• Atmosphere
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• v.31 no.5
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• pp.489-510
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• 2021

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.125-134
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• 2023

In order to develop a backtracking technique for turbidity measurement data without discriminatory characteristics, three turbidity backtracking techniques for predicting inflow turbidity of a stream were compared using real-time turbidity data measured at automatic water quality measurement points located upstream and downstream of the stream and the Py_STPS model. Three turbidity backtracking techniques were applied: 1) simple preservation method of turbidity load considering flow time, 2) a method of using the flow rate at the upstream boundary considering the flow time as the flow rate at the downstream boundary, 3) method of introducing internal reaction rate to reflect the behavior characteristics of turbidity-causing substances. As a result of applying the three backtracking models, it was confirmed that the backtracking technique that introduced the internal reaction rate had the best results.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.118-129
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• 2011

This Study has developed the information system of the rivers based on 3D image GIS by converging the latest information technology of GIS(Geographic Information System), RS(Remote Sensing), GNSS(Global Navigation Satellite System), aerial laser survey(LiDAR) with real time network technology in order to understand the current situation of all the four major rivers and support the administrative management system. The said information system acquires the high resolution aerial photographs of 25cm, aerial laser survey and water depth surveying data to express precise space information on the whole Youngsan River which is the leading project site out of the four river sites. Monitoring the site is made available on the transporting means such as a helicopter, boat or a bus in connection with locational coordinate tracking skill for the moving objects in real time using GNSS. It makes monitoring all the information on the four river job sites available at a glance, which can obtain the reliability of the people to such vast areas along with enhancing the recognition of the people by publicity of four Rivers Revitalizing Project and reports thereof.

• Journal of Veterinary Clinics
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• v.30 no.4
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• pp.264-267
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• 2013

Stroke volume (SV) is an important parameter for monitoring of a critically ill animal and is echocardiographically measured using the modified Simpson's method, automated contour tracking (ACT) method and left ventricular outflow method. The purpose of this study was to evaluate the effect of anesthesia (isoflurane) on the SV during echocardiographic examination and to evaluate the feasibility of ACT method for measuring the stroke volume without anesthesia (conscious state). Ten clinically healthy adult Beagle dogs (3 male and 7 female, weighing 6.6-10.8 kg, aged 2-3 years old) were enrolled. Our study found that the dogs anesthetized have a significantly lower SV, compared to the SV of dogs unanesthetized, regardless of methods measuring SV. In addition, in the dogs without anesthesia, the SV measured by ACT method was significantly lower than the other two methods. This result implies that the anesthesia may significantly lower the SV. Thus, the measurement of SV under anesthesia may not be adequate in veterinary field. Furthermore because the ACT method measured SV significantly lower than other two methods, this method may be inappropriate for measuring SV in even conscious dogs.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.243-248
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• 2013

In wireless sensor networks, consensus algorithms for dynamic systems may flexibly usable for their data fusion of a sensor network. In this paper, a distributed data fusion filter is implemented using an average consensus based on distributed sensor data, which is composed of some sensor nodes and a sink node to track the mean values of n sensors' data. The consensus filter resolve the problem of data fusion by a distribution Kalman filtering scheme. We showed that the consensus filter has an optimal convergence to decrease of noise propagation and fast tracking ability for input signals. In order to verify for the results of consensus filtering, we showed the output signals of sensor nodes and their filtering results, and then showed the result of the combined signal and the consensus filtering using zeegbee communication.

• Journal of the Korea Society for Simulation
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• v.16 no.3
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• pp.49-56
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• 2007

There are many application areas of wireless sensor networks, such as combat field surveillance, terrorist tracking and highway traffic monitoring. These applications collect sensed data from sensor nodes to monitor events in the territory of interest. One of the important issues in these applications is the existence of the radio-jamming zone between source nodes and the base station. Depending on the routing protocol the transmission of the sensed data may not be delivered to the base station. To solve this problem we propose a genetic algorithm based routing method for reliable transmission while considering the balanced energy depletion of the sensor nodes. The genetic algorithm finds an efficient routing path by considering the radio-jamming zone, energy consumption needed fur data transmission and average remaining energy level. The fitness function employed in genetic algorithm is implemented by applying the fuzzy logic. In simulation, our proposed method is compared with LEACH and Hierarchical PEGASIS. The simulation results show that the proposed method is efficient in both the energy consumption and success ratio of delivery.

• Tunnel and Underground Space
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• v.31 no.1
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• pp.1-9
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• 2021

Recently, the development of underground space has been accelerated with rapid urbanization, and it is significantly important for safe construction to accurately understand the geological conditions of the section when excavating rocks. In this paper, a boring alignment tracking and geological exploration system have been developed to identify the geological conditions beyond the excavation face by utilizing a MSP method that bores a large empty hole to reduce blast-induced vibration. The major advantage of the proposed exploration system is that we can obtain the ground condition of 50 m ahead of the excavation face through exploration along blast cut-holes drilled for the NATM tunnel construction. In addition, we introduce several case histories regarding the assessment of the geological conditions beyond the tunnel face by monitoring the inside of large empty holes using the proposed hole exploration system.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.97-100
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• 2009

The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. An adaptive feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. In this study, the Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 and 2000 using a dynamic technique, and the adaptive reconstruction of harmonic model was then applied to the NDVI time series for tracking changes on the ground surface. The results show that the adaptive approach is potentially very effective for continuously monitoring changes on near-real time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.672-678
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• 2020

Ultra-high-speed images that accurately depict the minute movements of objects have become common as low-cost and high-performance cameras that can film at high speeds have emerged. In this paper, the proposed method removes unexpected noise contained in images after input at high speed, and then extracts an area of interest that can represent personal information, such as skin areas, from the image in which noise has been removed. In this paper, noise generated by abnormal electrical signals is removed by applying bilateral filters. A color model created through pre-learning is then used to extract the area of interest that represents the personal information contained within the image. Experimental results show that the introduced algorithms remove noise from high-speed images and then extract the area of interest robustly. The approach presented in this paper is expected to be useful in various applications related to computer vision, such as image preprocessing, noise elimination, tracking and monitoring of target areas, etc.