• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.57-63
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• 1997

In this paper, we study the efficient feature vector extraction method and front-end processing to improve the performance of the speech recognition system using KT(Korea Telecommunication) database collected through various telephone channels. First of all, we compare the recognition performances of the feature vectors known to be robust to noise and environmental variation and verify the performance enhancement of the recognition system using weighted cepstral distance measure methods. The experiment result shows that the recognition rate is increasedby using both PLP(Perceptual Linear Prediction) and MFCC(Mel Frequency Cepstral Coefficient) in comparison with LPC cepstrum used in KT recognition system. In cepstral distance measure, the weighted cepstral distance measure functions such as RPS(Root Power Sums) and BPL(Band-Pass Lifter) help the recognition enhancement. The application of the spectral subtraction method decrease the recognition rate because of the effect of distortion. However, RASTA(RelAtive SpecTrAl) processing, CMS(Cepstral Mean Subtraction) and SBR(Signal Bias Removal) enhance the recognition performance. Especially, the CMS method is simple but shows high recognition enhancement. Finally, the performances of the modified methods for the real-time implementation of CMS are compared and the improved method is suggested to prevent the performance degradation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.6
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• pp.1-9
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• 2008

This paper suggests the triple-mode CMOS DC-DC converter that has temperature/voltage variation compensation techniques. The proposed triple-mode CMOS DC-DC converter is used to generate constant or variable voltages of 0.6-2.2V within battery source range of 3.3-5.5V. Also, it supports triple modes, which include Pulse Width Modulator (PWM) mode, Pulse Frequency Modulator (PFM) mode and Low Drop-Out (LDO) mode. Moreover, it uses 1MHz low-power CMOS ring oscillator that will compensate malfunction of chip in temperature/voltage variation condition. The proposed triple-mode CMOS DC-DC converter, which generates output voltages of 0.6-2.2V with an input voltage sources of 3.3-5.5V, exhibits the maximum output ripple voltage of below 10mV at PWM mode, 15mV at PFM mode and 4mV at LDO mode. And the proposed converter has maximum efficiency of 93% at PWM mode. Even at $-25{\sim}80^{\circ}C$ temperature variations, it has kept the output voltage level within 0.8% at PWM/PFM/LDO modes. For the verification of proposed triple-mode CMOS DC-DC converter, the simulations are carried out with $0.35{\mu}m$ CMOS technology and chip test is carried out.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.99-108
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• 2015

The embedded microcontroller which is operated by the logic gates synchronized on the clock pulse, is gradually used as main controller of mission-critical systems. Severe electrical situations such as high voltage/frequency surge may cause malfunctioning of the clock source. The tolerant system operation is required against the various external electric noise and means the robust design technique is becoming more important issue in system clock failure problems. In this paper, we propose on-chip backup clock change architecture for the automatic clock failure detection. For the this, we adopt the edge detector, noise canceller logic and glitch-free clock changer circuit. The implemented edge detector unit detects the abnormal low-frequency of the clock source and the delay chain circuit of the clock pulse by the noise canceller can cancel out the glitch clock. The externally invalid clock source by detecting the emergency status will be switched to back-up clock source by glitch-free clock changer circuit. The proposed circuits are evaluated by Verilog simulation and the fabricated IC is validated by using test equipment electrical field radiation noise

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.5
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• pp.574-580
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• 2016

In this paper, we will introduce the system that can control multiple vehicles on the road through Single Board Computers and V2I (Vehicle-To-Infrastructure). Also, we will propose the group evasive maneuver decision algorithm, which plays a critical role in deciding whether the vehicles in the system have to conduct evasive maneuvers to avoid obstacles on the road. In order to test this system, we have utilized Wi-Fi and TCP/IP for establishing the communication between multiple vehicles and the relay server, and observed their driving states on the road with obstacles. During the experiments, we have discovered that our original decision algorithm possesses high failure rate when there is frequency interference in ISM (Industrial Scientific Medical) band. In order to reduce this failure rate, we have implemented the data transition detector. This paper will focus on how the use of data transition detector can affect the reliability of the system under the frequency interference of ISM band. If this technology is improved and applied in the field, we will effectively deal with such dangerous situations as multiple collision accidents through vehicle-to-vehicle communication or vehicle-to-infrastructure communication. Furthermore, this can be applied to the autonomous driving technologies. This can be used as the reference data for the development of the similar system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.1017-1026
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• 2005

In burst OFDM system, the frame synchronization should be performed first for the acquisition of received frame and the estimation of the correct FFT-window position. The conventional frame synchronization algorithms using design features of the preamble symbol, the repetition pattern of the OFDM symbol by pilot sub-carrier allocation rule and Cyclic Prefix(CP), has difficulty in the detection of precise frame timing because its correlation characteristics would increase and decrease gradually. Also, the algorithm based on the correlation between the reference signal and the received signal has performance degradation due to frequency offset. Therefore, we adopt a differential correlation method that is robust to frequency offset and has the clear peak value at the correct frame timing for frame synchronization. However, performance improvement is essential for differential correlation methods, since it usually shows multiple peak values due to the repetition pattern. In this paper, we propose an enhanced frame synchronization algorithm based on the differential correlation method that shows a clear single peak value by using differential correlation between samples of identical repeating pattern. We also introduce a normalization scheme which normalizes the result of differential correlation with signal power to reduce the frame timing error in the high speed mobile channel environments.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.77-85
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• 2001

This paper proposes a method of time-scale modification of polyphonic audio signals based on a sinusoidal model. The signals are modeled with sinusoidal component and noise component. A multiresolution filter bank is designed which splits the input signal into six octave-spaced subbands without aliasing and sinusoidal modeling is applied to each subband signal. To alleviate smearing of transients in time-scale modification a dynamic segmentation method is applied to subbands which determines the analysis-synthesis frame size adaptively to fit time-frequency characteristics of the subband signal. For extracting sinusoidal components and calculating their parameters matching pursuit algorithm is applied to each analysis frame of subband signal. In accordance with spectrum analysis a psychoacoustic model implementing the effect of frequency masking is incorporated with matching pursuit to provide a resonable stop condition of iteration and reduce the number of sinusoids. The noise component obtained by subtracting the synthesized signal with sinusoidal components from the original signal is modeled by line-segment model of short time spectrum envelope. For various polyphonic audio signals the result of simulation shows suggested sinusoidal modeling can synthesize original signal without loss of perceptual quality and do more robust and high quality time-scale modification for large scale factor because of representing transients without any perceptual loss.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.5
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• pp.258-270
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• 2014

In this paper, we investigate channel capacity of two kinds of uplink OFDMA (Orthogonal Frequency Division Multiple Access) schemes, i.e. ZCZ (Zero Correlation Zone) code time-spread OFDMA and sparse SC-FDMA (Single Carrier Frequency Division Mmultiple Access) robust to access timing offset (TO) among multiple users. In order to reflect the practical condition, we consider not only access TO among multiple users but also peak to average power ratio (PAPR) which is one of hot issues of uplink OFDMA. In the case with access TO among multiple users, the amplified signal of users by power control might affect a severe interference to signals of other users. Meanwhile, amplified signal by considering distance between user and base station might be distorted due to the limit of amplifier and thus the performance might degrade. In order to achieve the maximum channel capacity, we investigate the combinations of transmit power so called ASF (adaptive scaling factor) by numerical simulations. We check that the channel capacity of the case with ASF increases compared to the case with considering only distance i.e. ASF=1. From the simulation results, In the case of high signal to noise ratio (SNR), ZCZ code time-spread OFDMA achieves higher channel capacity compared to sparse block SC-FDMA. On the other hand, in the case of low SNR, the sparse block SC-FDMA achieves better performance compared to ZCZ time-spread OFDMA.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.