• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6C
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• pp.565-571
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• 2007

In order to improve PAPR (Peak-to-Average Power Ratio) reduction performance of the conventional SLM (Selective Mapping) for OFDM (Orthogonal Frequency Division Multiplexing) signals, we propose an effective SLM-PRSC (PAPR Reduction Sub-Carrier) hybrid scheme. In the proposed scheme, after performing the SLM for the frequency domain OFDM symbol excluding pre-determined PRSC positions, the SLM-PRSC hybrid sequence with the lowest PAPR generated by adding the time domain PRSC sequence to the results of the SLM, is selected as the transmitted OFDM signal. Since the identical PRSC sequences generated a priori are repeatedly used for every OFDM symbol, excessive IFFT (inverse Fast Fourier Transform) calculation is avoided. Simulation results show that the proposed scheme significantly improves the PAPR reduction performance of the conventional SLM, while avoiding excessive increase of IFFT calculation and the overhead for the SLM.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.3 s.94
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• pp.327-334
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• 2005

In this paper, a new PAPR reduction method using correlation of input data sequence is proposed in order to overcome the serious drawbacks, such as necessity of side information transmission or increase of system complexity and calculation which are the basis of previous SLM, PTS and DSI method. A new PAPR reduction method can be classified into Method 1 and Method 2. Method 1 reduces the correlation between the smallest sub-sequence and neighbor smallest sub-sequence. And, to consider the own correlation in detail, the Method 2 reduces two case of correlation between neighbor smallest sub-sequences and neighbor largest sub-sequences. According to simulation result, Method 1 and Method 2 has about 0.5 $\~$ O.8 dB and 1 $\~$ l.2 dB PAPR reduction effect respectively. If more detailed process to reduce the correlation of input data sequence is considered, we can achieve better PAPR reduction performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12C
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• pp.1175-1187
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• 2007

This paper proposes a novel crest factor reduction (CFR) algorithm to be deployed on WCDMA basestation. Generally speaking, it is well described that the reduction of peak-to-average ratio (PAR) yields the possibility of using low cost power amplifier such that the basesation becomes economic However, the simple reduction of PAR could degrade the signal quality measured by either peak code domain error (PCDE) or error vector measurement (EVM), and the level of channel interference constrained by adjacent channel leakage ratio (ACLR). Regarding these imperfections, this paper introduces an effective CFR algorithm in which the function of filter-dependent CFR (FDCFR) incorporated with the histogram-based waterfilling code domain compensation (HBWCDC) carries out. To verify the performance of the proposed CFR technique, substantial simulations including comparative works are conducted with obeying W-CDMA basestation verification specification. To exploit the superiority, the performance of the proposed method is tentatively compared with that associated to the simple memoryless clipping method and the memory-required filter-dependent clipping method.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.21-27
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• 2012

This study deals with the relationships between signal operation and traffic accident of signalized intersections. The objective of this study is to analyze the traffic accident reduction effects on the operation of all-red clearance interval (ARCI). This study gives particular attentions to evaluating the traffic accident reduction effects of ARCI at 37 signalized intersections in Cheongju using before-after evaluation with comparison group. The main results are as follows. First, the number of traffic accidents, fatalities and injuries has changed before operating ARCI and thereafter. Second, the result of before-and-after evaluation with comparison group showed that the number of accidents was reduced by 20.0% and that the number of fatalities and injuries was reduced by 28.0%. Finally, reduction effects from operating ARCI were evaluated to be 6.0~35.0% in the number of accidents and 18.0~37.0% in the number of fatalities and injuries.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.374-381
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• 2021

Raman spectrometers are studied and developed for the military purposes because of their nondestructive inspection capability to capture unique spectral features induced by molecular structures of colorless and odorless chemical warfare agents(CWAs) in any phase. Raman spectrometers often suffer from random noise caused by their detector inherent noise, background signal, etc. Thus, reducing the random noise in a measured Raman spectrum can help detection algorithms to find spectral features of CWAs and effectively detect them. In this paper, we propose a denoising autoencoder for Raman spectra with a loss function for sample efficient learning using noisy dataset. We conduct experiments to compare its effect on the measured spectra and detection performance with several existing noise reduction algorithms. The experimental results show that the denoising autoencoder is the most effective noise reduction algorithm among existing noise reduction algorithms for Raman spectrum based standoff detection of CWAs.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.709-715
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• 2023

In this study, the performance evaluation of image quality for noise reduction was implemented using the U-net deep learning architecture in computed tomography (CT) images. In order to generate input data, the Gaussian noise was applied to ground truth (GT) data, and datasets were consisted of 8:1:1 ratio of train, validation, and test sets among 1300 CT images. The Adagrad, Adam, and AdamW were used as optimizer function, and 10, 50 and 100 times for number of epochs were applied. In addition, learning rates of 0.01, 0.001, and 0.0001 were applied using the U-net deep learning model to compare the output image quality. To analyze the quantitative values, the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. Based on the results, deep learning model was useful for noise reduction. We suggested that optimized hyper parameters for noise reduction in CT images were AdamW optimizer function, 100 times number of epochs and 0.0001 learning rates.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.1-10
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• 2019

This paper proposes a method to improve the detectable distance by reducing noise to perform a signal processing technique on the received signals. To increase the radar detection range, the noise component of the received signal has to be reduced. The proposed method reduces the noise component by employing two methods. First, the radar signals received with multiple pulses are accumulated. As the number of additions increases, the noise component gradually decreases due to noise randomness. On the other hand, the signal term gradually increases and thus signal to noise ratio increases. Secondly, after converting the accumulated signal into the frequency spectrum, a Least Mean Square (LMS) filter is applied. In the case of the radar received signal, desired signal exists in a specific part and most of the rest is a noise. Therefore, if the LMS filter is applied in the time domain, the noise increases. To prevent this, the LMS filter is applied after converting the received signal into the entire frequency spectrum. The LMS filter output is then transformed into the time domain and then range estimation algorithm is performed. Simulation results show that the proposed scheme reduces the noise component by about 25 dB. The experiment was conducted by comparing the proposed results with the conventional results of the radars held by the Korea Aerospace Research Institute for the international space station.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.179-187
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• 2009

This study develops a real-time signal control algorithm based on sectional travel times and includes a field test and evaluation. The objective function of the signal control algorithm is the equalization of delay of traffic movements, and the main process is calculating dissolved time of the queue and delay using the sectional travel time and detection time of individual vehicles. Then this algorithm calculates the delay variation and a targeted red time and calculates the length of the cycle and phase. A progression factor from the US HCM was applied as a method to consider the effect of coordinating the delay calculation, and this algorithm uses the average delay and detection time of probe vehicles, which were collected during the accumulated cycle for a stabile signal control. As a result of the field test and evaluation through the application of the traffic signal control algorithm on four consecutive intersections at 400m intervals, reduction of delay and an equalization effect of delay against TOD control were confirmed using the standard deviation of delay by traffic movements. This study was conducted to develop a real-time traffic signal control algorithm based on sectional travel time, using general-purpose traffic information detectors. With the current practice of disseminating ubiquitous technology, the aim of this study was a fundamental change of the traffic signal control method.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.541-549
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• 2023

The aim of this study wasto conduct a quantitative analysis of CT image quality according to an algorithm designed to reduce metal artifacts induced by metal components. Ten baseline images were obtained with the standard filtered back-projection algorithm using spectral detector-based CT and CT ACR 464 phantom, and ten images were also obtained on the identical phantom with the standard filtered back-projection algorithm after inducing metal artifacts. After applying the to raw data from images with metal artifacts, ten additional images for each were obtained by applying the virtual monoenergetic algorithm. Regions of interest were set for polyethylene, bone, acrylic, air, and water located in the CT ACR 464 phantom module 1 to conduct compare the Hounsfield units for each algorithm. The algorithms were individually analyzed using root mean square error, mean absolute error, signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index to assess the overall image quality. When the Hounsfield units of each algorithm were compared, a significant difference was found between the images with different algorithms (p < .05), and large changes were observed in images using the virtual monoenergetic algorithm in all regions of interest except acrylic. Image quality analysis indices revealed that images with the metal artifact reduction algorithm had the highest resolution, but the structural similarity index was highest for images with the metal artifact reduction algorithm followed by an additional virtual monoenergetic algorithm. In terms of CT images, the metal artifact reduction algorithm was shown to be more effective than the monoenergetic algorithm at reducing metal artifacts, but to obtain quality CT images, it will be important to ascertain the advantages and differences in image qualities of the algorithms, and to apply them effectively.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.158-167
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• 2017

Internet-based social and interactive video applications have become major constituents of the envisaged applications for next-generation multimedia networks. However, inherently dynamic network conditions, together with varying user expectations, pose many challenges for resource allocation mechanisms for such applications. Yet, in addition to addressing these challenges, service providers must also consider how to mitigate their operational costs (e.g., energy costs, equipment costs) while satisfying the end-user quality of service (QoS) expectations. This paper proposes a heuristic solution to the problem, where the energy incurred by the applications, and the monetary costs associated with the service infrastructure, are minimized while simultaneously maximizing the average end-user QoS. We evaluate the performance of the proposed solution in terms of serving probability, i.e., the likelihood of being able to allocate resources to groups of users, the computation time of the resource allocation process, and the adaptability and sensitivity to dynamic network conditions. The proposed method demonstrates improvements in serving probability of up to 27%, in comparison with greedy resource allocation schemes, and a several-orders-of-magnitude reduction in computation time, compared to the linear programming approach, which significantly reduces the service-interrupted user percentage when operating under variable network conditions.