• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.8-15
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• 1997

In this paper, a real-time operating Motion Picture Experts Group-1 (MPEG-1) audio encoder system is implemented using a TMS320C31 Digital Signal Processor (DSP) chip. The basic operation of the MPEG-1 audio encoder algorithm based on audio layer-2 and psychoacoustic model-1 is first verified by C-language. It is then realized using the Texas Instruments (Tl) assembly in order to reduce the overall execution time. Finally, the actual BSP circuit board for the encoder system is designed and implemented. In the system, the side-modules such as the analog-to-digital converter (ADC) control, the input/output (I/O) control, the bit-stream transmission from the DSP board to the PC and so on, are utilized with a field programmable gate array (FPGA) using very high speed hardware description language (VHDL) codes. The complete encoder system is able to process the stereo audio signal in real-time at the sampling frequency 48 kHz, and produces the encoded bit-stream with the bit-rate 192 kbps. The real-time operation capability of the encoder system and the good quality of the decoded sound are also confirmed using various types of actual stereo audio signals.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.9-17
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• 2023

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.263-267
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• 2016

채팅 시스템은 사람이 사용하는 언어로 컴퓨터와 의사소통을 하는 시스템이다. 최근 딥 러닝이 큰 화두가 되면서 다양한 채팅 시스템에 관한 연구가 빠르게 진행 되고 있다. 본 논문에서는 문장을 Recurrent Neural Network기반 의사형태소 분석기로 분리하고 Attention mechanism Encoder-Decoder Model의 입력으로 사용하는 채팅 시스템을 제안한다. 채팅 데이터를 통한 실험에서 사용자 문장이 짧은 경우는 답변이 잘 나오는 것을 확인하였으나 긴 문장에 대해서는 문법에 맞지 않는 문장이 생성되는 것을 알 수 있었다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.84-90
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• 1997

We proposed a new bit-rate control algorithm to improve MPEG-2 video software encoder. Bit-rate Control plays an improtant role in picture quality of MPEG-2 encoder. To achieve better encoding performance such as controlling picture quality and using bity properly, we proposed a MPEG-2 bit-rate control algorithm using linear average Step-Size. Using a benchmark Program, we compared our algorithm with MPEG-2 Test Model 5. Our proposed algorithm showed better Bit-Rate Control with respect to used bits, picture quality.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.78-80
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• 2021

In this paper, we proposed an improved method on the picture order of coding (POC) of MPEG-5 Essential video Coding (EVC) encoder to support a short intra period for Video-based Point Cloud Compression (V-PCC). As a codec-agnostically designed standard, V-PCC claimed to be able to work with a lot of codecs. Current EVC test model software shows that the baseline profile could not provide appropriate POC calculation. The proposed method offers a solution to this POC-related problem and provides up to 44.6% coding grains for EVC based V-PCC.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.93-96
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• 2000

Currently, the most important technology is the compression methods in the multimedia society. Audio files are rapidly propagated through internet. MP-3 is offered to CD tone quality in 128Kbps, but 64Kbps below tone quality is abruptly down and high bitrate. on the other hand, MPEG-II AAC (Advanced Audio Coding) is not compatible with MPEG-I, but AAC has a high compression ratio 1.4 better than MP-3. Especially, AAC has max. 7.1 channel and 96KHz sampling rate. In this paper, the perceptual model is dealt with 44.1KHz sampling rate for SMR(Signal to Masking Ratio)

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.216-226
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• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

• ETRI Journal
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• v.43 no.6
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• pp.1081-1092
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• 2021

Electroencephalography (EEG) recordings taken during the perception of music tempo contain information that estimates the tempo of a music piece. If information about this tempo stimulus in EEG recordings can be extracted and classified, it can be effectively used to construct a music-based brain-computer interface. This study proposes a novel convolutional recurrent attention model (CRAM) to extract and classify features corresponding to tempo stimuli from EEG recordings of listeners who listened with concentration to the tempo of musics. The proposed CRAM is composed of six modules, namely, network inputs, two-dimensional convolutional bidirectional gated recurrent unit-based sample encoder, sample-level intuitive attention, segment encoder, segment-level intuitive attention, and softmax layer, to effectively model spatiotemporal features and improve the classification accuracy of tempo stimuli. To evaluate the proposed method's performance, we conducted experiments on two benchmark datasets. The proposed method achieves promising results, outperforming recent methods.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.496-496
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• 2000

We propose a neuron computer for tracking motion of particles in multi-dimensional space. The neuron computer is constructed of neural networks and their connections, which is a simplified model of the brain. The neuron computer is assemblage of neural networks, it includes a control unit, and the actions of the unit are represented by instructions. We designed a neuron computer to recognize and predict motion of particles. The recognition unit is constructed of neuron-array, encoder, and control part. The neuron-array is a model of the retina, and particles crease an image on the array, where the image is binary. The encoder picks one particle from the array, and translates the particle's location to Cartesian coordinates, which is scaled in [0, 1] intervals. Next, the encoder picks another particle, and does same process. The ordering and reduction of complex processes are executed by instructions. The instructions are held in the control part. The prediction unit is constructed of a multi-layer neural network and a feedback loop, where real time learning is executed. The particles' future locations are forecasted by coordinate values. The neuron computer can chase maximum 100 particles that take evasions.