• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.364-371
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• 2017

TPMS is a vehicle electric system that measures the air pressure of a tire, and informs the driver of current tire states. The TPMS sensor typically uses unidirectional communication for small size, light weight, and low power. The transmission period of the sensor indicates the service quality of monitoring the tire. In order to determine the optimal transmission period, frame collision probability and the life time of the sensor should be analyzed. In this paper, collision probability model using Venn diagram is designed in low power TPMS with the normal and warning mode. And the life time and a collision probability were analyzed with the ratio(n) of the normal mode to warning mode transmission period. As a result, $T_{nP}=31sec$ and $T_{wP}=2.4sec$ at 5 years, and $T_{nP}=71sec$ and $T_{wP}=2.5sec$ at 7 years.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.23-28
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• 2002

Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

• Water for future
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• v.25 no.4
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• pp.109-119
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• 1992

The purpose of this study is to develop real-time streamflow forecasting models in order to manage effectively the flood warning system and water resources during the storm. The stochastic system models of the rainfall-runoff process using in this study are constituted and applied the Recursive Least Square and the Instrumental Variable-Approximate Maximum Likelihood algorithm which can estimate recursively the optimal parameters of the model. Also, in order to improve the performance of streamflow forecasting, initial values of the model parameter and covariance matrix of parameter estimate errors were evaluated by using the observed historical data of the hourly rainfall-runoff, and the accuracy and applicability of the models developed in this study were examined by the analysis of the I-step ahead streamflow forecasts.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.6
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• pp.965-973
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• 2021

The behavior of angelfish Pterophyllum scalare exposed to low and high temperatures was monitored by video tracking, and information such as the initial speed, changes in speed, and locations of the fish in the tank were analyzed. The water temperature was raised from 26℃ to 36℃ or lowered from 26℃ to 16℃ for 4 h. The control group was maintained at 26℃ for 8 h. The experiment was repeated five times for each group. Machine learning analysis comprising a long short-term memory model was used to train and test the behavioral data (80 s) after pre-processing. Results showed that when the water temperature changed to 36℃ or 16℃, the average speed, changes in speed and fractal dimension value were significantly lower than those in the control group. Machine learning analysis revealed that the accuracy of 80-s video footage data was 87.4%. The machine learning used in this study could distinguish between the optimal temperature group and changing temperature groups with specificity and sensitivity percentages of 86.9% and 87.4%, respectively. Therefore, video tracking technology can be used to effectively analyze fish behavior. In addition, it can be used as an early warning system for fish health in aquariums and fish farms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.610-616
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• 2020

This study conducted joint research with the Navy logistics command ship technology research institute to resolve the occurrence of naval vessel's high-temperature warning and equipment shutdown caused by VRTU overheating during summer operation and the dispatch of troops to equatorial regions. The cooling effect was checked according to the installation of a thermoelectric device cooling system, and heat flow and heat transfer characteristics inside VRTU was analyzed using Computational Fluid Dynamics. In addition, the temperature distribution inside the engine room was assessed through interpretation, and the optimal installation location to prevent VRTU overheating was identified. As a result, the average volume temperature inside the VRTU decreased by approximately 10 ℃ with the installation of the cooling system, and the fan installed in the cooling system made the heat circulation smooth, enhancing the cooling effect. The inside of the engine room showed a high-temperature distribution at the top of the engine room, and the end of the HVAC duct diffuser showed the lowest temperature distribution.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.3
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• pp.69-78
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• 2024

This study evaluates the detection and utilization of wind shear using data from the Low-Level Wind Shear Alert System (LLWAS) and the Aerodrome Meteorological Observation System (AMOS) for the year 2023 at Incheon International Airport. A comparison of wind shear occurrence days revealed that LLWAS recorded 57 days, the reproduced LLWAS recorded 84 days, and AMOS recorded 163 days, with AMOS and the reproduced LLWAS showing higher occurrences. Performance metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and True Skill Statistic (TSS), were analyzed to evaluate detection capabilities. For the reproduced LLWAS, most wind shear events were detected, but the FAR was high, indicating lower performance. AMOS detected about 50% of actual wind shear events, with a lower FAR than the reproduced LLWAS but still relatively high. To improve detection performance, optimal thresholds for wind shear warnings were analyzed and adjusted, resulting in an increase in the CSI from 0.53 to 0.68 for the reproduced LLWAS and from 0.25 to 0.28 for AMOS. By adjusting the wind shear warning thresholds, the balance between POD and FAR was improved, confirming the potential for ground-based equipment to issue wind shear warnings effectively.

• Journal of Korea Multimedia Society
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• v.23 no.6
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• pp.772-783
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• 2020

Tsunami is a frightful natural disaster that causes severe damages worldwide. To minimize the damage, South Korea has built a tsunami warning system and designated evacuation sites in the east and south coasts. However, such countermeasures have not been verified whether they are adequate to minimize casualties since tsunami rarely occurs in South Korea. Recently, due to increasing earthquakes in the west coast of Japan, the likelihood of South Korea entering the damage area of tsunami rises; thus, in this paper, we develops a simulator based on Unity game engine to simulate the evacuation from tsunami. In order to increase the fidelity of the simulation results, the simulator applies a tsunami simulation model that analyzes coastal inundation based on cellular automata. In addition, the objects included in tsunami evacuation, such as humans, are modeled as an agent model that determines the situation and acts itself, based on the discrete-event system specification (DEVS), a mathematical formalism for describing a discrete event system. The tsunami simulation model and agent models are integrated and visualized in the simulator using Unity game engine. As an example of the use of this simulator, we verify the existing tsunami evacuation site in Gwangalli Beach in Busan and suggest the optimal alternative site minimizing casualties.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.5
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• pp.94-104
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• 2000

The accuracy and reliability of the target tracking is very critical issue in the design of automotive collision warning radar A significant problem in multi-target tracking (MTT) is the target-to-measurement data association If an incorrect measurement is associated with a target, the target could diverge the track and be prematurely terminated or cause other targets to also diverge the track. Most methods for target-to-measurement data association tend to coalesce neighboring targets Therefore, many algorithms have been developed to solve this data association problem. In this paper, a new multi-target data association method based on order statistics is described The new approaches. called the order statistics probabilistic data association (OSPDA) and the order statistics joint probabilistic data association (OSJPDA), are formulated using the association probabilities of the probabilistic data association (PDA) and the joint probabilistic data association (JPDA) filters, respectively Using the decision logic. an optimal or near optimal target-to-measurement data association is made A computer simulation of the proposed method in a heavy cluttered condition is given, including a comparison With the nearest-neighbor CNN). the PDA, and the JPDA filters, Simulation results show that the performances of the OSPDA filter and the OSJPDA filter are superior to those of the PDA filter and the JPDA filter in terms of tracking accuracy about 18% and 19%, respectively In addition, the proposed method is implemented using a developed digital signal processing (DSP) board which can be interfaced with the engine control unit (ECU) of car engine and with the d?xer through the controller area network (CAN)

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.58-63
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• 2017

Emergencies and disasters can happen any time without any warning, and things can change and escalate very quickly, and often it is swift and decisive actions that make all the difference. It is a responsibility of the building facility management to ensure that a proven evacuation plan in place to cover various worst scenario to handled automatically inside the facility. To mapping out optimal safe escape routes is a straightforward undertaking, but does not necessarily guarantee residents the highest level of protection. The emergency evacuation navigation approach is a state-of-the-art that designed to evacuate human livings during an emergencies based on real-time decisions using live sensory data with pre-defined optimum path finding algorithm. The poor decision on causalities and guidance may apparently end the evacuation process and cannot then be remedied. This paper propose a cloud connected emergency evacuation system model to react dynamically to changes in the environment in emergency for safest emergency evacuation using IoT based emergency exit sign system. In the previous researches shows that the performance of optimal routing algorithms for evacuation purposes are more sensitive to the initial distribution of evacuees, the occupancy levels, and the type and level of emergency situations. The heuristic-based evacuees routing algorithms have a problem with the choice of certain parameters which causes evacuation process in real-time. Therefore, this paper proposes an evacuee routing algorithm that optimizes evacuation by making using high computational power of cloud servers. The proposed algorithm is evaluated via a cloud-based simulator with different "simulated casualties" are then re-routed using a Dijkstra's algorithm to obtain new safe emergency evacuation paths against guiding evacuees with a predetermined routing algorithm for them to emergency exits. The performance of proposed approach can be iterated as long as corrective action is still possible and give safe evacuation paths and dynamically configure the emergency exit signs to react for real-time instantaneous safe evacuation guidance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.317-321
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• 2022

In response to Incheon tap water accident in 2019, the Ministry of Environment has created the "Comprehensive Measures for Water Safety Management" to improve water operation management, provide systematic technical support, and respond to accidents. Accordingly, K-water is making a smart water supply management system for the entire process of tap water. In order to advance the response to water accidents, it is essential to secure the reliability of real-time water operation data such as flow rate, pressure, and water level, and to develop and apply a warning algorithm in advance using big data analysis techniques. In this paper, various statistical techniques are applied using water supply operation data (flow, pressure, water level, etc) to prepare the foundation for the selection of the optimal operating range and advancement of the monitoring and alarm system. In addition, the arrival time is analyzed through cross-correlation analysis of changes in raw water turbidity between the water intake and water treatment plants. The purpose of this paper is to study the model that predicts the raw water turbidity of a water treatment plant by applying raw water turbidity data considering the time delay according to the flow rate change.