• Journal of Korean Society of Water and Wastewater
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• v.24 no.1
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• pp.5-14
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• 2010

A pilot-scale biological aerated filter (BAF) was operated with an anaerobic, anoxic and oxic zone at $23{\pm}1^{\circ}C$. The influent sCOD and total nitrogen concentrations in the feedwater were approximately 250 mg/L and 35 mg N/L, respectively. sCOD removal at optimum hydraulic retention time (HRT) of 3 hours with recirculation rates of 100, 200 and 300% in the column was more than 96%. Total nitrogen removal was consistently above 80% for 4 and 6 hours HRT at 300% recirculation. For 3 hours HRT and 300% recirculation, total nitrogen removal was approximately 79%. Based on fitting results, the kinetic parameter values on nitrification and denitrification show that as recirculation rates increased, the rate of ammonia and nitrate transformation increased. The ammonium loading rates for maximum ammonium removed were 0.15 and 0.19 kg $NH_3$-N/$m^3$-day for 100% and 200% recirculation, respectively. The experimental results demonstrated that the BAF can be operated at an HRT of 3 hours with 200 - 300% recirculation rates with more than 96 % removal of sCOD and ammonium, and at least 75% removal of total nitrogen.

• Journal of Korean Society of Water and Wastewater
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• v.13 no.3
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• pp.61-72
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• 1999

To obtain the experimental data for design and operation of actual filtration processes, a sand filter and three kinds of dual media filters in pilot-plant scale were operated in this study. We analyzed the effect of filter medium composition on the filter performance and the effects of backwash water flow rates, length of stream line and air flow rate on the filter backwash efficiency. We also compared the efficiencies of the combined air-water backwashing and the water backwashing in dual media filters. As the backwash water flow rates or the length of stream line increased, the final turbidity of backwash water was decreased and the filtration duration time after backwash was increased. In the case of the combined air-water backwashing, the backwash water quantity needed for backwashing the dual media filters could be decreased. The total volume of filtered water for the dual media filters during filter run was over three times larger than that for the sand filter. The dual media filters could be operated at a high filtration rate of 360 m/day.

• The Journal of Health Technology Assessment
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• v.6 no.2
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• pp.133-141
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• 2018

Objectives: In the past, the pharmaceutical drug heparin was mostly used as the anticoagulant for continuous renal replacement therapy (CRRT), but the duration time is long to have the risk of a bleeding adverse effect, and in that case the drug therapy Nafamostat mesilate was utilized instead, as it is more safe in this case, with a short half-life and is increasing in use to permit lower concerns for bleeding incidents. However, there are insufficient number of large-scale studies on the comparison of Nafamostat mesilate and heparin. Methods: In this study, a systematic review are used to compare the bleeding risk of Nafamostat mesilate and Heparin, as subjected to patients and procedures for measuring risks performed with a CRRT, and the filter life span is to be evaluated as well in this patients. Results: As a result of literature review search, a total of 6 studies were included in systematic review. The reducing risk of bleeding and filter life span was analyzed. The retrospective cohort studies confirm that Nafamostat mesilate is less at risk of bleeding than heparin. And a cohort study confirms that Nafamostat mesilate is longer filter lifespan than heparin and randomized controlled trial studies show that Nafamostat mesilate is longer filter lifespan than not using the anticoagulants. Conclusion: Nafamostat mesilate is considered to be a good therapeutic option because it has a longer filter life span as well as the advantage of reducing bleeding.

• Journal of the Korean Society of Visualization
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• v.6 no.1
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• pp.41-46
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• 2008

In this paper, the recirculation flow motion and mixing characteristics driven by air bubble stream in a rectangular water tank is studied. The time-resolved PIV technique is adopted for the quantitative visualization and analysis. 488 nm Ar-ion CW laser is used for illumination and orange fluorescent ($\lambda_{ex}=540nm,\;\lambda_{em}=560nm$) particle images are acquired by a PCO 10bit high-speed CCD camera (1280$\times$1024). To obtain clean particle images, 545 nm long pass optical filter and an image intensifier are employed and the flow rates of compressed air is 3 l/min at 0.5 MPa. The recirculation and mixing flow field is further investigated by time-resolved POD analysis technique. It is observed that the large scale recirculation resulting from the interaction between rising bubble stream and side wall is the most dominant flow structure and there are small scale vortex structures moving along with large scale recirculation flow. It is also verified that the sum of 20 modes of velocity field has about 67.4% of total turbulent energy.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.95-100
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• 2018

In this paper, we propose a binarized multi-scale module to accelerate the speed of the pose estimating deep neural network. Recently, deep learning is also used for fine-tuned tasks such as pose estimation. One of the best performing pose estimation methods is based on the usage of two neural networks where one computes the heat maps of the body parts and the other computes the part affinity fields between the body parts. However, the convolution filtering with a large kernel filter takes much time in this model. To accelerate the speed in this model, we propose to change the large kernel filters with binarized multi-scale modules. The large receptive field is captured by the multi-scale structure which also prevents the dropdown of the accuracy in the binarized module. The computation cost and number of parameters becomes small which results in increased speed performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.229-236
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• 2005

This paper is contribute to Inverse continuous wavelets transform(ICWT) which permits to determine real 'time-scale' plan. The application of ICWT is not yet represented because of the numerical difficulty. If the signal can be reconstructed stably by ICWT, the multi scale filter bank system which composed by analysis and synthesis process can be designed. In this work, we represent the ICWT which leads to nearly perfect reconstruction of signal and the multi-scale filter bank system.

• Environmental Engineering Research
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• v.19 no.3
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• pp.293-298
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• 2014

An anaerobic photosynthetic treatment process utilizing purple non-sulfur photosynthetic bacteria (PNSB) was applied to the recovery of organic carbon from food processing wastewater. PNSB cells, by-product from the treatment, have high nutrition such as proteins and vitamins which are a good alternative for fish feed. Effects of light source on performance of anaerobic photosynthetic process were investigated in this study. Two bench-scale photo-bioreactors were lighted with infrared light emitting diodes (LEDs) and tungsten lamps covered with infrared transmitting filter, respectively, aiming to supply infrared light for photosynthetic bacteria growth. The photo-bioreactors were operated to treat noodle-processing wastewater for 323 days. Hydraulic retention time (HRT) was set as 6 days. Organic removals in the photo-bioreactor lighted with infrared LEDs (91%-95%) was found higher than those in photo-bioreactor with tungsten lamps with filter (79%-83%). Biomass production in a 150 L bench-scale photo-bioreactor was comparable to a 8 L small-scale photo-bioreactor in previous study, due to improvement of light supply efficiency. Application of infrared LEDs could achieve higher treatment performance with advantages in energy efficiency and wavelength specifity.

• 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.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.131-138
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• 2018

This work describes a binary image based fast Difference of Gaussian (DoG) filter using zero-dimensional (0-d) convolution and state machine look up tables (LUTs) for image and video stitching hardware platforms. The proposed approach for using binary images to obtain DoG filtering can significantly reduce the data size compared to conventional gray scale based DoG filters, yet binary images still preserve the key features of the image such as contours, edges, and corners. Furthermore, the binary image based DoG filtering can be realized with zero-dimensional convolution and state machine LUTs which eliminates the major portion of the adder and multiplier blocks that are generally used in conventional DoG filter hardware engines. This enables fast computation time along with the data size reduction which can lead to compact and low power image and video stitching hardware blocks. The proposed DoG filter using binary images has been implemented with a FPGA (Altera DE2-115), and the results have been verified.