• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.651-658
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• 2018

Particulate matter (PM) emitted from fossil fuel-combustion facilities can be classified as either filterable or condensable PM. The U.S. Environmental Protection Agency (EPA) defined condensable PM as material that is in the phase of vapor at the stack temperature of the sampling location which condenses, reacts upon cooling and dilution in the ambient air to form solid or liquid in a few second after the discharge from the stack. Condensable PM passed through the filter media and it is typically ignored. But condensable PM was defined as a component of primary PM. This study investigates the change of condensable PM according to the variation in the sulfur dioxide of combustion gas. Domestic oil boilers were used as the source of emission ($SO_2$) and the level of $SO_2$ concentration (0, 50, 80, and 120 ppm) was adjusted by diluting general light oil and marine gas oil (MGO) that contains sulfur less than 0.5%. Condensable PM was measured as 2.72, 6.10, 8.38, and $13.34mg/m^3$ when $SO_2$ concentration in combustion gas were 0, 50, 80, and 120 ppm respectively. The condensable PM tended to increase as the concentration of $SO_2$ increased. Some of the gaseous air pollutants emitted from the stack should be considered precursors of condensable PM. The gas phase pollutants which converted into condensable PM should reduced for condensable PM control.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.1
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• pp.11-18
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• 2017

The performance of a modeling system consisting of WRF model v3.3 and CMAQ model v4.7.1 for forecasting $PM_{2.5}$ concentrations were evaluated during the period May 2012 through December 2014. Twenty-four hour averages of $PM_{2.5}$ and its major components obtained through filter sampling at the Bulgwang intensive measurement station were used for comparison. The mean predicted $PM_{2.5}$ concentration over the entire period was 68% of the mean measured value. Predicted concentrations for major components were underestimated except for $NO_3{^-}$. The model performance for $PM_{2.5}$ generally tended to degrade with increasing the concentration level. However, the mean fractional bias (MFB) for high concentration above the $80^{th}$ percentile fell within the criteria, the level of accuracy acceptable for standard model applications. Among three bias correction methods, the ratio adjustment was generally most effective in improving the performance. Albeit for limited test conditions, this analysis demonstrated that the effects of bias correction were larger when using the data with a larger bias of predicted values from measurement values.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.101-104
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• 2004

본 논문에서는 보상기를 채용하여 안정성을 확보한 적응순환필터인 ACHARF(Adaptive Compensated Hyperstable Adaptive Recursive Filter)를 사용하여 잡음제거를 통한 음성의 음질개선을 DSP 프로세서를 통하여 구현하였다. 실험에서는 TI사의 최신 DSP 프로세서인 TMS320C6413와 스테레오 오디오 코덱인 TLV320AIC23을 탑재한 Evaluation board를 사용하였다. 2개의 입력마이크를 이용하여 음성신호와 기준 잡음신호를 별도로 수집하여 알고리즘을 수행하였으며, 실험 결과로 음질개선 효과를 확인할 수 있었다. 본 연구를 통해서 시스템의 성능개선의 핵심은 입력으로 들어오는 음성신호와의 상관도가 가능한 적은 잡음신호를 수집하는 방법이라 생각되며 앞으로 이에 대한 연구가 필요하겠다.

• Membrane Journal
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• v.15 no.2
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• pp.85-104
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• 2005

Recently, membrane can be prepared by two methods, phase inversion and electrospinning techniques. Phase inversion technique is a conventional but commercially preparation membrane. The most versatile of preparation in this technique is immersion of the cast film into nonsolvent bath, causing dense top layer with a finger-like pattern in the sub layer membrane. The membrane pore size getting from phase inversion is in the range of micro or submicrometer. As a result, it can be used as microfiltration and ultrafiltration applications. A new technique, electrospinning, is introduced for membrane preparation. Nonwoven nanofibrous mat or nanofibrous membrane is obtained. In this technique, electrostatic charge is introduced to the solution jet, causing a thin fiber with high surface area; hence it can be used in the applications where high surface area-to-volume or length-to-diameter ratios are required. Moreover, the pore size can be controlled by controlling the time of electrospinning. Hence, it can be used as a filter for filtering microparticles as well as nanoparticles.

• Journal of Korean Society for Quality Management
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• v.22 no.2
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• pp.20-32
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• 1994

This paper is concerned with the design of four control charts for real-time monitoring of the continuous flow processes. Control charts for both uncorrelated data and correlated data are designed using the Kalman filtering techinque. The relative performance between the designed control charts and traditional control charts is evaluated in terms of the Average Run Length(ARL). Results show that the Adaptive EWMA control charts designed for uncorrelated data has better performance when process mean is shifted, while the residual control charts for correlated data has better performance when process is in control.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.35-42
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• 2017

This paper presents hardware implementation of background timing-skew calibration technique for time-interleaved analog-to-digital converters (TI ADCs). The timing skew between any two adjacent analog-digital (A/D) channels is detected by using pure digital Finite Impulse Response (FIR) delay filter. This paper includes hardware architecture of the system, main units and small sub-blocks along with control logic circuits. Moreover, timing diagrams of logic simulations using ModelSim are provided and discussed for further understanding about simulations. Simulation process in MATLAB and Verilog is also included and provided with basic settings need to be done. For hardware implementation it not practical to work with all samples. Hence, the simulation is conducted on 512 TI ADC output samples which are stored in the buffer simultaneously and the correction arithmetic is done on those samples according to the time skew algorithm. Through the simulated results, we verified the implemented hardware is working well.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2085-2087
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• 2003

A new interacting multiple model (IMM) method based on intelligent input estimation (IIE) is proposed for tracking a maneuvering target. In the proposed method, the acceleration level of each sub-filter is determined by IIE using the fuzzy system, which is optimized by the genetic algorithm (GA). The tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method in computer simulations.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.139-142
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• 2018

This work presents a novel architecture of phase locked loop (PLL) with the current compensating scheme to improve phase noise characteristic. The proposed PLL has two charge pumps (CP), main-CP (MCP) and sub-CP (SCP). The smaller SCP current with same time duration but opposite direction of UP/DN MCP current is injected to the loop filter (LF). It suppresses the voltage fluctuation of LF. The PLL has a novel voltage controlled oscillator (VCO) consisting of a voltage controlled resistor (VCR) and the three-stage ring oscillator with latch type delay cells. The VCR linearly converts voltage into current, and the latch type delay cell has short active on-time of transistors. As a result, it improves phase noise characteristic. The proposed PLL has been fabricated with $0.35{\mu}m$ 3.3 V CMOS process. Measured phase noise at 1 MHz offset is -103 dBc/Hz resulting in 3 dBc/Hz phase noise improvement compared to the conventional PLL.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.652-654
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• 2012

본 논문에서는 다중 심도 카메라 기반의 실시간 피플 카운팅 시스템을 제안 한다. 카메라 영상으로부터 사람을 감지하고 추적하는 시스템 및 그 방법에 관한 것으로, 피플 카운팅 시스템은 쇼핑몰이나 대형건물의 출입구 등과 같은 다양한 환경에 적용될 수 있다. 기존 피플 카운팅 시스템에서의 급격한 조명의 변화나 겹침 현상, 가림 현상에 대한 해결 방법으로, 다중 심도 카메라 환경에서 동일 객체 추적을 위해 RLM(Range Laser Method)를 적용하고, 조명 등 환경 변화에 강인한 배경 제거 및 물체 검출 기법으로 가우시안 혼합 모델(Gaussian Mixture Model)을 적용해 객체인식에 대한 정확도를 높인다. 또한, 객체를 블랍(Blob)으로 지정해 확장 칼만 필터(Extended Kalman Filter, EKF) 방법으로 객체를 추적한다. 본 제안은 피플 카운팅 시스템에의 객체 검출 및 인식에 대한 정확도를 향상시킬 수 있으리라 기대된다.