• Analytical Science and Technology
• /
• v.31 no.4
• /
• pp.149-160
• /
• 2018

A large number of functional living products are being produced for eco-friendly or health-promoting purposes. In the manufacturing process, such products could be adulterated with raw materials with high radioactivity, such as monazite and tourmaline. Thus, it is essential to manage raw materials and products closely related to the public living. For proper management, an accurate radioactivity data of the processed products are needed. Therefore, it is essential to develop a rapid and validated analytical method. In this study, the concentration of the radioactive $^{238}U$ and $^{232}Th$ in building materials (e.g., tile, cement, paint, wall paper, and gypsum board) and living products (e.g., health products, textiles, and minerals) were determined and compared by ED-XRF and ICP-MS. By comparing the results of both methods, we confirmed the applicability of the rapid screening and precise analysis of ED-XRF and ICP-MS. In addition, $^{238}U$ and $^{232}Th$ levels were relatively lower in building materials than in living products. Particularly, $^{232}Th$ content in 6 of 47 living products exceeded (maximum $8.2Bq{\cdot}g^{-1}$) the standard limit of $^{232}Th$ content in raw material ($1.0Bq{\cdot}g^{-1}$).

• The Journal of Korean society of community based occupational therapy
• /
• v.8 no.1
• /
• pp.33-44
• /
• 2018

Objective : This study aims to review research trends in driving rehabilitation for the disabled in South Korea since 2000 suggesting research directions for clinicians and researchers. Methods : Fifty eight articles in 16 journals listed in accredited or candidate journal lists of National Research Foundation of Korea from January, 2000 to December, 2016 were reviewed. 'Driving rehabilitation' and 'driving for disabled' were used as search terms. Descriptive statistics were used to classify articles according to study methodology, levels of evidence, study participants, research topics, and academic associations or official journals. Results : Fifty percent of analyzed researches have been published since 2012. Twenty-two studies (37.9%) were published as group comparison and correlational research. Only seven studies (12.1%) were included in evidence level I. There were 19 studies (38.8%) conducted with brain-injured patients among 49 studies including participants. The Korean Society of Occupational Therapy Journal, having published 15 studies (25.9%) about driving rehabilitation, ranked first among the analyzed journals. In research topic, 15 (25.9%) studies were performed about clinical evaluation. Conclusion : The present study showed that the quality of driving rehabilitation-related studies has been increasing, but more intervention-based researches need to be carried out and it is also necessary to carry out various researches in related fields in order to establish efficient driving rehabilitation in Korea.

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

• Korean Journal of Plant Resources
• /
• v.22 no.5
• /
• pp.403-411
• /
• 2009

This study was conducted to gather the basic data on the increase of utilization for the Japanese staunton vine (Stauntonia hexaphylla), native plants which were grown in the southern districts in Korea. We have also determined their partial physical and chemical compositions and their physiological activities. Vitamin C contents in fruit skin was 85.23 mg/100 g, and that in flesh was 61.67 mg/100 g. Total amino acids contents in fruit skin increased much more by 762.72 mg/100 g DW compared to that in flesh by 434.05 mg/100 g DW. Inorganic matter contents were more increased in the fruit skin (108.48 mg/$\ell$) and its main components were K (76.53 mg/$\ell$), Ca (20.20 mg/$\ell$) and Mg (6.22 mg/$\ell$). Total phenol compound and flavonoid contents in 1,000 mg/$\ell$ methanol extracts were 7.3-9.6 mg/$\ell$ and 5.1-6.7 mg/$\ell$. Nitrite radical scavenging activity in 4,000 mg/$\ell$ methanol extracts of fruit skin and flesh for Stauntonia hexaphylla were 79.5% and 77.8%, however, that in seeds was 17.1%. Overall mushroom tyrosinase inhibition activity (% of control) was less than 10.8%. Anti-microbial activities of methanol extracts from the fruit skin against the gram negative and positive microbial strains were not significant in the lower concentration of extracting solution, however, that from flesh and seeds in terms of the inhibition diameter were $8.91{\sim}12.25\;mm$.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.34 no.5
• /
• pp.445-456
• /
• 2001

In the 1990s, Cochlodinium polykikoides red tide has been annually occurred in the southern coast of Korea and caused the mass damage to the fisheries with a huge amount of economic loss. The present study was done to establish the biological foundation for the elucidation of the mechanism of C. polykikoiaes red tide. The growth response of C. polykikoides to physico-chemical factors such as temperature, salinity, pH, and light intensity were examined using axenic cultures to evaluate the relative importance of these factors on the dynamics of natural populations, It was found that the highest growth conditions were $25^{\circ}C,\;40\%_{\circ}$, pH 7.5, and 7,500 lux, respectively. The tolerable salinity range of growth was relatively wide at an optimum temperature and was reduced to a much narrower range at a sub-optimum temperature. These findings indicate that C. polykikoides is an eurythermal and euryhaline organism. The organism demanded higher light intensity and oceanic pH narrow in its growth. C. polykikoides utilize inorganic nutrients, such as nitrate and ammonium as N, and phosphate as P. The nutritional requirements of C. polykikoides were $40{\mu}M$ for nitrate, $50{\mu}M$ for ammonium, and $5{\mu}M$ for phosphate. The half saturation constant (Ks) for growth was $2.10{\mu}M$ for nitrate, $1.03{\mu}M$ for ammonium, and $0.57{\mu}M$ for phosphate. These values were comparatively smaller than those of other dinoflagellates reported previously. We confirmed that the organism is characterized as an eutrophic species. However, ammonium Ks value is smaller than that of other eutrophic species, This result indicates that C. polykikoides red tide may outbreak in the waters which eutrophication is in progress rather than eutrophicated waters. C. polykikoides preferred ammonium better than nitrate as a nitrogen source when in a growth stage, Therefore, our results indicate that ammonium is more important nutrient on the growth of the organism in comparison with other inorganic nutrients and C. polykikoides red tide is related with the increased ammonium concentration in the coastal waters.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.137-154
• /
• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.