• Journal of Environmental Impact Assessment
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• v.29 no.6
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• pp.403-413
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• 2020

A mathematical model comprised with eight simultaneous quasi-linear partial differential equations was suggested to provide optimal chlorination strategy. Upstream weighted finite element method was employed to construct multidimensional numerical code. The code was verified against measured concentrations in three type of reactors. Boundary conditions and reaction rate were calibrated for the sixteen cases of experimental results to regenerate the measured values. Eight reaction rate coefficients were estimated from the modeling result. The reaction rate coefficients were expressed in terms of pH and temperature. Automatic optimal algorithm was invented to estimate the reaction rate coefficients by minimizing the sum of squares of the numerical errors and combined with the model. In order to minimize the concentration of chlorine and pollutants at the final usage sites, a real-time predictive control system is imperative which can predict the water quality variables from the chlorine disinfection process at the water purification plant to the customer by means of a model and operate the disinfection process according to the influent water quality. This model can be used to build such a system in water treatment plants.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.617-624
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• 2022

The small electric vehicles equipped with V2X(vehicle to everything) systems may provide more information and function to the existing navigation system of the vehicle. The key components of V2X technology include V2V (vehicle to vehicle), V2N(vehicle to network) and V2I (vehicle to infrastructure). This study is to design and implementation of VI type E-PTO which is interfaced with external equipments, the work designs the components of E-PTO such as DC/DC converter, DC/AC converter, battery bidirectional charging system etc. Also, it implements the devices and control systems for driving. The test results of VI type E-PTO components showed allowable 10% requirements of transient voltage variation rate and recovery time within 100ms for start/stop and normal operation.

• Journal of Environmental Impact Assessment
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• v.28 no.6
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• pp.592-603
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• 2019

This study is about the implementation of low energy sewage management technology through effective control of blower which consumes the most energy in sewage treatment. In calculating the amount of oxygen required for microorganisms, unlike the existing method using the operating index in the bioreactor or TMS data in the discharge port, the CODcr and NH₄⁺-N concentration changes in sewage flowing into the sewage treatment plant were detected in advance before entering the bioreactor and the amount of air was controlled based on this. The pre-sensing was found to have a high correlation compared with conventional products. As a result of blower control, it was possible to save about 9.9% energy more than the manual control. Consequently, this study suggested the possibility of blower's real-time control combined with pre-sensing technology. Also, it is expected that the low energy sewage treatment can be applied to sewage treatment facilities dependent on operation by manpower, and it will contribute to the reduction of greenhouse gas emissions.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.45-54
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• 2022

This paper is about designing and implementing the simulation training equipment (simulator) for the CBRN Reconnaissance Vehicle-II (armor type). The simulation training equipment (simulator) is a military training equipment in a virtual environment that analyzes the training using various CBRN equipment according to the CBRN situation and make a professional report. The controller or training instructor can construct a scenario using the instructor control system for a possible CBRN situation, spread the situation, and observe the process of the trainee performing the propagated situation appropriately. All process can be monitored and analyzed by the system, and it can be recorded, so it is also used for AAR (After Action Review). To implement CBRN situation training in a virtual environment, instructor control (IOS), host (HOS), video (IGS), input/output device (IOC), and sound (ACS) were implemented, a long-range chemical automatic detector (LCA), a combined chemical detector (CAD), a control (MCC) and an operation (OCC) computer were developed as simulators. In this paper, the design and development of simulation training equipment for CBRN Reconnaissance Vehicle-II (armor type) was conducted, and the performance was verified through integrated tests and acceptance tests.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.77-86
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• 2023

Ship detection is widely used in areas such as maritime security, maritime traffic, fisheries management, illegal fishing, and border control, and ship detection is important for rapid response and damage minimization as ship accident rates increase due to recent increases in international maritime traffic. Currently, according to a number of global and national regulations, ships must be equipped with automatic identification system (AIS), which provide information such as the location and speed of the ship periodically at regular intervals. However, most small vessels (less than 300 tons) are not obligated to install the transponder and may not be transmitted intentionally or accidentally. There is even a case of misuse of the ship'slocation information. Therefore, in this study, ship detection was performed using high-resolution optical satellite images that can periodically remotely detect a wide range and detectsmallships. However, optical images can cause false-alarm due to noise on the surface of the sea, such as waves, or factors indicating ship-like brightness, such as clouds and wakes. So, it is important to remove these factors to improve the accuracy of ship detection. In this study, false alarm wasreduced, and the accuracy ofship detection wasimproved by removing wake.As a ship detection method, ship detection was performed using machine learning-based random forest (RF), and convolutional neural network (CNN) techniquesthat have been widely used in object detection fieldsrecently, and ship detection results by the model were compared and analyzed. In addition, in this study, the results of RF and CNN were combined to improve the phenomenon of ship disconnection and the phenomenon of small detection. The ship detection results of thisstudy are significant in that they improved the limitations of each model while maintaining accuracy. In addition, if satellite images with improved spatial resolution are utilized in the future, it is expected that ship and wake simultaneous detection with higher accuracy will be performed.

• Journal of Korean Society of Forest Science
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• v.66 no.1
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• pp.95-108
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• 1984

In order to see the effect of various factors on the success of grafting of walnut, a modified cleft grafting experiment has been conducted with Juglans sinensis Dode, J. regia (persian walnut), J. regiu (carpathian walnut), J. mandshurica, J. nigra and five cultivars of Carpathian walnut as "HANSEN", "METCALFE" "SCHOOL", "ILL CRATH", "LAKE" in a large green house equipped with automatic environmental control system in which temperature of the grafting bed at $28^{\circ}C$ and 85-100% for humidity during the period of January 1981-June 1983. As results of the experiments, the following facts were observed. 1) Hteroplastic grafting was proved to be more successful than homoplastic grafting with all five species tested among the J. regia (carpathian walnut), J. regia (persian walnut), J. sinensis, J. mondshurica, J. nigra. 2) It was interesting to notice that results hight survival of graft high as 99.33-100% with heteroplastic grafting when "Persian Walnut" and "Carpathian Walnut" was grafted on the stock of J. mandshurica and J. nigra. 3) A statistical significance of 5% Level was recognized in the above stocks among five species of homoplastic grafting with heteroplastic grafting and no significance among the scions. 4) The heteroplastic grafting when five cultivars of Carpathian Walnut as above as was grafted on the stock of Juglans nigra and J. mandshurica resulting high survival of graft as high as 85.33-100%. 5) As conclusion, the heteroplastic grafts of Carpathian Walnut and Persian Walnut was grafted on the stock of Juglans nigra and Jugdans mandshurica, the graft union, between stock and scion completed, in short period and was followed by a vigorous growth as well as "Cross Breeding", or hybrization, became apparent, with different desirable traits which could he used to cultivate a Later generation that combined these characteristics.