• Title/Summary/Keyword: 신호변환

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Design and Implementation of BNN-based Gait Pattern Analysis System Using IMU Sensor (관성 측정 센서를 활용한 이진 신경망 기반 걸음걸이 패턴 분석 시스템 설계 및 구현)

  • Na, Jinho;Ji, Gisan;Jung, Yunho
    • Journal of Advanced Navigation Technology
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    • v.26 no.5
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    • pp.365-372
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    • 2022
  • Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

Comparative study of data augmentation methods for fake audio detection (음성위조 탐지에 있어서 데이터 증강 기법의 성능에 관한 비교 연구)

  • KwanYeol Park;Il-Youp Kwak
    • The Korean Journal of Applied Statistics
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    • v.36 no.2
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    • pp.101-114
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    • 2023
  • The data augmentation technique is effectively used to solve the problem of overfitting the model by allowing the training dataset to be viewed from various perspectives. In addition to image augmentation techniques such as rotation, cropping, horizontal flip, and vertical flip, occlusion-based data augmentation methods such as Cutmix and Cutout have been proposed. For models based on speech data, it is possible to use an occlusion-based data-based augmentation technique after converting a 1D speech signal into a 2D spectrogram. In particular, SpecAugment is an occlusion-based augmentation technique for speech spectrograms. In this study, we intend to compare and study data augmentation techniques that can be used in the problem of false-voice detection. Using data from the ASVspoof2017 and ASVspoof2019 competitions held to detect fake audio, a dataset applied with Cutout, Cutmix, and SpecAugment, an occlusion-based data augmentation method, was trained through an LCNN model. All three augmentation techniques, Cutout, Cutmix, and SpecAugment, generally improved the performance of the model. In ASVspoof2017, Cutmix, in ASVspoof2019 LA, Mixup, and in ASVspoof2019 PA, SpecAugment showed the best performance. In addition, increasing the number of masks for SpecAugment helps to improve performance. In conclusion, it is understood that the appropriate augmentation technique differs depending on the situation and data.

Statistical Techniques to Detect Sensor Drifts (센서드리프트 판별을 위한 통계적 탐지기술 고찰)

  • Seo, In-Yong;Shin, Ho-Cheol;Park, Moon-Ghu;Kim, Seong-Jun
    • Journal of the Korea Society for Simulation
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    • v.18 no.3
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    • pp.103-112
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    • 2009
  • In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

Comparison of CNN and GAN-based Deep Learning Models for Ground Roll Suppression (그라운드-롤 제거를 위한 CNN과 GAN 기반 딥러닝 모델 비교 분석)

  • Sangin Cho;Sukjoon Pyun
    • Geophysics and Geophysical Exploration
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    • v.26 no.2
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    • pp.37-51
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    • 2023
  • The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.

Finite element analysis for acoustic and temperature characteristics of a piezoelectric HIFU transducer at 10 MHz (10 MHz용 압전 HIFU 트랜스듀서의 음향 및 온도 특성에 대한 유한요소해석)

  • Jong-Ho Kim;Il-Gok Hong;Ho-Yong Shin;Hyo-Jun Ahn;Jong-In Im
    • Journal of the Korean Crystal Growth and Crystal Technology
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    • v.33 no.3
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    • pp.116-123
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    • 2023
  • A high intensity focuses ultrasound (HIFU) is one of the emerging technologies in the biomedical field. The piezoelectric HIFU transducer is a device that utilizes the thermal energy generated by high ultrasound energy. Recently an operating frequency of the HIFU transducer is to expand above a 7 MHz. In this study, the acoustic pressures and temperature distributions in the tissue that generated by the HIFU transducer at 10 MHz were calculated with the finite element method. In addition, the pressure focusing characteristics of the device were analyzed. The geometrical variables are the piezomaterial thickness, lens shape, water height, and film thickness. The results shown that the acoustic pressure increased and saturated gradually when the height/radius (HL/RL) ratio of the lens increased. Moreover, the focal area was gradually decreases with HL/RL ratio of the lens. In case of the optimized HIFU transducer, the maximum pressure and temperature were analyzed about 19 MPa and 65℃ respectively. And the -3 dB focused distances in the axial and lateral direction are around 2.3 mm and 0.23 mm respectively.

A Study on the Development of Harmonic Limit Device for Stabilizing Main Circuit Equipment of Train (열차운행 안정화를 위한 주회로 기기의 고조파 제한장치 개발에 관한 연구)

  • Kim, Sung Joon;Chae, Eun Kyung;Kang, Jeong Won
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.8 no.6
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    • pp.853-861
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    • 2018
  • This paper proposes the application of harmonic constraints to address the problems caused by abnormal voltage increases when electric railway vehicles are running. The AC line that supplies the train with power during operation is used to provide electricity of 25kV/60 Hz, but gradually the size and frequency of harmonics involved in the line are varied with the technological evolution of the railroad vehicle electrical equipment. An increase in heat losses due to the failure of the instrument transformer (PT), the main circuit device, which is a serious problem with the recent train safety operation, or to the main displacement voltage. When high frequency components are introduced through low frequency Transformers of the main circuit device, the high intensity of the components is caused by the high intensity of the core and the current flow of the parasitic core is increased, thus generating heat. To solve this problem, the recent adjustment of the sequence has applied artificial NOTCH OFF of the power converter. However, the method of receiving and controlling the OFF signal operates by interaction between the ground and the vehicle's devices, thus it is invalid in the event of failure, and an actual accident is occurring. Therefore, the harmonic currents were required to prevent possible flow of harmonics, and conducted a study to prevent accidental occurrence of train accidents and to verify feasibility of the device through the simulations of the train's experimental analysis and the simulations of the train for safe operation.

Development of Temperature Compensated Micro Cone by using Fiber Optic Sensor (광섬유를 이용한 온도 보상형 마이크로콘의 개발)

  • Kim, Raehyun;Lee, Woojin;Yoon, Hyung-Koo;Lee, Jong-Sub
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.4C
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    • pp.163-174
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    • 2009
  • Mechanical device using the load cell or strain gage sensor can be influenced by tempearute changes because temperature change can cause a shift in the load cell or straing gage output at zero loading. In this paper, micro cone penetrometers with 1~7mm in diameter, are developed by using an optical fiber sensor (FBG: Fiber Bragg Grating) to compensate the continous temperature change during cone penetration test. Note the temperature compensated method using optical fiber sensor which has hair-size in diameter, and is not affected by environmental conditions because the measured data is the wavelength shifting of the light instead of the intensity of the electric voltage. Temperature effect test shows that the output voltage of strain gage changes and increases with an increase in the temperature. A developed FBG cone penetrometer, however, achieves excellent temperature compensation during penetration, and produces continuous change of underground temperature. In addition, the temperature compensated FBG cone shows the excellent sensitivity and detects the interface of the layered soils with higher resolution. This study demonstrates that the fiber optic sensor renders the possibility of the ultra small size cone and the new fiber optic cone may produce more reliable temperature compensated tip resistance.

Entropy-Based 6 Degrees of Freedom Extraction for the W-band Synthetic Aperture Radar Image Reconstruction (W-band Synthetic Aperture Radar 영상 복원을 위한 엔트로피 기반의 6 Degrees of Freedom 추출)

  • Hyokbeen Lee;Duk-jin Kim;Junwoo Kim;Juyoung Song
    • Korean Journal of Remote Sensing
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    • v.39 no.6_1
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    • pp.1245-1254
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    • 2023
  • Significant research has been conducted on the W-band synthetic aperture radar (SAR) system that utilizes the 77 GHz frequency modulation continuous wave (FMCW) radar. To reconstruct the high-resolution W-band SAR image, it is necessary to transform the point cloud acquired from the stereo cameras or the LiDAR in the direction of 6 degrees of freedom (DOF) and apply them to the SAR signal processing. However, there are difficulties in matching images due to the different geometric structures of images acquired from different sensors. In this study, we present the method to extract an optimized depth map by obtaining 6 DOF of the point cloud using a gradient descent method based on the entropy of the SAR image. An experiment was conducted to reconstruct a tree, which is a major road environment object, using the constructed W-band SAR system. The SAR image, reconstructed using the entropy-based gradient descent method, showed a decrease of 53.2828 in mean square error and an increase of 0.5529 in the structural similarity index, compared to SAR images reconstructed from radar coordinates.

Spontaneous Speech Emotion Recognition Based On Spectrogram With Convolutional Neural Network (CNN 기반 스펙트로그램을 이용한 자유발화 음성감정인식)

  • Guiyoung Son;Soonil Kwon
    • The Transactions of the Korea Information Processing Society
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    • v.13 no.6
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    • pp.284-290
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    • 2024
  • Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

Implementation of Non-Stringed Guitar Based on Physical Modeling Synthesis (물리적 모델링 합성법에 기반을 둔 줄 없는 기타 구현)

  • Kang, Myeong-Su;Cho, Sang-Jin;Chong, Ui-Pil
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.2
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    • pp.119-126
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    • 2009
  • This paper describes the non-stringed guitar composed of laser strings, frets, sound synthesis algorithm and a processor. The laser strings that can depict stroke and playing arpeggios comprise laser modules and photo diodes. Frets are implemented by voltage divider. The guitar body does not need to implement physically because commuted waveguide synthesis is used. The proposed frets enable; players to represent all of chords by the chord glove as well as guitar solo. Sliding, hammering-on and pulling-off sounds are synthesized by using parameters from the voltage divider. Because the pitch shifting corresponds to the time-varying propagation speed in the digital waveguide model, the proposed model can synthesize vibrato as well. After transformation of signals from the laser strings and frets into parameters for synthesis algorithm, the digital signal processor, TMS320F2812, performs the real-time synthesis algorithm and communicates with the DAC. The demonstration movieclip available via the Internet shows one to play a song, 'Arirang', synthesized by proposed algorithm and interfaces in real-time. Consequently, we can conclude that the proposed synthesis algorithm is efficient in guitar solo and there is no problem to play the non-stringed guitar in real-time.