• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.60-61
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• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.

• Journal of Soil and Groundwater Environment
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• v.14 no.4
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• pp.23-32
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• 2009

The sorption of europium (Eu (III)) onto kaolinite and the influence of humic acids over a range of pH 3 ~ 11 has been studied by batch adsorption experiment (V/m = 250 : 1 mL/g, $C_{Eu(III)}\;=\;1\;{\times}\;10^{-5}\;mol/L$, $C_{HA}\;=\;5{\sim}50\;mg/L$, $P_{CO2}=10^{-3.5}\;atm$). The concentrations of HA and Eu(III) in aqueous phase were measured by UV absorbance at 254nm (e.g., $UV_{254}$) and ICP-MS after microwave digestion for HA removals, respectively. Results showed that the HA sorption onto kaolinite was decreased with increasing pH and their sorption isotherms fit well with the Langmuir adsorption model (except pH 3). Maximum amount ($q_{max}$) for the HA sorption at pH 4 to 11 was ranged from 4.73 to 0.47 mg/g. Europium adsorption onto the kaolinite in the absence of HA was typical, showing an increases with pH and a distinct adsorption edge at pH 3 to 5. However in the presence of HA, Eu adsorption to kaolinite was significantly affected. HA was shown to enhance Eu adsorption in the acidic pH range (pH 3 ~ 4) due to the formation of additional binding sites for Eu coming from HA adsorbed onto kaolinite surface, but reduce Eu adsorption in the intermediate and high pH above 6 due to the formation of aqueous Eu-HA complexes. The results on the ternary interaction of kaolinte-Eu-HA are compared with those on the binary system of kaolinite-HA and kaolinite-Eu and adsorption mechanism with pH was discussed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.135-151
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• 2021

This research attempts to analyze the factors influencing the entrepreneurial intention of employees in manufacturing field. In particular, key factors of entrepreneurship and personal characteristics explain a significant association with the intention to start-up. And study whether R&D support from public enterprise adjusts intention to entrepreneurial Intention. This study conducted a online survey on 292 small and medium-sized enterprise manufacturing employees in May 2020. Using linear regression model and binary logistic model. The main study results are the following: First, among the key factors(innovativeness, proactiveness, risk-taking) of entrepreneurship, proactiveness hardly influenced the opportunity competency. Second, among the factors(risk-taking propensity, locus of control, tolerance for ambiguity) of personal characteristics, locus of control hardly influenced the opportunity competency. Third, opportunity competency(opportunity recognition and opportunity evaluation) had positive influence to entrepreneurial intention. Fourth, the study investigated the mediated effect of opportunity competency. The result showed that among the factors of entrepreneurship and personal characteristics, only two factors that are proactiveness and locus of control were not mediated by opportunity competency. and opportunity evaluation was acted as a mediator between proactiveness and entrepreneurial Intention, compared with opportunity recognition. Lastly, public enterprise's R&D supporting moderated the entrepreneurial intention). Based on the result, the study showed that first, the key factors of entrepreneurship except for proactiveness and personal characteristics(risk-taking propensity, locus of control, tolerance for ambiguity) except for locus of control affect the intention to start-up, repeatedly. This results are explained that employees have not started a business yet. Second, research on start-up suggests the need to analyze factors differentiated before and after the start-ups. Based on the results, entrepreneurship and personal characteristics show that study on the effects of start-up intentions should be carried out before and after the actual start-up takes place, and can be used as effective data in policies to promoting start-ups in manufacturing field.

• Journal of agricultural medicine and community health
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• v.46 no.3
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• pp.131-143
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• 2021

Objectives: This study aimed 1) to investigate high school students' reliability on COVID-19 responses in schools and private academies and 2) to identify the differences in COVID-19 prevention practice. Methods: This cross-sectional survey collected data from 200 high school respondents, using an anonymous online questionnaire designed by the Yonsei Global Health Center, from July 2 to 17, 2020 in this study. Chi-square tests were conducted to analyze the differences in preventative practices and practice rates between schools and private academies. Binary logistics regression analysis was conducted to identify the factor affecting the reliability of COVID-19 response. Results: These high school students reliabilityed the schools' COVID-19 response more than the private academy. In addition, students who studied only at school did more COVID-19 prevention practices than students who studied both at school and academy. There was a significant difference in avoiding public transportation (p=.028), sitting in one row while having a meal (p=.011) in the practice rates depending on the schools' COVID-19 response. A significant difference in Covering the mouth when coughing and sneezing (p-.041) was also found in the practice rates depending on the private academies' COVID-19 response. Conclusion: The reason why schools were more reliable than private academies was that there are health teachers. Because schools are supervised by the ministry of education, the Ministry of education and local government need to work together to manage and monitor the COVID-19 response in the academies through cooperation between two organizations. In addition, it is necessary to arrange a temporary circulation health teacher who will provide the COVID-19 prevention education at the academies.

• Journal of Korean Philosophical Society
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• v.145
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• pp.91-117
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• 2018

This paper aims to clarify the similarities and differences between the Zhouyi and artificial intelligence. The divination of the Zhouyi is rooted in the oldest system of human knowledge, while artificial intelligence stands at the cutting edge of modern scientific revolution. At first sight, there does not appear to be any association that links the one to the another. However, they share the same ground as seen from a semiotic standpoint because both of them depend on the semiotic system as a means of obtaining knowledge. At least four aspects can be pointed out in terms of similarities. First, artificial intelligence and the Zhouyi use artificial language that consists of semiotic signs. Secondly, the principle that enables divination and artificial intelligence lies in imitation and representation. Thirdly, artificial intelligence and the Zhouyi carry out inferences based on mathematical algorithms that adopt the binary system. Fourth, artificial intelligence and the Zhouyi use analogy as a means of obtaining knowledge. However, those similarities do not guarantee that the Zhouyi could arrive at the scientific certainty. Nevertheless, it can give us important insight into the essence of our civilization. The Zhouyi uses intellect in order to get new information about the unknown world. However, it is hard to know what kind of intellect is involved in the process of divination. Likewise, we do not know the fundamental character of artificial intelligence. The intellect hidden in the unknown subject is a mystic and fearful existence to us. Just as the divination of the Zhouyi inspires the sense of reverence toward the supernatural subject, we could not but have fear in front of the invisible subject hidden in artificial intelligence. In the past, traditional philosophy acknowledged the existence of intellect only in conscious beings. Nonetheless, it becomes evident that human civilization ushers into a new epoch. As Ray Kurzweil mentioned, the moment of singularity comes when artificial intelligence surpasses human intelligence. In my viewpoint, the term of singularity can be used for denoting the critical point in which the human species enters into the new phase of civilization. To borrow the term of Shao Yong(邵雍) in the Northern Song Dynasty, the past civilization belongs to the Earlier Heaven(先天), the future civilization belongs to the Later Heaven(後天). Once our civilization passes over the critical point, it is impossible to go back into the past. The opening of the Later Heaven foretold by the religious thinkers in the late period of Joseon Dynasty was a prophecy in its own age, but it is becoming a reality in the present.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.562-569
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• 2019

Raman spectroscopy has been receiving increased attention as a standoff explosive detection technique. In addition, there is a growing need for a fast search method that can identify raman spectrum for measured chemical substances compared to known raman spectra in large database. By far the most simple and widely used method is to calculate and compare the Euclidean distance between the given spectrum and the spectra in a database. But it is non-trivial problem because of the inherent high dimensionality of the data. One of the most serious problems is the high computational complexity of searching for the closet spectra. To overcome this problem, we presented the MPS Sort with Sorted Variance+PDS method for the fast algorithm to search for the closet spectra in the last paper. the proposed algorithm uses two significant features of a vector, mean values and variance, to reject many unlikely spectra and save a great deal of computation time. In this paper, we present two new methods for the fast algorithm to search for the closet spectra. the PCA+PDS algorithm reduces the amount of computation by reducing the dimension of the data through PCA transformation with the same result as the distance calculation using the whole data. the Hierarchical Cluster Tree algorithm makes a binary hierarchical tree using PCA transformed spectra data. then it start searching from the clusters closest to the input spectrum and do not calculate many spectra that can not be candidates, which save a great deal of computation time. As the Experiment results, PCA+PDS shows about 60.06% performance improvement for the MPS Sort with Sorted Variance+PDS. also, Hierarchical Tree shows about 17.74% performance improvement for the PCA+PDS. The results obtained confirm the effectiveness of the proposed algorithm.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.503-508
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• 2019

In short a binary value according to a change in the tube voltage by using one of VOLUME AXIAL MODE of scanning techniques of chest CT image quality evaluation in order to obtain high image and to present the appropriate tube voltage. CT instruments were GE Revolution (GE Healthcare, Wisconsin USA) model and Phantom used Pediatric Whole Body Phantom PBU-70. The test method was examined in Volume Axial mode using the pediatric protocol used in the Y university hospital of mass-produced material. The tube voltage was set to 70kvp, 80kvp, 100kvp, and mAs was set to smart mA-ODM. The mean SNR difference of the heart was $-4.53{\pm}0.26$ at 70 kvp, $-3.34{\pm}0.18$ at 80 kvp, $-1.87{\pm}0.15$ at 100 kvp, and SNR at 70 kvp was about -2.66 higher than 100 kvp and statistically significant (p<0.05) In the Lung SNR mean difference analysis, $-78.20{\pm}4.16$ at 70 kvp, $-79.10{\pm}4.39$ at 80 kvp, $-77.43{\pm}4.72$ at 100 kvp, and SNR at 70 kvp at about -0.77 higher than 100 kvp were statistically significant. (p<0.05). Lung CNR mean difference was $73.67{\pm}3.95$ at 70 kvp, $75.76{\pm}4.25$ at 80 kvp, $75.57{\pm}4.62$ at 100 kvp and 20.9 CNR at 80 kvp higher than 70 kvp and statistically significant (p<0.05) At 100 kvp of tube voltage, the SNR was close to 1 while maintaining the quality of the heart image when 70 kvp and 80 kvp were compared. However, there is no difference in SNR between 70 kvp and 80 kvp, and 70 kvp can be used to reduce the radiation dose. On the other and, CNR showed an approximate value of 1 at 70 kvp. There is no difference between 80 kvp and 100 kvp. Therefore, 80 kvp can reduce the radiation dose by pediatric chest CT. In addition, it is possible to perform a scan with a short scan time of 0.3 seconds in the volume axial mode test, which is useful for pediatric patients who need to move or relax.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.65-73
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• 2019

The MODIS (Moderate Resolution Imaging Spectroradiometer) data in Hierarchical Data Format (HDF) have been processed using the Geospatial Data Abstraction Library (GDAL). Because of a relatively large data size, it would be preferable to build and install the data analysis tool with greater computing performance, which would differ by operating system and the form of distribution, e.g., source code or binary package. The objective of this study was to examine the performance of the GDAL for processing the HDF files, which would guide construction of a computer system for remote sensing data analysis. The differences in execution time were compared between environments under which the GDAL was installed. The wall clock time was measured after extracting data for each variable in the MODIS data file using a tool built lining against GDAL under a combination of operating systems (Ubuntu and openSUSE), compilers (GNU and Intel), and distribution forms. The MOD07 product, which contains atmosphere data, were processed for eight 2-D variables and two 3-D variables. The GDAL compiled with Intel compiler under Ubuntu had the shortest computation time. For openSUSE, the GDAL compiled using GNU and intel compilers had greater performance for 2-D and 3-D variables, respectively. It was found that the wall clock time was considerably long for the GDAL complied with "--with-hdf4=no" configuration option or RPM package manager under openSUSE. These results indicated that the choice of the environments under which the GDAL is installed, e.g., operation system or compiler, would have a considerable impact on the performance of a system for processing remote sensing data. Application of parallel computing approaches would improve the performance of the data processing for the HDF files, which merits further evaluation of these computational methods.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• The Journal of Bigdata
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• v.6 no.1
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• pp.83-89
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• 2021

Lifestyle is changing rapidly, and food consumption patterns vary widely among households as dietary and food processing technologies evolve. This paper reclassified the food group of consumer panel data established by the Rural Development Administration, which contains information on purchasing agricultural products by household unit, and compared the consumption characteristics of agricultural products by age group. The criteria for age classification were divided into groups in their 60s and older with a prevalence of 20% or more metabolic diseases and groups in their 30s and 40s with less than 10%. Using the LightGBM algorithm, we classified the differences in food consumption patterns in their 30s and 50s and 60s and found that the precision was 0.85, the reproducibility was 0.71, and F1_score was 0.77. The results of variable importance were confectionery, folio, seasoned vegetables, fruit vegetables, and marine products, followed by the top five values of the SHAP indicator: confectionery, marine products, seasoned vegetables, fruit vegetables, and folio vegetables. As a result of binary classification of consumption patterns as a median instead of the average sensitive to outliers, confectionery showed that those in their 30s and 40s were more than twice as high as those in their 60s. Other variables also showed significant differences between those in their 30s and 40s and those in their 60s and older. According to the study, people in their 30s and 40s consumed more than twice as much confectionery as those in their 60s, while those in their 60s consumed more than twice as much marine products, seasoned vegetables, fruit vegetables, and folioce or logistics as much as those in their 30s and 40s. In addition to the top five items, consumption of 30s and 40s in wheat-processed snacks, breads and noodles was high, which differed from food consumption patterns in their 60s.