• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.3 no.3
• /
• pp.517-531
• /
• 1999

This paper presents the development of the 2-mode variable gain high power amplifier for a transmitter of INMARSAT-M operating at L-band(1626.5-1646.5 MHz). This SSPA(Solid State Power Amplifier) is amplified 42 dBm in high power mode and 36 dBm in low power mode for INMARSAT-M. The allowable error sets +1 dBm of an upper limit and -2 dBm of a lower limit, respectively. To simplify the fabrication process, the whole system is designed by two parts composed of a driving amplifier and a high power amplifier, The HP's MGA-64135 and Motorola's MRF-6401 are used for driving amplifier, and the ERICSSON's PTE-10114 and PTF-10021 are used the high power amplifier. The SSPA was fabricated by the circuits of RF, temperature compensation and 2-mode gain control circuit in aluminum housing. The gain control method was proposed by controlling the voltage for the 2-mode. In addition, It has been experimentally verified that the gain is controlled for single tone signal as well as two tone signals. The realized SSPA has 42 dB and 36 dB for small signal gain within 20 MHz bandwidth, and the VSWR of input and output port is less than 1.5:1 The minimum value of the 1 dB compression point gets 5 dBm for 2-mode variable gain high power amplifier. A typical two tone intermodulation point has 32.5 dBc maximum which is single carrier backed off 3 dB from 1 dB compression point. The maximum output power of 43 dBm was achieved at the 1636.5 MHz. These results reveal a high power of 20 Watt, which was the design target.the design target.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.1-10
• /
• 2023

This paper proposes a method to extend Inter-Carrier Interference (ICI) canceling Orthogonal Frequency Division Multiplexing (OFDM) receivers for 5G mobile systems to spatial multiplexing 2×2 MIMO (Multiple Input Multiple Output) systems to support high-speed ground transportation services by linear motor cars traveling at 500 km/h. In Japan, linear-motor high-speed ground transportation service is scheduled to begin in 2027. To expand the coverage area of base stations, 5G mobile systems in high-speed moving trains will have multiple base station antennas transmitting the same downlink (DL) signal, forming an expanded cell size along the train rails. 5G terminals in a fast-moving train can cause the forward and backward antenna signals to be Doppler-shifted in opposite directions, so the receiver in the train may have trouble estimating the exact channel transfer function (CTF) for demodulation. A receiver in such high-speed train sees the transmission channel which is composed of multiple Doppler-shifted propagation paths. Then, a loss of sub-carrier orthogonality due to Doppler-spread channels causes ICI. The ICI Canceller is realized by the following three steps. First, using the Demodulation Reference Symbol (DMRS) pilot signals, it analyzes three parameters such as attenuation, relative delay, and Doppler-shift of each multi-path component. Secondly, based on the sets of three parameters, Channel Transfer Function (CTF) of sender sub-carrier number n to receiver sub-carrier number l is generated. In case of n≠l, the CTF corresponds to ICI factor. Thirdly, since ICI factor is obtained, by applying ICI reverse operation by Multi-Tap Equalizer, ICI canceling can be realized. ICI canceling performance has been simulated assuming severe channel condition such as 500 km/h, 8 path reverse Doppler Shift for QPSK, 16QAM, 64QAM and 256QAM modulations. In particular, 2×2MIMO QPSK and 16QAM modulation schemes, BER (Bit Error Rate) improvement was observed when the number of taps in the multi-tap equalizer was set to 31 or more taps, at a moving speed of 500 km/h and in an 8-pass reverse doppler shift environment.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.26 no.2
• /
• pp.99-112
• /
• 2013

This paper presents a subspace system identification for estimating the stiffness matrix and flexural rigidities of a shear building. System matrices are estimated by LQ decomposition and singular value decomposition from an input-output Hankel matrix. The estimated system matrices are converted into a real coordinate through similarity transformation, and the stiffness matrix is estimated from the system matrices. The accuracy and the stability of an estimated stiffness matrix depend on the size of the associated Hankel matrix. The estimation error curve of the stiffness matrix is obtained with respect to the size of a Hankel matrix using a prior finite element model of a shear building. The sizes of the Hankel matrix, which are consistent with a target accuracy level, are chosen through this curve. Among these candidate sizes of the Hankel matrix, more proper one can be determined considering the computational cost of subspace identification. The stiffness matrix and flexural rigidities are estimated using the Hankel matrix with the candidate sizes. The validity of the proposed method is demonstrated through the numerical example of a five-story shear building model with and without damage.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.10
• /
• pp.1-7
• /
• 2020

In recent years, as LED display systems are actively spread, study on effective control methods for an LED driver for driving the systems has been in progress. The most representative among them is the uniform brightness control method for the LED driver channel. In this paper, we propose an LED driver IC for BLU with current compensation and system protection functions to minimize channel luminance deviation. It is designed for current accuracy within ±3% between channels and a channel current of 150 mA. In order to satisfy the design specifications, the channel amplifier offset was canceled out by a chopping operation using a channel-driving PWM signal. Also, a pre-charge function was implemented to minimize the fast operation speed and luminance deviation between channels. LED error (open, short), switch TR short detection, and operating temperature protection circuits were designed to protect the IC and BLU systems. The proposed IC was fabricated using a Magnachip 0.35-um CMOS process and verified using Cadence and Synopsys' Design Tool. The fabricated LED driver IC has current accuracy within ±1.5% between channels and 150-mA channel output characteristics. The error detection circuits were verified by a test board.

• Journal of the Korean Society of Radiology
• /
• v.3 no.1
• /
• pp.29-39
• /
• 2009

In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.

• Korean Journal of Remote Sensing
• /
• v.11 no.3
• /
• pp.33-47
• /
• 1995

The representation of point-observed environmental variables in Geographic Information Systems(GIS) has often been inadequate to meet the need of regional-scale ecological and environmental applications. To create a map of continuous surface that would represent more reliable spatial variations for these applications, I present three spatial estimation methods. Using a secondary variable of the proximity to coast line together with rainfall acidity data collected at the 63 acid rain monitoring stations in Korea, average rainfall acidity map was cteated using co-kriging. For comparison, two other commonly used interpolation methods (inverse distance weighting and kriging) were also applied to rainfall acidity data without reference to the secondary variable. These estimation methods were evaluated by both visual assessments of the output maps and the quantitative comparison of error measures that were obtained from cross validation. The co-kriging method produced a rainfall acidity map that showed noticeable improvement in repoducing the inherent spatial pattern as well as provided lower statistical error as compared to the methods using only the primary variable.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.487-496
• /
• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of Korea Water Resources Association
• /
• v.51 no.11
• /
• pp.1031-1041
• /
• 2018

ANFIS-based methodology for improving GloSea5 ensemble weather forecast is developed and evaluated in this study. The proposed method consists of two steps: pre & post processing. For ensemble prediction of GloSea5, weights are assigned to the ensemble members based on Optimal Weighting Method (OWM) in the pre-processing. Then, the bias of the results of pre-processed is corrected based on Model Output Statistics (MOS) method in the post-processing. The watershed of the Chungju multi-purpose dam in South Korea is selected as a study area. The results of evaluation indicated that the pre-processing step (CASE1), the post-processing step (CASE2), pre & post processing step (CASE3) results were significantly improved than the original GloSea5 bias correction (BC_GS5). Correction performance is better the order of CASE3, CASE1, CASE2. Also, the accuracy of pre-processing was improved during the season with high variability of precipitation. The post-processing step reduced the error that could not be smoothed by pre-processing step. It could be concluded that this methodology improved the ability of GloSea5 ensemble weather forecast by using ANFIS, especially, for the summer season with high variability of precipitation when applied both pre- and post-processing steps.

• Journal of the Korean GEO-environmental Society
• /
• v.12 no.11
• /
• pp.71-77
• /
• 2011

Recently, non-destructive stress measurement method using magnetic anisotropy sensor has been applied to the construction site such as steel bridges and steel pipes. In addition, steel rib used in the tunnel construction site was found to be possible to measure the stress by non-destructive method. In this study, steel loading experiments using magnetic anisotropy sensor developed in Japan and strain gauges were conducted to derive stress sensitivity curve for domestic steel SS400. Also, additional steel loading experiments and numerical analysis were performed for evaluation of applicability for non-destructive stress measurement method using magnetic anisotropy sensor. As a result of this study, stress sensitivity curves for domestic steel SS400 were derived using output voltage measured by magnetic anisotropy sensor and average of stress measured by strain gauges depending on the measurement location. And as a result of comparing additional steel loading experiments with the numerical analysis, error level of magnetic anisotropy sensor is around 20MPa. When considering the level of the yield stress(245MPa) of steel, in case of using magnetic anisotropy sensor in order to determine the stress status of steel, it has sufficient accuracy in engineering. Especially, magnetic anisotropy sensor can easily identify the current state of stress which considers residual stress at steel structure that stress measurement sensor is not installed, so we found that magnetic anisotropy sensor can be applied at maintenance of steel structure conveniently.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.415-423
• /
• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.