• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.233-236
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• 2001

Orthogonal freqency division multiplexing (OFDM) is an attractive technique for achieving high-bit-rate wireless data transmission. However, the potentially large peak-to-average power ratio (PAPR) has limited its application; An OFDM signal with the large PAPR can cause power degradation (In-band distortion) and spectral spreading (Out-of-band distortion) by being clipped passing through a power amplifier. Thus, we propose the combining algorithm of Hadamard transform and phase shift, which is ascribed to the relation between the correlation of the IFFT input sequence function and PAPR. Extensive computer simulations show that the combining algorithm is an effective technique to reduce PAPR.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.195-195
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• 2004

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.293-298
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• 2023

The military is facing a continuous decrease in personnel, and in order to cope with potential accidents and challenges in operations, efforts are being made to reduce the direct involvement of personnel by utilizing the latest technologies. Recently, the use of various sensors related to Manned-Unmanned Teaming and artificial intelligence technologies has gained attention, emphasizing the need for flexible utilization methods. In this paper, we propose four dataset construction methods that can be used for effective training of robots that can be deployed in military operations, utilizing not only RGB image data but also data acquired from IR image sensors. Since there is no publicly available dataset that combines RGB and IR image data, we directly acquired the dataset within buildings. The input values were constructed by combining RGB and IR image sensor data, taking into account the field of view, resolution, and channel values of both sensors. We compared the proposed method with conventional RGB image data classification training using the same learning model. By employing the proposed image data fusion method, we observed improved stability in training loss and approximately 3% higher accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.97-104
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• 2010

In this paper, we provide an embedded type non-contact bio-radar heart and respiration rate monitoring system. We implemented the rate finding algorithm into the embedded system. The high-speed and reliable real-time signal processor is then tested. To avoid null-point data loss problem, we applied quadrature demodulation. Among several other combining techniques, we suggest arctangent demodulation for quadrature channel combining and DSP is used for real-time signal processing. We also suggest DC-offset compensation technique to preserve the wanted DC components of the IQ signals for accurate demodulation while keeping the dynamic range of the ADC lower. Using Texas Instrument C6711 series DSP and external 12Bit ADC, we implemented proper elliptic digital filter and autocorrelation detection algorithm for robust commercial hand held device.

• Korean Journal of Poultry Science
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• v.7 no.2
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• pp.28-36
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• 1980

This study was carried out to estimate combining abilitie or economic traits in layer chickens. The data used in this study were the record of 10 single crosses produced by half diallel cross of 5 lines of Single Comb White Leghorns, such as A, B, C, K and S lines. Total 720 progenies of the crosses were reared at the Poultry Breeding Farm, College of Agriculture, Seoul National University from Feb. 1979 to August, 1980. Combining abilities were estimated by Grilling's mathematical model for the traits; age the first egg, total egg number, egg weight and body weight. The results attained from the studies were summarized as follows; In estimate of combining ability, an age at first egg of BS cross was largely due to significiantly higher general combining ability (G. C. A.) effect of B and S strains than Cand K strains in G. C. A. effect, and to specific combining ability (S. C. A) effect of B and S-trains. AB and BS crosses showed the highest egg Production. AB cross performance was result from high G.C.A. effect of A ana B strains. BS cross performance was result from high G. C. A. effect of B and high S. C. A. effect of BS cross. Specific combining ability effect in egg. weight was not statiscally significiant, but S strain showed high G. C. A. effect. A and B strains in body weight showed significantly low C. C. A. effect. From the above results, BS cross in an age at first egg, AB and BS crosses in egg Production, S strain in egg weight and AB cross in body weight were superior to other strains or crosses.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.154-158
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• 2003

In this paper, Error rate performance of BCH coded DS-CDMA 16 QAM signal is analyzed using selective combining Diversity reception techniques in the environments of Rician fading. First in the performance of DS-CDMA 16 QAM signal in Rician fading channel, Second using SC diversity recepting techniques, and third using both diversity and BCH coding error rate performance is evaluated. from the results of Numerical analyzed it is found that a synergistic performance improvement is shown due to both diversity reception and coding techniques overcoming mobile wireless data communication channel environments.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.516-521
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• 2010

In the high-speed packet service, next generation mobile communication system has emerged as a major feature. If the fire of these high-speed D kit services and non-continuous transmission of data due to the symmetrical nature of daeyiteo traffic for D-CDMA system has been actively studied. In this paper, we apply turbo codes for D-CDMA has been investigated. Considering the nature of the system configuration of the transmission frame type, such as physical channel structure and channel coding has been investigated. In addition to TD-CDMA system, one HARQ gibeopjung hase Combining the performance analysis techniques were applied.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.193-199
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• 2000

In many studies, considerable attention has been focussed upon choosing a model which represents underlying process of time series and forecasting the future. In the real world, however, there may be some cases that one model can not reflect all the characteristics of original time series. Under such circumstances, we may get better performance by combining the forecasts from several models. The most popular methods for combining forecasts involve taking a weighted average of multiple forecasts. But the weights are usually unstable. In cases the assumptions of normality and unbiasedness for forecast errors are satisfied, a Bayesian method can be used for updating the weights. In the real world, however, there are many circumstances the Bayesian method is not appropriate. This paper proposes a PNN(Probabilistic Neural Net) approach as a method for combining forecasts that can be applied when the assumption of normality or unbiasedness for forecast errors is not satisfied. In this paper, PNN method, which is similar to Bayesian approach, is suggested as an updating method of the unstable weights in the combination of the forecasts. The PNN method has been usually used in the field of pattern recognition. Unlike the Bayesian approach, it requires no assumption of a specific prior distribution because it gets probabilities by using the distribution estimated from given data. Empirical results reveal that the PNN method offers superior predictive capabilities.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.933-939
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• 2009

The bivariate data in clinical research fields often has two types of failure times, which are mark variable for the first failure time and the final failure time. This paper showed how to generate bootstrap data to get Bayesian estimation for the joint distribution of bivariate survival times. The observed data was generated by Frank's family and the fake date is simulated with the Gamma prior of survival time. The bootstrap data was obtained by combining the mimic data with the observed data and the simulated fake data from the observed data.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.509-520
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• 2005

Data fusion is defined as the process of combining data and information from different sources for the effectiveness of the usage of useful information contents. In this paper, we propose a data fusion algorithm using k-means clustering method for data enrichment to improve data quality in knowledge discovery in database(KDD) process. An empirical study was conducted to compare the proposed data fusion technique with the existing techniques and shows that the newly proposed clustering data fusion technique has low MSE in continuous fusion variables.