• Journal of Energy Engineering
• /
• v.15 no.3 s.47
• /
• pp.146-153
• /
• 2006

A performance test of the stainless steel based sintered metal filter was conducted on the low and intermediate level radioactive waste (LILW) vitrification process. The applicability of the metal filter was based on the test results as well. The baseline pressure drop of the metal filter was evaluated similar to the ceramic filter. During the test, when the flow rate of off-gas was $110Nm^{3}/h$, the total baseline pressure drop was shown as $92mmH_{2}O$. The total pressure drop was attributed to the filter media and the residual dust layer and the value of each was $25mmH_{2}O\;and\;67mmH_{2}O$ respectively. The SEM-EDS spectrum and micrograph of the metal filter specimen showed, no corrosion and no physical damage both at the skin membrane and at the support layer. And most of the baseline pressure drop was caused by the deposition of dust on the surface of the membrane. In conclusion, even though the filter exposure time was short at the test, the performance of the stainless steel based metal filter was acceptable for the treatment of LILW vitrification process.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.175-186
• /
• 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
• /
• 1999.03a
• /
• pp.175-186
• /
• 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.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.49 no.8
• /
• pp.63-72
• /
• 2012

In this paper, we proposed a parallel architecture of line & block edge filter for high-performance H.264/AVC deblocking filter for Quad Full High Definition(Quad FHD) video real time processing. To improve throughput, we designed 4-parallel block edge filter with 16 line edge filter. To reduce internal buffer size and processing cycle, we scheduled 4-parallel zig-zag scan order as deblocking filtering order. To avoid data conflicts we placed 1 delay cycle between block edge filtering. We implemented interleaving buffer, as internal buffer of block edge filter, to sharing buffer for reducing buffer size. The proposed architecture was simulated in 0.18um standard cell library. The maximum operation frequency is 108MHz. The gate count is 140.16Kgates. The proposed H.264/AVC deblocking filter can support Quad FHD at 113.17 frames per second by running at 90MHz.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.10 no.4
• /
• pp.282-291
• /
• 2010

This paper presents a full-CMOS transmitter and receiver for VDSL2 systems. The transmitter part consists of the low-pass filter, programmable gain amplifier (PGA) and 14-bit DAC. The receiver part consists of the low-pass filter, variable gain amplifier (VGA), and 13-bit ADC. The low pass filter and PGA are designed to support the variable data rate. The RC bank sharing architecture for the low pass filter has reduced the chip size significantly. And, the 80 Msps, high resolution DAC and ADC are integrated to guarantee the SNR. Also, the transmitter and receiver are designed to have a wide dynamic range and gain control range because the signal from the VDSL2 line is variable depending on the distance. The chip is implemented in 0.25 ${\mu}m$ CMOS technology and the die area is 5 mm $\times$ 5 mm. The spurious free dynamic range (SFDR) and SNR of the transmitter and receiver are 67.5 dB and 41 dB, respectively. The power consumption of the transmitter and receiver are 160 mW and 250 mW from the supply voltage of 2.5 V, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.11
• /
• pp.2849-2865
• /
• 2012

In order to block mobile junk messages automatically, many studies on spam filters have applied machine learning algorithms. Most previous research focused only on the accuracy rate of spam filters from the view point of the algorithm used, not on individual user's preferences. In terms of individual taste, the spam filters implemented on a mobile device have the advantage over spam filters on a network node, because it deals with only incoming messages on the users' phone and generates no additional traffic during the filtering process. However, a spam filter on a mobile phone has to consider the consumption of resources, because energy, memory and computing ability are limited. Moreover, as time passes an increasing number of feature words are likely to exhaust mobile resources. In this paper we propose a spam filter model distributed between a users' computer and smart phone. We expect the model to follow personal decision boundaries and use the uniform resources of smart phones. An authorized user's computer takes on the more complex and time consuming jobs, such as feature selection and training, while the smart phone performs only the minimum amount of work for filtering and utilizes the results of the information calculated on the desktop. Our experiments show that the accuracy of our method is more than 95% with Na$\ddot{i}$ve Bayes and Support Vector Machine, and our model that uses uniform memory does not affect other applications that run on the smart phone.

• Journal of the Korean Ceramic Society
• /
• v.54 no.2
• /
• pp.114-120
• /
• 2017

In underdeveloped countries, many people suffer from water shortage due to the absence of water supply service. Although water purifiers have provided support in such situations, it is not easy to maintain water filters without a continuous supply of consumable filters. To obtain a sustainable drinking water source, appropriate technology of water treatment is necessary. Herein, a low cost water purification system was developed using natural raw materials. A non-electric water treatment system was developed using filtration through an Onggi filter, which is a type of Korean traditional earthenware with a microporous surface. The porosity and flux of the prepared Onggi filter were 29.06% and 31.63 LMH, respectively. After purification of water with total dissolved solids of 10.4 mg/L and turbidity of 100 NTU, the total dissolved solids and turbidity of the water treated using the Onggi filter decreased by 12% and 99.8%, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.401-404
• /
• 2015

This paper proposes a high-performance in-loop filter in HEVC(High Efficiency Video Coding) encoder for Ultra HD video processing in real time. HEVC uses in-loop filter consisting of deblocking filter and SAO(Sample Adaptive Offset) to solve the problems of quantization error which causes image degradation. In the proposed in-loop filter encoder hardware architecture, the deblocking filter and SAO has a 2-level hybrid pipeline structure based on the $32{\times}32CTU$ to reduce the execution time. The deblocking filter is performed by 6-stage pipeline structure, and it supports minimization of memory access and simplification of reference memory structure using proposed efficient filtering order. Also The SAO is implemented by 2-statge pipeline for pixel classification and applying SAO parameters and it uses two three-layered parallel buffers to simplify pixel processing and reduce operation cycle. The proposed in-loop filter encoder architecture is designed by Verilog HDL, and implemented by 205K logic gates in TSMC 0.13um process. At 110MHz, the proposed in-loop filter encoder can support 4K Ultra HD video encoding at 30fps in realtime.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.105-106
• /
• 2007

We present a fast reconstruction technique for elliptic objects, which can be applied to real-time computer tomography (CT) for simple geometric objects. It will be also shown that only 3 projections are needed to reconstruct an ellipse. A piecewise quadratic model is also proposed for more efficient Kalman filter based support estimation, which is used for the fast reconstruction technique. The performance of the piecewise quadratic model is compared with that of the existing piecewise linear model. Simulation results for the fast reconstruction are also presented.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.17 no.11
• /
• pp.1185-1198
• /
• 1992

In this paper, the differential weighted algorithm proposed in order to improve th noise smoothing characteristics of conventional Median filter and FIR-Median Hybrid filter. Performance of some image restoration filter(median filter, FIR-Median Hybird filter, FIR-Median Hybrid filter to proposed differential weighted algorithm) are compared and evaluated on the noise smoothing characteristics and sharp edge conservation characteristics. Test and Real images used in this paper are Lenna and Urological images corrupted by impulse, gaussian, exponential and laplacian noise. Experimental results show that the FIR-Median Hybrid filter applied to the differential weighted algorithm are comparatively superior to others. But the filter orders have increased, the more time consumed to image processing. Hence if the adequate filtering by the type of image is selected. now after a great support will be take consideration into the various parts of application by computer science and of medical image processing.