• 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.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.6
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• pp.669-674
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• 2012

D-Cube City is a recently completed multi purpose building consisting of four types of facilities; offices, a department store, a hotel, and congregation spaces. A community energy supply system(CES) has been installed to supply this building with electricity, steam, heat, and cold water. The BEMS, building energy management system, is currently being designed to reduce building energy consumption through the efficient operation of the various pieces of building service equipment. In this study the optimal methods for operating the CES of D-Cube City were considered. This system includes three combined heat and power systems, seven steam boilers, two hot water boilers, two absorption chillers, and four turbo chillers, and various other pieces of equipment. In result, the optimal methods of operating the CES for various energy demand levels were obtained along with the seasonal effects on the economic efficiency of the operation. The effect of the amount of energy demanded by the various facility areas on the total energy consumption was also analyzed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.579-586
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• 2019

The national base map of Korea has become more useful as the spatial information industry has developed rapidly. Beyond the simple application of the past paper map era, the role as important spatial information in the smart city and digital twin era is required with the IT (Information Technology) revolution. Therefore, the concepts of seamless, multi scale, object-oriented, customized production and real-time updating have emerged, and innovations in the distribution process through the internet are also taking place. Although the concept and definition in the law that is corresponds to the status of the national base map must be supported, the concept and definition of the paper map introduced from the surveying law in 1980 still exist. The definition of the national base map cannot meet current technological developments and social needs and does not reflect the perceptions of the majority of the people as well as the practical capacity of government organizations that manage it. Therefore, it is necessary to establish the concept of national base map in accordance with the current situation and to define it in the law. In this study, the concept of national base map was established and defined by comprehensive analysis of the development process of the national base map, the changes in the times and cases of the United States, the United Kingdom and Japan.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.461-470
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• 2017

In Korea, starting with the Government 3.0 Policy, the utilization of public data as an important driving force to promote economic growth has been highlighted as a major issue. However Korea is currently only able to open and provide accumulated data stored in the public domain. To resolve this issue, we need to not only open and provide public information, but also to create new information by linking the data and developing related services. Thus, this study analyzes the linkability of public information and provides lists of the linkable public data. In order to do this, we first have performed preconditioning processes on the accessibility and workability of the data. Next, we have deduced the major keywords in public data through analyzing the morphemes, and then the core keywords (Top 10) and their linkable keyword lists through an analysis of social networks. Based on the outcome of this study, a subsequent study will deduce new information by linking the public data and creating various services and information contents. Furthermore, not only conceptual but also practical linking measures need to be created, and a related law must be prepared.

• 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.

• Korean Journal of Poultry Science
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• v.33 no.3
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• pp.239-248
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• 2006

Improvements in understanding the effects of dietary fermentable carbohydrates and their interaction with supplemental feed enzymes and the feed manufacturing process may lead to reductions in volatile organic compound (VOC) emissions from poultry manure. Starch digestibility has been improved by replacing ground wheat or barley with whole wheat or barley, but there was no consistent effect of cereal species or feed form on the pH value of the gizzard contents. Pelleting results in improvements in feed conversion from 0 to 12%. Starch digestibility has been reported to account for up to 35 % of the improvement in available metabolic energy as a result of xylase supplementation. Factors which affect starch utilization and non-starch polysaccharide (NSP) absorption include the presence of anti-nutrient facto. (ANF) in grains, the nature of grain starch, NSP and the digestive capacity of animals. Improvements in feed production technology have been made in enzyme stabilization, allowing some dry enzyme products to be pelleted after conditioning at up to $87.69^{\circ}C$ and liquid enzymes to be stored in the feed mill for up to low months prior to use. The soluble NSP, arabinokylans and beta-glucans are partially degraded into smaller fragments by enzymes. With fragmentation, the water holding capacity is decreased, which leads to a reduction in digesta moisture, wet feces, and dirty eggs from hens fed diets containing viscosity-inducing ingredients.