• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.34 no.6
• /
• pp.363-370
• /
• 2021

In this paper, we presented an optimized crack and flexural strength analysis of a glass-fiber reinforced polymer (GFRP) rebar, used as reinforcements for in-site railway concrete slabs. The insulation performance of a GFRP rebar has the advantage of avoiding the loss of signal current in an audio frequency (AF) track circuit. A full-scale experiment, and three-dimensional finite element simulation results were compared to validate our approaches. Parametric numerical results revealed that the diameters and arrangements of the GFRP rebar had a significant effect on the flexural strength and crack control performances of the concrete track slabs. The results of this study could serve as a benchmark for future guidelines in designing more efficient, and economical concrete slabs using the GFRP rebar.

• Korean Journal of Audiology
• /
• v.18 no.3
• /
• pp.105-111
• /
• 2014

Assistive listening devices (ALDs) refer to various types of amplification equipment designed to improve the communication of individuals with hard of hearing to enhance the accessibility to speech signal when individual hearing instruments are not sufficient. There are many types of ALDs to overcome a triangle of speech to noise ratio (SNR) problems, noise, distance, and reverberation. ALDs vary in their internal electronic mechanisms ranging from simple hard-wire microphone-amplifier units to more sophisticated broadcasting systems. They usually use microphones to capture an audio source and broadcast it wirelessly over a frequency modulation (FM), infra-red, induction loop, or other transmission techniques. The seven types of ALDs are introduced including hardwire devices, FM sound system, infra-red sound system, induction loop system, telephone listening devices, television, and alert/alarm system. Further development of digital wireless technology in hearing instruments will make possible direct communication with ALDs without any accessories in the near future. There are two technology solutions for digital wireless hearing instruments improving SNR and convenience. One is near-field magnetic induction combined with Bluetooth radio frequency (RF) transmission or proprietary RF transmission and the other is proprietary RF transmission alone. Recently launched digital wireless hearing aid applying this new technology can communicate from the hearing instrument to personal computer, phones, Wi-Fi, alert systems, and ALDs via iPhone, iPad, and iPod. However, it comes with its own iOS application offering a range of features but there is no option for Android users as of this moment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.584-588
• /
• 2021

The impedance of the speaker is likely to be recognized as a fixed value. However, speaker impedance continues to vary with frequency variation, especially larger in resonant frequency region. The sound pressure level of loudspeakers is determined by the current flowing throughout the coil that consists loudspeakers. If loudspeakers are driven by voltage, sound pressure level of the loudspeaker is distorted by the variation of loudspeaker impedance. Current-drive of loudspeakers can solve this problem, but distortion of sound pressure level occurs in low frequencies due to resonance. The distortion can degrade the sound quality of the sound system. So to solve this problem, In this paper, we propose a resonance compensation circuit using DSP. we simulates audio systems using an equivalent model of loudspeakers to verify distortion of sound pressure level due to impedance variation and propose a circuit to compensate it. The proposed circuit is configured using a state variable filter and it can adjust the center frequency and output, so it will be used various sound systems.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.7
• /
• pp.39-46
• /
• 2016

This paper describes the third order feedforward delta-sigma modulator with inverter-based integrators and a 1.5bit comparator for the application of audio signal processing. The proposed 3rd-order delta-sigma modulator is multi-bit structure using 1.5 bit comparator instead of operational amplifier. This delta-sigma modulator has high SNR compared with single-bit 4th-order delta-sigma modulator in a low OSR. And it minimizes power consumes and simplified circuit structure using inverter-based integrator and using inverter-based integrator as analogue adder. The modulator was designed with 0.18um CMOS standard process and total chip area is $0.36mm^2$. The measured power cosumption is 28.8uW in a 0.8V analog supply and 66.6uW in a 1.8V digital supply. The measurement result shows that the peak SNDR of 80.7 dB, the ENOB of 13.1bit and the dynamic range of 86.1 dB with an input signal frequency of 2.5kHz, a sampling frequency of 2.56MHz and an oversampling rate of 64. The FOM (Walden) from the measurement result is 269 fJ/step, FOM (Schreier) was calculated as 169.3 dB.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.5
• /
• pp.414-423
• /
• 2020

In this paper, we propose a Dilated Convolution Gate Linear Unit (DCGLU) to mitigate the lack of sparsity and small receptive field problems caused by the segmentation map extraction process in sound event detection with weak labels. In the advent of deep learning framework, segmentation map extraction approaches have shown improved performance in noisy environments. However, these methods are forced to maintain the size of the feature map to extract the segmentation map as the model would be constructed without a pooling operation. As a result, the performance of these methods is deteriorated with a lack of sparsity and a small receptive field. To mitigate these problems, we utilize GLU to control the flow of information and Dilated Convolutional Neural Networks (DCNNs) to increase the receptive field without additional learning parameters. For the performance evaluation, we employ a URBAN-SED and self-organized bird sound dataset. The relevant experiments show that our proposed DCGLU model outperforms over other baselines. In particular, our method is shown to exhibit robustness against nature sound noises with three Signal to Noise Ratio (SNR) levels (20 dB, 10 dB and 0 dB).

• The Journal of the Acoustical Society of Korea
• /
• v.30 no.4
• /
• pp.181-189
• /
• 2011

According to developments of digital signal processing, 3D sound come into focus on multimedia systems. Many studies on 3d sound have proposed lots of clues to create realistic sounds. But these clues are only focused on binaural systems which two ears are normal. If we make the 3d sound using those clues at monaural systems, the performance goes down dramatically. In order to use the clues for monaural systems, we have studies algorithms such as duplex theory. In duplex theory, the sounds that we listen are affected by human's body, pinna and shoulder. So, we can enhance sound localization performances using its characteristics. In this paper, we propose a new method to use psychoacoustic theory that creates realistic 3D audio at monaural systems. To improve 3d sound, we calculate the excitation energy rates of each symmetric HRTF and extract the weights in each bark range. Finally, they are applied to emphasize the characteristics related to each direction. Informal listening tests show that the proposed method improves sound localization performances much better than the conventional methods.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.43 no.6 s.312
• /
• pp.101-109
• /
• 2006

Recently, according to the number of internet in widely use and the development of the related application program, the distribution and use of multimedia content(text, images, video, audio etc.) is very easy. Digital signal may be easily duplicated and the duplicated data can have same quality of original data so that it is difficult to warrant original owner. For the solution of this problem, the protection method of copyright which is encipher and watermarking. Digital watermarking is used to protect IP(Intellectual Property) and authenticate the owner of multimedia content. In this paper, the proposed watermarking algerian embeds watermark into multiple lower bitplanes of digital image. In the proposed algorithm, original and watermark images are decomposed to bitplanes each other and the watermarking operation is executed in the corresponded bitplane. The position of watermark image embedded in each bitplane is used to the watermarking key and executed in multiple lower bitplane which has no an influence on human visual recognition. Thus this algorithm can present watermark image to the multiple inherent patterns and needs small watermarking quantity. In the experiment, the author confirmed that it has high robustness against attacks of JPEG, MEDIAN and PSNR but it is weakness against attacks of NOISE, RNDDIST, ROT, SCALE, SS on spatial domain when a criterion PSNR of watermarked image is 40dB.

• KIISE Transactions on Computing Practices
• /
• v.21 no.1
• /
• pp.64-69
• /
• 2015

Place awareness is an essential for location-based services that are widely provided to smartphone users. However, traditional GPS-based methods are only valid outdoors where the GPS signal is strong and also require symbolic place information of the physical location. In this paper, environmental sounds and images are used to recognize important aspects of each place. The proposed method extracts feature vectors from visual, auditory and location data recorded by a smartphone with built-in camera, microphone and GPS sensors modules. The heterogeneous feature vectors were then learned by an ensemble learning method that learns each group of feature vectors for each classifier respectively and votes to produce the highest weighted result. The proposed method is evaluated for place recognition using a data group of 3000 samples in six places and the experimental results show a remarkably improved recognition accuracy when using all kinds of sensory data comparing to results using data from a single sensor or audio-visual integrated data only.

• Journal of Broadcast Engineering
• /
• v.23 no.6
• /
• pp.855-865
• /
• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.4
• /
• pp.329-344
• /
• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.