• Korean Journal of Optics and Photonics
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• v.15 no.4
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• pp.299-308
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• 2004

We proposed more efficient encoding methods that can design a multi-channel multi-level phase only computer-generated hologram(CGH) that can reconstruct many objects simultaneously without a conjugate image. We used a fabrication technique for the pixel oriented CGH for designing the pattern of the proposed multi-channel CGH. We investigated the difference of the optical efficiency(η), mean square error(MSE) and signal-to-noise ratio(SNR) of multi-channel CGHs that were designed by three kinds of encoding methods according to the number of quantization phase levels, and we estimated the performance of the pattern of the proposed multi-channel CGH. Generally, as the number of input objects' reference patterns stored in the CGH is increased, the reconstruction quality of the CGH is degraded. But we observed through computer simulation that the diffraction efficiency of the 1-ch CGH is 70%, and those of the 2-ch, 4-ch, 8-ch CGHs are 62%, 62% and 63%. Therefore we found that the diffraction efficiencies of the multi-channel CGHs using the newly proposed encoding method are similar to that of 1-ch CGH. We implemented the CGH optically using a liquid crystal spatial light phase modulator that consisted of a PAL-SLM efficiently coupled with a XGA type LCD by an optical lens and an LD for illuminating the LCD. We discussed the output images that are reconstructed from the PAL-SLM.

• The Korean Journal of Nuclear Medicine Technology
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• v.24 no.1
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• pp.15-19
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• 2020

Purpose Generative Adversarial Network(GAN) is one of deep learning technologies. This is a way to create a real fake image after learning the real image. In this study, after acquiring artificial intelligence images through GAN, We were compared and evaluated with real scan time images. We want to see if these technologies are potentially useful. Materials and Methods 30 patients who underwent ¹⁸F-FDG Brain PET/CT scanning at Severance Hospital, were acquired in 15-minute List mode and reconstructed into 1,2,3,4,5 and 15minute images, respectively. 25 out of 30 patients were used as learning images for learning of GAN and 5 patients used as verification images for confirming the learning model. The program was implemented using the Python and Tensorflow frameworks. After learning using the Pix2Pix model of GAN technology, this learning model generated artificial intelligence images. The artificial intelligence image generated in this way were evaluated as Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR), and Structural Similarity Index(SSIM) with real scan time image. Results The trained model was evaluated with the verification image. As a result, The 15-minute image created by the 5-minute image rather than 1-minute after the start of the scan showed a smaller MSE, and the PSNR and SSIM increased. Conclusion Through this study, it was confirmed that AI imaging technology is applicable. In the future, if these artificial intelligence imaging technologies are applied to nuclear medicine imaging, it will be possible to acquire images even with a short scan time, which can be expected to reduce artifacts caused by patient movement and increase the efficiency of the scanning room.

• Journal of Food Hygiene and Safety
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• v.34 no.4
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• pp.374-379
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• 2019

This study developed predictive growth models of Salmonella enterica Serovar Typhimurium on lettuce washed with chlorine (100~300 ppm) and ultrasound (US, 37 kHz, 380 W) treatment and stored at different temperatures ($10{\sim}25^{\circ}C$) using a polynomial equation. The primary model of specific growth rate (SGR) and lag time (LT) showed a good fit ($R^2{\geq}0.92$) with a Gompertz equation. A secondary model was obtained using a quadratic polynomial equation. The appropriateness of the secondary SGR and LT model was verified by coefficient of determination ($R^2=0.98{\sim}0.99$ for internal validation, 0.97~0.98 for external validation), mean square error (MSE=-0.0071~0.0057 for internal validation, -0.0118~0.0176 for external validation), bias factor ($B_f=0.9918{\sim}1.0066$ for internal validation, 0.9865~1.0205 for external validation), and accuracy factor ($A_f=0.9935{\sim}1.0082$ for internal validation, 0.9799~1.0137 for external validation). The newly developed models for S. Typhimurium could be incorporated into a tertiary modeling program to predict the growth of S. Typhimurium as a function of combined chlorine and US during the storage. These new models may also be useful to predict potential S. Typhimurium growth on lettuce, which is important for food safety purposes during the overall supply chain of lettuce from farm to table. Finally, the models may offer reliable and useful information of growth kinetics for the quantification microbial risk assessment of S. Typhimurium on washed lettuce.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.101-107
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• 2020

Road surface measurement is an essential process for quantifying the degree and displacement of roughness in road surface management. For safer road surface management and quick maintenance, it is important to accurately measure the road surface while mounted on a vehicle. In this paper, we propose a sophisticated road surface measurement system that can be measured on a moving vehicle. The proposed road surface measurement system supports more accurate measurement of the road surface by using a high-performance line laser sensor. It is also possible to measure the transverse and longitudinal profile by matching the position information acquired from the RTK, and the velocity adaptive update algorithm allows a manager to monitor in a real-time manner. In order to evaluate the proposed system, the Gocator laser sensor, MRP module, and NVIDIA Xavier processor were mounted on a test mobile and tested on the road surface. Our evaluation results demonstrate that our system measures accurate profile base on the MSE. Our proposed system can be used not only for evaluating the condition of roads but also for evaluating the impact of adjacent excavation.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.10
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• pp.629-636
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• 2016

This study presents the development of an acid resistance velocity sensor that is used for measuring velocity inside a copper sulfate plating bath. First, researchers investigated the acid resistance coating to confirm the suitability of the anti-acid sensor in a very corrosive environment. Then, researchers applied signal processing methods to reduce noise and amplify the signal. Next, researchers applied a pressure-resistive sensor with an operation amplifier (Op Amp) and low-pass filter with high impedance to match the output voltage of a commercial flowmeter. Lastly, this study compared three low-pass filters (Bessel, Butterworth and Chebyshev) to select the appropriate signal process circuit. The results show 0.0128, 0.0023, and 5.06％ of the mean square error, respectively. The Butterworth filter yielded more precise results when compared to a commercial flowmeter. The acid resistive sensor is capable of measuring velocities ranging from 2 to 6 m/s with a 2.7％ margin of error.

• Journal of Food Hygiene and Safety
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• v.26 no.1
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• pp.25-30
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• 2011

In this study, predictive mathematical models were developed to predict the kinetics of Listeria monocytogenes growth in the mixed fresh-cut vegetables, which is the most popular ready-to-eat food in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At the specified storage temperatures, the primary growth curve fit well ($r^2$=0.916~0.981) with a Gompertz and Baranyi equation to determine the specific growth rate (SGR). The Polynomial model for natural logarithm transformation of the SGR as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). As the storage temperature decreased from $30^{\circ}C$ to $4^{\circ}C$, the SGR decreased, respectively. Polynomial model was identified as appropriate secondary model for SGR on the basis of most statistical indices such as mean square error (MSE=0.002718 by Gompertz, 0.055186 by Baranyi), bias factor (Bf=1.050084 by Gompertz, 1.931472 by Baranyi) and accuracy factor (Af=1.160767 by Gompertz, 2.137181 by Baranyi). Results indicate L. monocytogenes growth was affected by temperature mainly, and equation was developed by Gompertz model (-0.1606+$0.0574^*Temp$+$0.0009^*Temp^*Temp$) was more effective than equation was developed by Baranyi model (0.3502-$0.0496^*Temp$+$0.0022^*Temp^*Temp$) for specific growth rate prediction of L.monocytogenes in the mixed fresh-cut vegetables.

• Journal of Korea Multimedia Society
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• v.9 no.7
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• pp.872-893
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• 2006

In this study, I classify the elements of physiognomic judgment of character with regard to form and meaning from a visual perspective based on physiognomic judgment of character study in 'the cosmic dual forces and the Five Elements theory'. Individual characters for each type are designed using graphic data. Based on that, design system of individual characters for each personality type is investigated using Neural Network system. Faces with O-Haeng (Five Elements) shapes are shown to constitute the system with ${\pm}0.3%$ degree of error tolerance for the non-loaming input data. For the shapes of Chinese characters 'tree, fire, soil, gold and water', their MSE(Mean Square Error) are 0.3, 0.3, 0.2, 0.5, 0.2. It seems to be the best regarding the scoring system which ranges from 0 to 5. Therefore, this system might be regarded to produce the most accurate facial shape of character automatically when we input character's personality we desire to make.

• Korean Journal of Poultry Science
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• v.43 no.1
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• pp.1-14
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• 2016

Prediction of growth patterns of commercial chicken strains is important. It can provide visual assessment of growth as function of time and prediction body weight (BW) at a specific age. The aim of current study is to compare the three nonlinear functions (i.e., Logistic, Gompertz, and von Betalanffy) for modeling the growth of twenty five commercial Korean native chicken (KNC) strains reared under a battery cage system until 32 weeks of age and to evaluate the three models with regard to their ability to describe the relationship between BW and age. A clear difference in growth pattern among 25 strains were observed and classified in to the groups according to their growth patterns. The highest and lowest estimated values for asymptotic body weight (C) for 3H and 5W were given by von Bertalanffy and Logistic model 4629.7 g for 2197.8 g respectively. The highest estimated parameter for maturating rate (b) was given by Logistic model 0.249 corresponds to the 2F and lowest in von Bertalanffy model 0.094 for 4Y. According to the coefficient of determination ($R^2$) and mean square of error (MSE), Gompertz and von Bertalanffy models were suitable to describe the growth of Korean native chicken. Moreover, von Bertalannfy model was well described the most of KNC growth with biologically meaningful parameter compared to Gompertz model.

• Journal of Microbiology and Biotechnology
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• v.15 no.6
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• pp.1323-1329
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• 2005

A mathematical model was developed for predicting the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) as a function of combined effects of temperature, pH, and NaCl. The TSB containing four different concentrations of NaCl (2, 4, 5, and $10\%$) was initially adjusted to six different pH levels (pH 5, 6, 7, 8, 9, and 10) and incubated at 4, 10, 25, or 37$^{circ}C$. In all experimental variables, the primary growth curves were well fitted ($r^{2}$=0.982 to 0.998) to a Gompertz equation to obtain the lag time (LT) and specific growth rate (SGR). Surface response models were identified as appropriate secondary models for LT and SGR on the basis of coefficient determination ($r^{2}$=0.907 for LT, 0.964 for SGR), mean square error (MSE=3.389 for LT, 0.018 for SGR), bias factor ($B_{1}$B,=0.706 for LT, 0.836 for SGR), and accuracy factor ($A_{f}$=1.567 for LT, 1.213 for SGR). Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on both LT and SGR for L. monocytogenes in TSB.

• Journal of Microbiology and Biotechnology
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• v.17 no.9
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• pp.1437-1444
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• 2007

A response surface model was developed for predicting the growth rates of Staphylococcus aureus in tryptic soy broth (TSB) medium as a function of combined effects of temperature, pH, and NaCl. The TSB containing six different concentrations of NaCl (0, 2, 4, 6, 8, and 10%) was adjusted to an initial of six different pH levels (pH 4, 5, 6, 7, 8, 9, and 10) and incubated at 10, 20, 30, and $40^{\circ}C$. In all experimental variables, the primary growth curves were well ($r^2=0.9000$ to 0.9975) fitted to a Gompertz equation to obtain growth rates. The secondary response surface model for natural logarithm transformations of growth rates as a function of combined effects of temperature, pH, and NaCl was obtained by SAS's general linear analysis. The predicted growth rates of the S. aureus were generally decreased by basic (pH 9-10) or acidic (pH 5-6) conditions and higher NaCl concentrations. The response surface model was identified as an appropriate secondary model for growth rates on the basis of correlation coefficient (r=0.9703), determination coefficient ($r^2=0.9415$), mean square error (MSE=0.0185), bias factor ($B_f=1.0216$), and accuracy factor ($A_f=1.2583$). Therefore, the developed secondary model proved reliable for predictions of the combined effect of temperature, NaCl, and pH on growth rates for S. aureus in TSB medium.