• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.427-427
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• 2021

During the rainy season, heavy downpours are always a source of concern for the world. Flooding and heavy rains can devastate communities, disrupt agriculture, and contribute to traffic accidents.. Weir and flow hall effect sensors are the conventional analytical methods for measuring flow rate; in this paper, we analyzed manhole flowrate statistics. The measurement of the flow rate of a notch/weir is a time-consuming task that necessitates continuous mathematical analysis. . We created three types of IoT sensors in this study: (HC-SR04 ultrasonic, YF-S201 magnetic, and HB100 radar), which take the sensor's real-time input signal and estimate the flow using a notch equation and a previously calibrated optimized coefficient of discharge. The proposed systems are cost-effective, but in terms of accuracy, we found that the HC-SR04 ultrasonic sensor is the best of the three systems

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.311-314
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF. To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKP uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.264-270
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• 2017

A Doppler radar sensor was applied to detect respirations and heartbeats of persons who were lying on a bed. This study is preliminary study aiming at non-contact and non-intrusive respiration and heart rate monitoring during sleep in daily life. For the experiments, 10GHz Doppler radar with patch-type antenna was used and installed on the upper right and the distance between the body and the antenna was 1 m. The results show that each signal of respiration and heartbeat is observed in each frequency band however the frequency band and the waveform vary according to the subjects and the posture. The results show that the heartbeats can be detected with the peak detection in some frequency band. This study shows the feasibility of applying the Doppler radar to detection of heartbeat and respiration during sleep and further studies about heartbeat detection algorithm are required.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.11
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• pp.1171-1177
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• 2009

A missile warning radar is an essential sensor for active protection system to detect antitank missile in all weather environments. This paper introduces missile warning radar for main battle tank and presents the results of the design and implementation of the radar signal processor using high speed multi-DSP. The key algorithms include adaptive CF AR, weighted linear fitting algorithm, S/W tracking capability, and threat decision and present test result.

• Journal of IKEEE
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• v.24 no.3
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• pp.777-782
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• 2020

In this paper, we propose CNN-based HMI system using Doppler radar and voice sensor, and present hardware design and implementation results. To overcome the limitation of single sensor monitoring, the proposed HMI system combines data from two sensors to improve performance. The proposed system exhibits improved performance by 3.5% and 12% compared to a single radar and voice sensor-based classifier in noisy environment. In addition, hardware to accelerate the complex computational unit of CNN is implemented and verified on the FPGA test system. As a result of performance evaluation, the proposed HMI acceleration platform can be processed with 95% reduction in computation time compared to a single software-based design.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.105-115
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• 2017

Conventional traffic detection sensors such as loop detectors and image sensors are expensive to install and maintain and require different detection algorithms depending on the night and day and have a disadvantage that the detection rate varies widely depending on the weather. On the other hand, the millimeter-wave radar is not affected by bad weather and can obtain constant detection performance regardless of day or night. In addition, there is no need for blocking trafficl for installation and maintenance, and multiple vehicles can be detected at the same time. In this study, a multi-target detection algorithm for a radar sensor with this advantage was devised / implemented by applying a conventional single target detection algorithm. We performed the evaluation and the meaningful results were obtained.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.119-125
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• 2022

The power supply for the anti-aircraft radar homing sensor should allow the system to receive power quickly and stably without the influence of noise. For this purpose, DC-DC converters are widely used for reliable power conversion. Also, switching of DC-DC converters Frequency noise should not cause false alarms and ghosts that may affect the detection and tracking performance of the system, and it should have a check function that can monitor power in real time while the homing sensor is operating. In order to apply to anti-aircraft radar homing sensor, we developed a multi-output switching power supply with maximum output 𐩒𐩒𐩒 W, efficiency 80% or more (@100% load), output power by receiving 28VDC input, and power supply to achieve more than 80% efficiency. A DC-DC converter was applied to this large output, and the multi-output flyback method was applied to the rest of the low-power output.

• Journal of the Korean earth science society
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• v.33 no.7
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• pp.596-607
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• 2012

The R-Z relationship is one of important error factors to determine the accuracy of radar rainfall estimation. In this study, we have explored the effect of the R-Z relationships derived from disdrometer data in estimating the radar rainfall. The heavy rain event that produced flooding in St-Remi, Quebec, Canada has been occurred. We have tried to investigate the severity of rain for this event using high temporal (2.5 min) and spatial resolution ($1^{\circ}$ by 250 m) radar data obtained from the McGill S-band radar. Radar data revealed that the heavy rain cells pass directly over St-Remi while the coarse raingauge network was not sufficient to detect this rain event. The maximum 30 min (1 h) accumulation reaches about 39 (42) mm in St-Remi. During the rain event, the two disdrometers (POSS; Precipitation Occurrence Sensor System) were available: One used for the reflectivity calibration by comparing disdrometer Z and radar Z and the other for deriving disdrometric R-Z relationships. The result shows the significant improvement with the disdrometric reflectivity-dependent R-Z relationships against the climatological R-Z relationship. The bias in radar rain estimation is reduced from +12% to -2% and the root-mean squared error from 16 to 10% for daily accumulation. Using the estimated radar rainfall rate with disdrometric R-Z relationships, the flood event was well captured with proper timing and amount.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.51-64
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• 2009

A location tracking sensor such as GPS, INS, Radar, and optical equipments is used in tracking Maneuvering Targets with a multi-sensor, and such systems are used to track, detect, and control UAV, guided missile, and spaceship. Until now, Most of the studies related to tracking Maneuvering Targets are on fusing multiple Radars, or adding a supplementary sensor to INS and GPS. However, A study is required to change the degree of application in fusions since the system property and error property are different from sensors. In this paper, we perform the error analysis of the sensor properties by adding a ground radar to GPS and INS for improving the tracking performance by multi-sensor fusion, and suggest the tracking algorithm that improves the precision and stability by changing the sensor probability of each sensor according to the error. For evaluation, we extract the altitude values in a simulation for the trajectory of UAV and apply the suggested algorithm to carry out the performance analysis. In this study, we change the weight of the evaluated values according to the degree of error between the navigation information of each sensor to improve the precision of navigation information, and made it possible to have a strong tracking which is not affected by external purposed environmental change and disturbance.