• 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.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.55-60
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• 2021

This study is to check waterproof by number of twists of fire hose and measure the first waterproof time to analyze the relationship between twists of fire hose and first waterproof time and waterproof by position of twists so as to suggest the efficient plan to prevent twists of fire hose. Ordinary fire hose did not make waterproof in case that position of twists was near the nozzle with twists 5 times or more, while anti-twist fire hose had no problem for waterproof only with delayed time. Like ordinary fire hose, anti-twist fire hose also showed the tendency to increase the waterproof time in proportion to the number of twists. In case that the position of twists was near waterproof port even with 10 times of twists in anti-twist fire hose, the first waterproof time was increase by 0.63 seconds on average without any problem for waterproof, which was somewhat faster than that in ordinary fire hose. With respect to the position of twists, waterproof of anti-twist fire hose was affected more as the number of twists was increased more near the nozzle rather than near the waterproof port, like ordinary fire hose. In summary, anti-twist fire hose equipped with anti-twist tool at the middle connection port and the nozzle showed a good waterproof performance with delayed waterproof time regardless of number of twists, as a solution for the twist problem of ordinary fire hose.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.468-479
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• 2022

To detect targets with active sonar system in the underwater environments, the targets are localized by receiving the echoes of the transmitted sounds reflected from the targets. In this case, reverberation from the scatterers is also generated, which prevents detection of the target echo. To detect the target effectively, reverberation suppression techniques such as pre-whitening based on autoregressive model and principal component inversion have been studied, and recently a Non-negative Matrix Factorization (NMF)-based technique has been also devised. The NMF-based reverberation suppression technique shows improved performance compared to the conventional methods, but the geometry of the transducer and receiver and attenuation by distance have not been considered. In this paper, the performance is improved through preprocessing such as the directionality of the receiver, Doppler related thereto, and attenuation for distance, in the case of using a continuous wave with a bistatic sonar. In order to evaluate the performance of the proposed system, simulation with a reverberation model was performed. The results show that the detection probability performance improved by 10 % to 40 % at a low false alarm probability of 1 % relative to the conventional non-negative matrix factorization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1248-1254
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• 2022

Efforts are being made to prevent traffic accidents in the school zone in advance. However, traffic accidents in school zones continue to occur. If the driver can know the situation information in the child protection area in advance, accidents can be reduced. In this paper, we design a camera that eliminates blind spots in school zones and a number recognition camera system that can collect pre-traffic information. It is designed by improving the LIDAR system that recognizes vehicle speed and pedestrians. It collects and processes pedestrian and vehicle image information recognized by cameras and LIDAR, and applies artificial intelligence time series analysis and artificial intelligence algorithms. The artificial intelligence traffic accident prevention system learned by deep learning proposed in this paper provides a forced push service that delivers school zone information to the driver to the mobile device in the vehicle before entering the school zone. In addition, school zone traffic information is provided as an alarm on the LED signboard.

• BMB Reports
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• v.54 no.11
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• pp.557-562
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• 2021

Microglial activation is closely associated with neuroinflammatory pathologies. The nucleotide-binding and oligomerization domain-like receptor containing a pyrin domain 3 (NLRP3) inflammasomes are highly organized intracellular sensors of neuronal alarm signaling. NLRP3 inflammasomes activate nuclear factor kappa-B (NF-κB) and reactive oxygen species (ROS), which induce inflammatory responses. Moreover, NLRP3 dysfunction is a common feature of chronic inflammatory diseases. The present study investigated the effect of a novel thiazol derivative, N-cyclooctyl-5-methylthiazol-2-amine hydrobromide (KHG26700), on inflammatory responses in lipopolysaccharide (LPS)-treated BV-2 microglial cells. KHG26700 significantly attenuated the expression of several pro-inflammatory cytokines, including tumor necrosis factor-α, interleukin-1β, and interleukin-6, in these cells, as well as the LPS-induced increases in NLRP3, NF-κB, and phospho-IkBα levels. KHG26700 also suppressed the LPS-induced increases in protein levels of autophagy protein 5 (ATG5), microtubule-associated protein 1 light chain 3 (LC3), and beclin-1, as well as downregulating the LPS-enhanced levels of ROS, lipid peroxidation, and nitric oxide. These results suggest that the anti-inflammatory effects of KHG26700 may be due, at least in part, to the regulation of the NLRP3-mediated signaling pathway during microglial activation.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3604-3613
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• 2022

The Physical Protection System (PPS) plays an important role and must effectively deal with various adversary attacks in nuclear security. In specific single adversary path scenarios, we can calculate the PPS effectiveness by EASI (Estimated Adversary Sequence Interruption) through Probability of Interruption (P_I) calculation. EASI uses a single value of the probability of detection (P_D) and the probability of alarm communications (P_C) in the PPS. In this study, we develop a multi-path analysis code based on EASI to evaluate the effectiveness of PPS. Our quantification method for P_I considers the variability and uncertainty of P_D and P_C value by Monte Carlo simulation. We converted the 2-D scheme of the nuclear facility into an Adversary Sequence Diagram (ASD). We used ASD to find the adversary path with the lowest probability of interruption as the most vulnerable paths (MVP). We examined a hypothetical facility (Hypothetical National Nuclear Research Facility - HNNRF) to confirm our code compared with EASI. The results show that implementing the variability extension can estimate the P_I value and its associated uncertainty. The multi-path analysis code allows the analyst to make it easier to assess PPS with more extensive facilities with more complex adversary paths. However, the variability of the P_D value in each protection element allows a significant decrease in the PI value. The possibility of this decrease needs to be an important concern for PPS designers to determine the P_D value correctly or set a higher standard for PPS performance that remains reliable.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.1-8
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• 2021

In this paper, we propose the construction of an IOT that monitors foresters' physical abnormalities in real time, performs emergency measures, and provides alarms for natural disasters or heatstroke such as a nearby forest fire or landslide. Nodes provided to foresters include 6-axis sensors, temperature sensors, GPS, and LoRa, and transmit the measured data to the network server through the gateway using LoRa communication. The network server uses 6-axis sensor data to determine whether or not a forester has any signs of abnormal body, and performs emergency measures by tracking GPS location. After analyzing the temperature data, it provides an alarm when there is a possibility of heat stroke or when a forest fire or landslide occurs in the vicinity. In this paper, it was confirmed that the real-time detection of physical abnormalities of foresters and the establishment of disaster early warning IOT is possible by analyzing the data obtained by constructing a node and a gateway and constructing a network server.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.59-68
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• 2022

We propose the vehicle maintenance support system to alarm consumable replacement reminders to the vehicle owner. Since the delayed replacement of the consumables makes the condition of the vehicle worse, it is crucial to replace consumables in a recommended period. The vehicle maintenance support system alarms the replacement time, which is set by the vehicle owner, based on the mileage of the installed vehicle. It integrates speed information acquired from the Controller Area Network interface for communication between Electronic Control Unit and instrument panel, exposed at the On Board Diagnostics-II port, to calculate the vehicle mileage. By this, there is no additional wiring required for the system. We verify the system has only 0.28% error by comparing the mileage on the system with the instrument cluster on the vehicle. It automatically enters low-power mode consuming 15mW, which is a negligible amount for the typical conditions of the car, to prevent the vehicle battery from discharging when the ignition is off.