• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.162-170
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• 2015

In the bandpass sampling (BPS), the sampling frequency is lower than the frequency of the signal to be sampled. In this method, the baseband spectrum can be directly obtained by the sampling operation. This makes the frequency down converter unnecessary as well as the receiver's circuit simpler. In the second-order BPS system, two sampling devices are used. When aliasing occurs due to the sampling operation, the aliased component can be cancelled by combining the two sampled signals. In this paper, it is presented a design method of the second-order BPS system when multiple interferences are simultaneously aliased to the signal component. The optimum phase of the interpolant filter is searched for maximizing the signal-to-interference ratio, and a practical formula for the suboptimal phase is derived in terms of the power spectrum profile of the BPS input. A computer simulation has been performed for the proposed second-order BPS system, and it has been shown that the signal-to-interference ratio can be increased by considering multiple aliasing.

• Design & Manufacturing
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• v.10 no.2
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• pp.18-23
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• 2016

Injection molded thermoplastic parts may need to be coated to facilitate paint adhesion, or to satisfy other surface property requirements, such as appearance, durability, and weather resistance. In this paper, a two-component polyurethane metering system was developed for the simultaneous injection and surface coating of a plastic substrate. The system was composed of storage tanks, feed pumps, axial piston pumps, mixing head. The tank was designed to be double-jacket structured and fabricated for polyol and isocyanate, respectively. A temperature chamber was used to maintain the material temperature to be $80^{\circ}C$ during flowing from storage tank to mixing head. Inside the chamber, feed pump, low pressure filter, high pressure pump, high pressure filter, pressure sensor, flow meter were installed. A mixing head of L-type was used for homogeneous mixing of polyol and isocyanate. Inside the mixing head, a cartridge heater and a temperature sensor were installed to control the temperature of the materials. The flow rate of axial-piston pump was controlled by using closed-loop feedback control algorithm. The input flow-rates were compared with the measured values. The output error was 6.7% for open-loop control, whereas the error was below 2.2% for closed-loop control. In addition, the pressure generated through mixing-head nozzle increased with increasing flow rate. It was found that the pressure drop between metering pump and mixing-head nozzle was almost 10 bar.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.10
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• pp.1107-1116
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• 2007

In this paper, a broadband open stubs band pass filters which can suppress the second harmonics using Z-transform technique, is designed, fabricated and characterized. The proposed broadband filters integrate the band stop filter with the FSCS structure and ${\lambda}_g/4$ open stub in order to suppress the second harmonics. Due to insertion of BSF at input and output terminal, the size of the filter was increased in the conventional filter, however, in the proposed structure, the position of inverter that connects the stubs can be integrated between those stubs, thereby decreasing the size. So, it can be fabricated in the size of $18.7{\times}16.9mm^2$ which is smaller size than conventional one. The measured results of the proposed filters have center frequency of a 5.8 GHz with bandwidth of 95 %, insertion loss of 0.6 dB, return loss of 14 dB. The simulation results are consistent with measurement results. The filter is designed far X-band satellite communication and ITS applications.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• The Journal of the Acoustical Society of Korea
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• v.29 no.5
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• pp.303-313
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• 2010

This paper describes an acoustic communication receiver structure, which is designed for QPSK (Quadrature Phase Shift Keying) signal with 25 kHz carrier frequency and 5 kHz symbol rate, and takes samples from received signal at 100 kHz sampling rate. Based on the described receiver structure, optimum design parameters, such as number of taps of FF (Feed-Forward) and FB (Feed-Back) filters and forgetting factor of RLS (Recursive Least-Square) algorithm, of joint equalizer are determined to minimize the BER (Bit Error Rate) performance of the joint equalizer output symbols when the acquisition data in shallow water using implemented acoustic transducers is decimated at a rate of 2:1 and then enforced to the input of receiver. The transmission distances are 1.4 km, 2.9 km, and 4.7 km. Analysis results show that the optimum number of taps of FF and FB filters are different according to the distance between source and destination, but the optimum or near optimum value of forgetting factor is 0.997. Therefore, we can reach a conclusion that the proper receiver structure could change the number of taps of FF and FB filters with the fixed forgetting factor 0.997 according to the transmission distance. Another analysis result is that there are an acceptable performance degradation when the 16-tap-length simple filter is used as a low-pass filter of receiver instead of 161-tap-length matched filter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.10
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• pp.1169-1176
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• 2010

The RF front-end for L1/L2 dual-band Global Positioning System(GPS) receiver is presented in this paper. The RF front-end(down-converter) using low IF architecture consists of a wideband low noise amplifier(LNA), a current mode logic(CML) frequency divider and a I/Q down-conversion mixer with a poly-phase filter for image rejection. The current bleeding technique is used in the LNA and mixer to obtain the high gain and solve the head-room problem. The common drain feedback is adopted for low noise amplifier to achieve the wideband input matching without inductors. The fabricated RF front-end using $0.18{\mu}m$ CMOS process shows a gain of 38 dB for L1 and 41 dB for L2 band. The measured IIP3 is -29 dBm in L1 band and -33 dBm in L2 band, The input return loss is less than -10 dB from 50 MHz to 3 GHz. The measured noise figure(NF) is 3.81 dB for L1 band and 3.71 dB for L2 band. The image rejection ratio is 36.5 dB. The chip size of RF front end is $1.2{\times}1.35mm^2$.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.152-159
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• 2016

This paper describes an offset and 1/f noise cancellation technique based hall sensor signal processor. The hall sensor outputs a hall voltage from the input magnetic field, which direction is orthogonal to hall plate. The two major elements to complete the hall sensor operation are: the one is a hall sensor to generate hall voltage from input magentic field, and the other one is a hall signal process system to cancel the offset and 1/f noise of hall signal. The proposed hall sensor splits the hall signal and unwanted signals(i.e. offset and 1/f noise) using a spinning current biasing technique and chopper stabilizer. The hall signal converted to 100 kHz and unwanted signals stay around DC frequency pass through chopper stabilizer. The unwanted signals are bloked by highpass filter which, 60 kHz cut off freqyency. Therefore only pure hall signal is enter the ADC(analog to dogital converter) for digitalize. The hall signal and unwanted signal at the output of an amplifer and highpass filter, which increase the power level of hall signal and cancel the unwanted signals are -53.9 dBm @ 100 kHz and -101.3 dBm @ 10 kHz. The ADC output of hall sensor signal process system has -5.0 dBm hall signal at 100 kHz frequency and -55.0 dBm unwanted signals at 10 kHz frequency.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.84-89
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• 2002

In this paper, we present the design method of noise reduction circuit in telephonic system. The circuit consists of compressor, expander and a filter. The basic idea of a proposed method compresses the audible signal in order to mask the channel noise during transmission and then expand at the reverse rate the transmitted signal to naturally recover the original signal. Of course, there should be no distortion or other degradation of the audio itself in passing through companding(compress/expand) cycle. In the compressing process, the gain of compressor is automatically controlled by the envelope level of input signal in order to increase the effective dynamic range of input signal and to improve the signal to noise ratio. The compressed rate is the root time of a audible signal. The compressed signal should be expanded at the square time of the signal to recover a original signal. Simulation shows the proposed method improves the performance of the noise reduction of a channel noise as well as stability.