference for the evaluation of technology demand based ICT R&D technology in the industrial internet market in the fourth industrial revolution era.