• Korean Journal of Remote Sensing
• /
• v.24 no.6
• /
• pp.535-549
• /
• 2008

Vicarious calibration for the satellite sensor relies on simulated TOA (Top-of-Atmosphere) radiances over various targets. In this study, TOA visible radiance was calculated over ocean targets which are located in five different regions over the Indian and Pacific ocean, and its possible use for the satellite sensor calibration was examined. TOA radiances are simulated with the 6S radiative transfer model for the comparison with MODIS/Terra and SeaWiFS measurements. Geometric angles and sensor characteristics of the reference satellites were taken into account for the simulation. AOT (Aerosol Optical Thickness) from MODIS/Terra, pigment concentrations from Sea WiFS, and ozone amount from OMI measurements were used as inputs to the model. Other atmospheric input parameters such as surface wind and total column water vapor were taken from NCEP/NCAR reanalysis data. The 5-day averaged radiances over all targets show that the percent differences between simulated and observed radiances are within about ${\pm}5%$ in year 2005, indicating that the calculated radiances are in good agreement with satellite measurements. It has also been shown that the algorithm can produce the SeaWiFS radiances within about ${\pm}5%$ uncertainty range. It has been suggested that the algorithm can be used as a tool for calibrating the VIS bands within about 5% uncertainty range.

• Korean Journal of Remote Sensing
• /
• v.35 no.2
• /
• pp.203-215
• /
• 2019

As hyperspectral sensors that can be mounted on drones are developed, it is possible to acquire hyperspectral imagery with high spatial and spectral resolution. Although the importance of atmospheric correction has been reduced since imagery of drones were acquired at a low altitude,studies on the conversion process from raw data to spectral reflectance should be done for studies such as estimating the concentration of surface materials using hyperspectral imagery. In this study, a vicarious radiometric calibration and an atmospheric correction algorithm based on atmospheric radiation transfer model were applied to hyperspectral data of drone and the results were compared and analyzed. The vicarious calibration method was applied to an empirical line calibration using the spectral reflectance of a tarp made of uniform material. The atmospheric correction algorithm used ATCOR-4 based Modran-5 that was widely used for the atmospheric correction of aerial hyperspectral imagery. As a result of analyzing the RMSE of the difference between the reference reflectance and the correction, the vicarious calibration using the tarp in a single period of hyperspectral image was the most accurate, but the atmospheric correction was possible according to the application purpose of using hyperspectral imagery. If the correction process of normalized spectral reflectance is carried out through the additional vicarious calibration for imagery from multiple periods in the future, accurate analysis using hyperspectral drone imagery will be possible.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.1
• /
• pp.46-54
• /
• 2021

In this paper, active noise control was performed to reduce radiated noise in the low frequency band of dishwashers. First, through an analysis of the noise environment of the dishwasher, it was confirmed that the pump noise contributed the most to the radiated noise in the low frequency band, From the result of the noise environment analysis, the reference signal was selected to be the vibration signal of the pump body. The reference signal was obtained by using the accelerometer on the pump body, which can prevent acoustic feedback. The error signal sensor was selected as a microphone located at 1 m in front of the dishwasher and 0.5 m in height. And to design the controller, the error signal and the reference signal were measured at the operational rpms of the dishwasher at 2,500 rpm, 2,600 rpm and 2,800 rpm, and the secondary path transfer function was measured. The designed controller was mounted on Digital Signal Processor (DSP) equipment, and the control performance was verified experimentally. As a result of the measurement at the 3 operational rpms, the 7th multiple component of pump operating frequency decreased by 1.93 dB, 4.43 dB, 5.15 dB per rpm, and the 12th multiple component decreased by 6.67 dB, 2.34 dB, 4.28 dB per rpm. And overall Sound Pressure Level (SPL) decreased by 0.84 dB, 2.58 dB, 1.48 dB by rpm.

• Journal of the Korean Geotechnical Society
• /
• v.22 no.6
• /
• pp.15-26
• /
• 2006

Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.10
• /
• pp.299-308
• /
• 2023

As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared, allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution of this paper satisfies throughput and fairness at the same time than other algorithms.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.2
• /
• pp.335-343
• /
• 2024

Real-time target signal processing techniques for FMCW LiDAR are described in this paper. FMCW LiDAR is gaining attention as the next-generation LiDAR for self-driving cars because of its detection robustness even in adverse environmental conditions such as rain, snow and fog etc. in addition to its long range measurement capability. The hardware architecture which is required for high-speed data acquisition, data transfer, and parallel signal processing for frequency-domain signal processing is described in this article. Fourier transformation of the acquired time-domain signal is implemented on FPGA in real time. The paper also details the C-FAR algorithm for ensuring robust target detection from the transformed target spectrum. This paper elaborates on enhancing frequency measurement resolution from the target spectrum and converting them into range and velocity data. The 3D image was generated and displayed using the 2D scanner position and target distance data. Real-time target signal processing and high-resolution image acquisition capability of FMCW LiDAR by using the proposed parallel signal processing algorithms based on FPGA architecture are verified in this paper.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.4
• /
• pp.89-96
• /
• 2016

In this paper, we study mobile data collector (MDC) based data-gathering schemes in wireless sensor networks. In Such networks, MDCs are used to collect data from the environment and transfer them to the sink. The majority of existing data-gathering schemes suffer from high data-gathering latency because they use only a single MDC. Although some schemes use multiple MDCs, they focus on maximizing network lifetime rather than minimizing packet delay. In order to address the limitations of existing schemes, this paper focuses on minimizing packet delay for given number of MDCs and minimizing the number of MDCs for a given delay bound of packets. To achieve the minimum packet delay and minimum number of MDCs, two optimization problems are formulated, and traveling distance and traveling time of MDCs are estimated. The interior-point algorithm is used to obtain the optimal solution for each optimization problem. Numerical results and analysis are presented to validate the proposed method.

• Journal of the Korean Society for Railway
• /
• v.19 no.3
• /
• pp.314-323
• /
• 2016

Overhead line voltage of DC railway traction substations has rising or falling characteristics depending on the acceleration and regenerative braking of the subway train loads. The suppression of this irregular fluctuation of the line voltage gives rise to improved energy efficiency of both the railway substation and the trains. This paper presents parameter estimation schemes using the RC circuit model for an overhead line voltage at a 1500V DC electric railway traction substation. A linear artificial neural network with a back-propagation learning algorithm was trained using the measurement data for an overhead line voltage and four feeder currents. The least square estimation method was configured to implement batch processing of these measurement data. These estimation results have been presented and performance analysis has been achieved through raw data simulation.

• Journal of Internet Computing and Services
• /
• v.17 no.5
• /
• pp.17-23
• /
• 2016

Wireless sensor-actuator networks (WSANs) enhance the existing wireless sensor networks (WSNs) by equipping sensor nodes with an actuator. The actuators work with the sensor nodes and perform application-specific operations. The WSAN systems have several applications such as disaster relief, intelligent building, military surveillance, health monitoring, and infrastructure security. These applications require capability of reliable data transfer to act responsively and accurately. Biologically inspired modeling techniques have received considerable attention for achieving robustness, scalability, and adaptability, while retaining individual simplicity. In this paper, an epidemic-inspired algorithm for data dissemination with delay constraints while minimizing energy consumption in WSAN is proposed. The steady states and system stability are analyzed using control theory. Also, simulation results indicate that the proposed scheme provides desirable dissemination delay and energy saving.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.23 no.6 s.165
• /
• pp.931-942
• /
• 1999

This paper develops a 3 DOF vehicle model which includes lateral, roll and yaw motion to study a 4WS vehicle. The model is used for the simulation of a 4WS vehicle behavior, and to derive a control algorithm for rear wheel steering. This paper uses a feedforward plus feedback control scheme to compute a rear wheel steering angle. The feedforward control scheme for computing the first rear wheel steering angle uses a gain which is acquired by multiplying a proper value on a gain to maintain a zero sideslip angle. The feedback control scheme for computing the second rear wheel steering angle uses fuzzy logic and model following control scheme. A linear 2 DOF model is used as a reference model for model following control, and is derived from the developed 3 DOF model by neglecting sprung mass roll motion. A reference state variable is yaw rate, and is computed using the linear 2 DOF model. J-turn and lane change maneuver simulation are performed to show the effectiveness of the developed control scheme. The simulation results show that the 4WS vehicle with the developed control scheme has much better performance in yaw rate, lateral acceleration, roll angle, and sideslip angle than the 2WS vehicle. Also, the results show that the performance of the developed control is close to the one of an optimal control which assumes all states are perfect.