• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.577-587
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• 2021

Drought is generally considered to be a natural disaster caused by accumulated water shortages over a long period of time, taking months or years and slowly occurring. However, climate change has led to rapid changes in weather and environmental factors that directly affect agriculture, and extreme weather conditions have led to an increase in the frequency of rapidly developing droughts within weeks to months. This phenomenon is defined as 'Flash Drought', which is caused by an increase in surface temperature over a relatively short period of time and abnormally low and rapidly decreasing soil moisture. The detection and analysis of flash drought is essential because it has a significant impact on agriculture and natural ecosystems, and its impacts are associated with agricultural drought impacts. In South Korea, there is no clear definition of flash drought, so the purpose of this study is to identify and analyze its characteristics. In this study, flash drought detection condition was presented based on the satellite-derived drought index Evaporative Stress Index (ESI) from 2014 to 2018. ESI is used as an early warning indicator for rapidly-occurring flash drought a short period of time due to its similar relationship with reduced soil moisture content, lack of precipitation, increased evaporative demand due to low humidity, high temperature, and strong winds. The flash droughts were analyzed using hydrometeorological characteristics by comparing Standardized Precipitation Index (SPI), soil moisture, maximum temperature, relative humidity, wind speed, and precipitation. The correlation was analyzed based on the 8 weeks prior to the occurrence of the flash drought, and in most cases, a high correlation of 0.8(-0.8) or higher(lower) was expressed for ESI and SPI, soil moisture, and maximum temperature.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.15-22
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• 2014

This paper presents the implementation of a control system of pellet boiler using wood pellet as carbon neutral material. The system also has the additional features to provide remote controlling and monitoring based on home networking technology through either public switched telephone networks or mobile communication networks. It consists of three kinds of sub-modules; a main controller provides basic and additional features such as a setting of temperature, a supplying of wood pellet, a controlling of ignition and fire-power, and a removing of soot. The second is temperature controller of individual rooms which is connected to the main controller through RS-485 links. And interface modules with PSTN and mobile networks can support remote controlling and monitoring the functions. The test results under the heating area of $172m^2$ show a thermal efficiency of 93.6%, a heating power of 20,640kcal/hr, and a fuel consumption of 5.54kg/hr. These results are superior to those of the conventional pellet boilers. In order to obtain the such high performance, we newly applied a 3-step ignition flow, a flame detection by $C_dS$ sensor, and a fire-power control by fine controlling of shutter to our pellet boiler.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.1
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• pp.81-88
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• 2003

For driver's convenience, the ACC(Adaptive Cruise Control) requires a system which determines the direction of vehicles and controls the vehicle to keep the distance among the automobiles constant. This paper describes the microstrip array antennas designed to operate at 24 GHz, and used as a direction indicator of moving vehicles. 8${\times}$2 transmit array antenna with wide beamwidth, 8${\times}$4 receive center array antenna, and two 8${\times}$8 receive array antennas with narrow beamwidth were designed and fabricated. Measurement results for the arrays showed that the azimuthal beamwidth is 50$^{\circ}$and the gain is 16.7 dBi for the transmit array antenna. For the receive array antenna, the center, the left, and the right array antenna have beamwidths of 20$^{\circ}$, 13$^{\circ}$, 13$^{\circ}$respectively, and have gains of more than 20 dBi. The left and right array antenna have the beam tilt angle of ${\pm}$18$^{\circ}$. The measured radiation patterns showed a good agreement with the simulated patterns, and the designed array antennas are suitable fur detecting 3 directions of the vehicle within the scan angle area.

• Journal of the Korean Magnetics Society
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• v.18 no.5
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• pp.174-179
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• 2008

The measurement dissolution sensor system using GMR-SV device with magnetic sensitivity of 0.8 %/Oe and Mg-film thick of 200 nm and Mg-foil thick of 50 mm was fabricated and characterized. During the water dissolving process of Mg-film and Mg-foil, the subtle variation of magnetic field by the decrease of current in solenoid was detected by the GMR-SV sensor. The variations of Mg bubble number and ORP as a function of time for three different kinds of edible, tap, and distilled water, are measured and compared. A After 45 min, the speed of fast dissolving Mg was shown the order of edible > tap > DI water. The variation of output magnetoresistance as a function of dissolved time of Mg-film and Mg-foil for edible water, which is composed of mineral content of $0.8{\sim}5.4\;mg/l$ was investigated. The response times for the dissolution in edible water were 5 min and 20 min, respectively. From the measurement of dissolving time and speed for Mg-film and Mg-foil using GMR-SV device, the mineral Mg sensor system in edible water can be possible to develop.

• The Journal of Bigdata
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• v.7 no.2
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• pp.31-41
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• 2022

Steel is one of the most fundamental components to mechanical industry. However, the quality of products are greatly impacted by the surface defects in the steel. Thus, researchers pay attention to the need for surface defects detector and the deep learning methods are the current trend of object detector. There are still limitations and rooms for improvements, for example, related works focus on developing the models but don't take into account real-time application with practical implication on industrial settings. In this paper, a real-time application of steel surface defects detection based on YOLOv4 is proposed. Firstly, as the aim of this work to deploying model on real-time application, we studied related works on this field, particularly focusing on one-stage detector and YOLO algorithm, which is one of the most famous algorithm for real-time object detectors. Secondly, using pre-trained Yolov4-Darknet platform models and transfer learning, we trained and test on the hot rolled steel defects open-source dataset NEU-DET. In our study, we applied our application with 4 types of typical defects of a steel surface, namely patches, pitted surface, inclusion and scratches. Thirdly, we evaluated YOLOv4 trained model real-time performance to deploying our system with accuracy of 87.1 % mAP@0.5 and over 60 fps with GPU processing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.96-105
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• 2011

Ubiquitos sensor network(USN) is a technology which is widely used in our life. This paper introduces an example of design and implementation for a system which is based on the USN technology and can provide an efficient management tool for a space that should be precisely controlled for a certain range of uniformity in temperature and humidity. This introduced system builds a wireless sensor network using a number of sensor modules that are equipped with temperature and humidity sensors, and collects temperature and humidity information in real-time while simultaneously providing a method for monitoring the status of temperature and humidity by the graphical user interface. Also, the system will give a warning signal if the monitored values are differ from the pre-specified values of temperature and humidity for each sensor module more than a certain amount of tolerance. This temperature and humidity logging and monitoring system can perform better management for the space easily and efficiently by automating the existing manual method for data collection and management. Furthermore, using the stored data, it can make possible to perform post-analysis on the problems caused by temperature and humidity and to obtain information for environmental enhancement for the space.

• Smart Media Journal
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• v.12 no.11
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• pp.57-66
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• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.640-647
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• 2016

By minimizing fluorescence interference phenomena, aequorin-based luminescence technology can provide a relatively sensitive detection platform with integration of $G{\alpha}16$ protein in order to track internal calcium mobilization by G protein-coupled receptors (GPCR). In this type of cell-based functional assay format, it is essential to optimize the transfection process of a receptor and $G{\alpha}16$ protein. For this study, corticotropin releasing factor receptor subtype 2(CRF2) was set as a model system to generate three stable cells with CRF2 and $G{\alpha}16$ in addition to transiently transfected cells under three different conditions. Agonist (sauvagine) and antagonist (K41498) responses in those cells were analyzed to develop the optimum transfection process. As a result, the effective signal ratio in the dose response experiments of sauvagine and K41498 were at least 10-fold higher (z'=0.77) in CRF2-$G{\alpha}16$ stable cells. For the transient transfection cells, stable expression of $G{\alpha}16$ prior to the CRF2 represented a two-fold higher signal (z'=0.84) than the other cases of transient transfection. In conclusion, for the utilization of transient transfection processes to develop a cell-based GPCR functional assay system, it is suggested to introduce various target receptors after stable expression of $G{\alpha}16$ protein.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.129-143
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• 2019

As the importance of autonomous vehicle safety is emphasized, the application of ISO-26262, a development verification guideline for improving safety and reliability, and the safety verification of autonomous vehicles are becoming increasingly important, in particular, SAE standard level 3 or higher level autonomous vehicles detect and decision the surrounding environment instead of the human driver. Therefore, if there is and failure or malfunction in the autonomous driving function, safety may be seriously affected. So autonomous vehicles, it is essential to apply and verity the safety concept against failure and malfunctions. In this study, we study the fault injection scenarios for safety evaluation and verification of autonomous vehicles using ISO-26262 part3 process and STPA were studied and safety measures for safety concept design were studied through simulation bases fault injection test.