• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.37-42
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• 2022

A high-performance wideband transition from microstrip to waveguide is proposed. This transition is designed by consideration of gradual field transformation and optimal impedance matching between microstrip line and fin-line. Clear design guidelines of proposed transition using fin-line taper with offset DSPSL (double-sided parallel stripline) are provided to determine the transition shape and the transition length. The fabricated transition exhibits less than 0.67 dB insertion loss per transition for frequencies from 85 to 108 GHz, and less than 1 dB insertion loss from 83 to over 110 GHz. Proposed transition is expected compact radar and various applications.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.42-56
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• 2022

The thesis is automated traffic control equipment system aims to improve. Areas where improvement is needed about the existing automated traffic control equipment by applying the latest technology and to improve the things that can be derived. Reflecting the derived improvements, we intend to present a plan for the design and implementation of a new unmanned traffic control device. The main improvements were designed to change the housing material of the unmanned traffic control equipment, simplify the configuration of the equipment, reduce the weight of the equipment, and change the purpose of the software. In order to evaluate the objective performance of the improved unmanned traffic control equipment through this study, it was requested to a public certification authority. The reliability of the equipment was secured through KC certification and durability test. It is intended to present the feasibility of securing the marketability of the unmanned traffic control equipment by comparing and evaluating the construction period and installation cost with the existing unmanned traffic control equipment.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.175-189
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• 2023

Fault detection and diagnosis (FDI) algorithms are actively being researched for ensuring the integrity and reliability of environment perception sensors in autonomous vehicles. In this paper, a fault detection algorithm based on a multi-sensor perception system composed of radar, camera, and lidar is proposed to guarantee the safety of an autonomous vehicle's perception system. The algorithm utilizes reference generation filters and residual generation filters based on finite impulse response (FIR) filter estimates. By analyzing the residuals generated from the filtered sensor observations and the estimated state errors of individual objects, the algorithm detects faults in the environment perception sensors. The proposed algorithm was evaluated by comparing its performance with a Kalman filter-based algorithm through numerical simulations in a virtual environment. This research could help to ensure the safety and reliability of autonomous vehicles and to enhance the integrity of their environment perception sensors.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.153-153
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• 2022

Large-scale and accurate observations at fine spatial resolution through a means of remote sensing offer an effective tool for capturing rainfall variability over the traditional rain gauges and weather radars. Although satellite rainfall products (SRPs) derived using two major estimation approaches were evaluated worldwide, their practical applications suffered from limitations. In particular, the traditional top-down SRPs (e.g., IMERG), which are based on direct estimation of rain rate from microwave satellite observations, are mainly restricted with their coarse spatial resolution, while applications of the bottom-up approach, which allows backward estimation of rainfall from soil moisture signals, to novel high spatial resolution soil moisture satellite sensors over South Korea are not introduced. Thus, this study aims to evaluate the performances of a state-of-the-art bottom-up SRP (the self-calibrated SM2RAIN model) applied to the C-band SAR Sentinel-1, a statistically downscaled version of the conventional top-down IMERG SRP, and their integration for a targeted high spatial resolution of 0.01° (~ 1-km) over central South Korea, where the differences in climate zones (coastal region vs. mainland region) and vegetation covers (croplands vs. mixed forests) are highlighted. The results indicated that each single SRP can provide plus points in distinct climatic and vegetated conditions, while their drawbacks have existed. Superior performance was obtained by merging these individual SRPs, providing preliminary results on a complementary high spatial resolution SRP over central South Korea. This study results shed light on the further development of integration framework and a complementary high spatial resolution rainfall product from multi-satellite sensors as well as multi-observing systems (integrated gauge-radar-satellite) extending for entire South Korea, toward the demands for urban hydrology and microscale agriculture.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.885-894
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• 2023

Setting RAM target value for a defense system plays a crucial role in the development and design phases and the production phase as well. It is apparent that the achieved RAM target value can help maximizing the combat capability of a defense system and improving its performance throughout the system's life cycle from acquisition phase to disposal. Usually a defense system operates according to its OMS/MP (Operational Mode Summary / Mission Profile) and it consists of several subsystems which are supposed to be operated at each utilization rate under its operating conditions and the mission profiles assigned. In this study, a method of setting RAM target value is proposed for a defense system that are composed of several independent subsystems considering their utilization rates and the mission profiles assigned. And the case study of applying the proposed method of setting RAM target value to system A is dealt with.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Journal of Ocean Engineering and Technology
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• v.38 no.2
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• pp.64-73
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• 2024

Oil spills pose significant threats to marine ecosystems, human health, socioeconomic aspects, and coastal communities. Accurate real-time predictions of oil slick transport along coastlines are paramount for quick preparedness and response efforts. This study used an open-source OpenOil numerical model to simulate the fate and trajectories of oil slicks released during the 2007 Hebei Spirit accident along the Korean coasts. Six combinations of input parameters, derived from a five-day met-ocean dataset incorporating various hydrodynamic, meteorological, and wave models, were investigated to determine the input variables that lead to the most reasonable results. The predictive performance of each combination was evaluated quantitatively by comparing the dimensions and matching rates between the simulated and observed oil slicks extracted from synthetic aperture radar (SAR) data on the ocean surface. The results show that the combination incorporating the Hybrid Coordinate Ocean Model (HYCOM) for hydrodynamic parameters exhibited more substantial agreement with the observed spill areas than Copernicus Marine Environment Monitoring Service (CMEMS), yielding up to 88% and 53% similarity, respectively, during a more than four-day oil transportation near Taean coasts. This study underscores the importance of integrating high-resolution met-ocean models into oil spill modeling efforts to enhance the predictive accuracy regarding oil spill dynamics and weathering processes.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.136-141
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• 2024

In this paper, a broadband inset-fed microstrip patch antenna operating at 10.525 GHz band was proposed. The proposed broadband inset-fed microstrip patch antenna consists of three narrow rectangular patches. At the center of the center patch, two symmetrical side patches were connected by a strip conductor and were arranged with their centers shifted in a perpendicular direction with respect to the center patch. For performance comparison, a conventional inset-fed square microstrip patch antenna was designed. Experiment results show that the frequency band of the measured input reflection coefficient with a voltage standing wave ratio less than 2 for the broadband inset-fed microstrip patch antenna was 10.036-11.051 GHz (9.63%), whereas that for the conventional inset-fed rectangular microstrip patch antenna was 10.306-10.772 GHz (4.42%). Therefore, the input reflection coefficient frequency bandwidth of the fabricated broadband inset-fed microstrip patch antenna was increased by 2.18 times, compared to the conventional inset-fed square microstrip patch antenna.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.3
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• pp.52-58
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• 2009

If a moving target has a linear characteristics, the Kalman filter can estimate relatively accurate the location of a target, but this performance depends on how the dynamic status characteristics of the target is accurately modeled. In many practical problems of tracking a maneuvering target, a simple kinematic model can fairly accurately describe the target dynamics for a wide class of maneuvers. However, since the target can exhibit a wide range of dynamic characteristics, no fixed SKF(Simple Kalman filter) can be matched to estimate, to the required accuracy, the states of the target for every specific maneuver. In this paper, a new AKF(Active Kalman filter) is proposed to solve this problem The process noise covariance level of the Kalman filter is adjusted at each time step according to the study result which uses the neural network algorithm. It is demonstrated by means of a computer simulation that the tracking capability of the proposed AKF(Active Kalman filter) is better than that of the SKF(Simple Kalman Filter).