• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.12
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• pp.975-985
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• 2018

The drones have been developed for military purposes and are now used in many fields such as logistics, communications, agriculture, disaster, defense and media. As the range of use of drones increases, cases of abuse of drones are increasing. It is necessary to develop anti-drone technology to detect the position of unwanted drones using the physical phenomena that occur when the drones fly. In this paper, we estimate the DOA(direction of arrival) of the drone by using the acoustic signal generated when the drone is flying. In addition, the dynamics model of the drones was applied to the unscented kalman filter to improve the microphone array detection performance and reduce the error of the position estimation. Through simulation, the drone detection performance was predicted and verified through experiments.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.9
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• pp.694-701
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• 2002

In this paper, through the study on locations of structural transducers for active control of the radiated sound from the vibrating plate, the active structural acoustic control (ASAC) system is proposed. And, for the evaluation of the proposed location, the experiment of the active structural acoustic control is implemented using the multi-channel filtered-x LMS algorithm and an additional filter (Acoustic Prediction Filter) to estimate the radiated sound using the acceleration signals of the plate. The structural transducers are piezoceramic actuator (PZT) and accelerometer. PZT is used as an actuator to reduce the vibration and the radiated sound. To maximize the control performance, each PZT actuator is located at the position that has the largest control sensitivity of the plate bending moment in the direction of x and y coordinates and the optimal PZT location is validated experimentally. Also, to find the acoustic prediction filter accurately, two accelerometers are located at the positions that have the largest radiation efficiencies of the plate, and the proposed locations are validated by simulation using the Rayleigh integral. The multi-channel filtered-x LMS algorithm is introduced to control a complex 2-D structural vibration mode. Finding the locations of structural transducers for active structural acoustic control of the radiated sound, the active structural acoustic control (ASAC) system can be presented and validated by experiments using a real time control system.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.21-32
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• 1996

The performance of the energy density control algorithm for controlling a broadband noise is evaluated in a one-dimensional enclosure. To avoid noncausality problem of a control filter, which often happens in a frequency domain optimization, analyses presented in this paper are undertaken in the time domain. This approach provides the form of the causally constrained optimal controller. Numerical results are presented to predict the performance of the active noise control system, and indicate that imp개ved global attenuation of the broadband noise can be achieved by minimizing the energy density, rather than the squared pressure. It is shown that minimizing the energy density at a single location yields global attenuation results that are comparable to minimizing the potential energy. Furthermore, unlike the squared pressure control, the energy density control does not demonstrate any dependence on the error sensor location for this one-dimensional field. A practical implementation of the energy-based control algorithm is presented. Results show that the energy density control can be implemented using the two sensor technique with a tolerable margin of performance degradation.