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

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.5
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• pp.536-544
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• 2001

Three harmful algal bloom species with similar morphology, Cochlodinium polykrikoides, Gyodinium impudicum and Gymodinium catenatum have damaged to aquatic animals or human health by either making massive blooms or intoxication of shellfishes in a food chain. Eco-physiological and hydrodynamic studies on the harmful algae offer useful informations in the understanding their bloom mechanism by giving promising data for the prediction and modelling of harmful algal blooms event. Thus, we studied the abundance of these species in the coastal area of South Sea of Korea and their effects of temperature, salinity, irradiance and nutrient on the growth for the isolates. The timing for initial appearance of the three species around the coastal area of Namhaedo, Narodo and Wando was between Bate July and late August in 1999 when water temperature ranged from $22.8^{\circ}C\;to\;26.5^{\circ}C$ Vegetative cells of C. polykrikoides and G. impudicum were abundant until late September when water temperature had been dropped to less than $23^{\circ}C$. By contrast, vegetative cell of G. catenatum disappeared before early September, showing shorter period of abundance than the other two species in the South Sea. Both G. impudicum and G. catenatum revealed comparatively low density with a maximal cell density of 3,460 cells/L and 440 cells/L, respectively without making any bloom, while C. polykrikoides made massive blooms with a maximal cell density more than $40\times10^6$cells/L, The three species showed a better growth at the relatively higher water temperature ranging from 22 to $28^{\circ}C$ with their maximal growth rate at $25^{\circ}C$ in culture, which almost corresponded with the water temperature during the outbreak of C. polykrikoides in the coastal area of South Sea. Also, they all showed a relatively higher growth at the salinity from 30 to $35\%$. Specially, G. impudicum showed the euryhalic characteristics among the species, On the other hand, growth rate of G. catenatum decreased sharply with the increase of water temperature at the experimental ranges more than $35\%$. The higher of light intensities showed the better growth rates for the three species, Moreover, C. polykrikoides and G. impudirum continued their exponential growth even at 7,500 lux, the highest level of light intensity in the experiment, Therefore, It is assumed that C. polykrikoides has a physiological capability to adapt and utilize higher irradiance resulting in the higher growth rate without any photo inhibition response at the sea surface where there is usually strong irradiance during its blooming season. Although C. poiykikoides and G. impudicum continued their linear growth with the increase of nitrate ($NO_3^-$) and ammonium ($NH_4^-$) concentrations at less than the $40{\mu}M$, they didn't show any significant differences in growth rates with the increase of nitrate and ammonium concentrations at more than $40{\mu}M$, signifying that the nitrogen critical point for the growth of the two species stands between 13.5 and $40{\mu}M$. Also, even though both of the two species continued their linear growth with the increase of phosphate ($PO_4^{2-}$) concentrations at less than the $4.05{\mu}M$, there were no any significant differences in growth rates with the increase of phosphate concentrations at more than $4.05{\mu}M$, signifying that the phosphate critical point for the growth of the two species stands between 1.35 and $4.05{\mu}M$. On the other hand, C. polykrikoides has made blooms at the oligotrophic environment near Narodo and Namhaedo where the concentration of DIN and DIP are less than 1.2 and $0.3{\mu}M$, respectively. We attributed this phenomenon to its own ecological characteristics of diel vertical migration through which C. polykrikoides could uptake enough nutrients from the deep sea water near bottom during the night time irrespective of the lower nutrient pools in the surface water.

• Progress in Medical Physics
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• v.22 no.3
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• pp.140-147
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• 2011

The purpose of this study was to estimate internal motion using molecular sieve for quantitative improvement of lung tumor and to localize lung tumor in the small animal PET image by evaluated data. Internal motion has been demonstrated in small animal lung region by molecular sieve contained radioactive substance. Molecular sieve for internal lung motion target was contained approximately 37 kBq Cu-64. The small animal PET images were obtained from Siemens Inveon scanner using external trigger system (BioVet). SD-Rat PET images were obtained at 60 min post injection of FDG 37 MBq/0.2 mL via tail vein for 20 min. Each line of response in the list-mode data was converted to sinogram gated frames (2~16 bin) by trigger signal obtained from BioVet. The sinogram data was reconstructed using OSEM 2D with 4 iterations. PET images were evaluated with count, SNR, FWHM from ROI drawn in the target region for quantitative tumor analysis. The size of molecular sieve motion target was $1.59{\times}2.50mm$. The reference motion target FWHM of vertical and horizontal was 2.91 mm and 1.43 mm, respectively. The vertical FWHM of static, 4 bin and 8 bin was 3.90 mm, 3.74 mm, and 3.16 mm, respectively. The horizontal FWHM of static, 4 bin and 8 bin was 2.21 mm, 2.06 mm, and 1.60 mm, respectively. Count of static, 4 bin, 8 bin, 12 bin and 16 bin was 4.10, 4.83, 5.59, 5.38, and 5.31, respectively. The SNR of static, 4 bin, 8 bin, 12 bin and 16 bin was 4.18, 4.05, 4.22, 3.89, and 3.58, respectively. The FWHM were improved in accordance with gate number increase. The count and SNR were not proportionately improve with gate number, but shown the highest value in specific bin number. We measured the optimal gate number what minimize the SNR loss and gain improved count when imaging lung tumor in small animal. The internal motion estimation provide localized tumor image and will be a useful method for organ motion prediction modeling without external motion monitoring system.

• Journal of Animal Science and Technology
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• v.49 no.1
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• pp.137-146
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• 2007

This study was conducted to determine fertilizer nutrient and pollutant production of Holstein dairy cattle by estimating manure characteristics. The moisture content of feces was 83.9% and 95.1% for urine. The pH of feces and urine were in the ranges of 7.0～7.4 and 7.5～7.8, respectively. The average BOD5, COD, SS, T-N, T-P concentrations of the dairy feces were 18,294, 52,765, 102,889, 2,575, and 457mg/ℓ, respectively. Dairy urine showed lower levels of BOD5(5,455mg/ℓ), COD(8,089mg/ℓ), SS(593mg/ℓ), T-N(3,401mg/l), and T-P(13mg/ℓ) than feces. The total daily produced pollutant amounts of a dairy cow were 924.1g(Milking cow), 538.8g(Dry cow), 284.4g(Heifer) of BOD5, 2,336.5g (Milking cow), 1,651.8g(Dry cow), 734.1g(Heifer) of COD and 4,210.1g(Milking cow), 2,417.1g(Dry cow), 1,629.1g(Heifer) of SS and 194.8g(Milking cow), 96.4g(Dry cow), 58.3g(Heifer) of T-N and 24.0g(Milking cow), 10.2g(Dry cow), 6.1g(Heifer) of T-P. The calculated amount of pollutants produced by a 450kg dairy cow for one year were 181.3kg of BOD5, 492.5kg of COD, 899.9kg of SS, 36.0kg of T-N and 4.1kg of T-P. The total yearly estimated pollutant production from all head(497,261) of dairy cattle in Korea is 90,149 tons of BOD5, 244,890 tons of COD, 447,491 tons of SS, 17,898 tons of T-N and 2,008 tons of T-P. The fertilizer nutrient concentrations of dairy feces was 0.26% N, 0.1% P2O5 and 0.14% K2O. Urine was found to contain 0.34% N, 0.003% of P2O5 and 0.31% K2O. The total daily fertilizer nutrients produced by dairy cattle were 197.4g (Milking cow), 97.4g(Dry cow), and 57.9g(Heifer) of Nitrogen, 54.2g(Milking cow), 22.2g(Dry cow), and 14.2g(Heifer) of P2O5 and 110.8g(Milking cow), 80.4g (Dry cow), and 39.5g(Heifer) of K2O. The total yearly estimated fertilizer nutrient produced by a 450kg dairy animal is 36.2kg of N, 8.8kg of P2O5, 24.6kg of K2O. The estimated yearly fertilizer nutrient production from all dairy cattle in Korea is 18,000 tons of N, 4,397 tons of P2O5, 12,206 tons of K2O. Dairy manure contains useful trace minerals for crops, such as CaO and MgO, which are contained in similar levels to commercial compost being sold in the domestic market. Concentrations of harmful trace minerals, such as As, Cd, Hg, Pb, Cr, Cu, Ni, Zn, met the Korea compost standard regulations, with some of these minerals being in undetected amounts.

• Korean Journal of Environmental Agriculture
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• v.14 no.2
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• pp.232-245
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• 1995

The garbage from the dwelling house was composted in two kinds of small composter in the laboratory, and the possibility of garbage composting was examined. The composters were general small. One (type 3) was constructed with the double layer walls and the other (type 4) was the same as the first except for being insulated. Because it was found that type 3 was not available for composting under our meteorological conditions through the winter experiment, only type 4 was tested in spring and summer. The experiment was performed for 8 weeks in each season. The seasonal variation of several components in the compost was evaluated and discussed. The results summarized below were those obtained at the end of the experiment, if the time was not specified. 1) The maximum temperature was $43^{\circ}C$ in winter, $55^{\circ}C$ in spring and $56^{\circ}C$ in summer. 2) The mass was reduced to an average of 63% and the volume reduction was an average of 78%. 3) The density was estimated as 1.5 kg/l in winter and 0.8 kg/l in spring and summer. 4) The water content was not much changed during the composting periods. It was 79.3% in winter, 75.0% in spring and 70.0% in summer. 5) After pH value increased during the first week, it decreased until the second week and increased again continuously thereafter. It reached pH 6.19 in winter, pH 7.59 in spring and pH 8.69 in summer. 6) The faster the organic matter was decomposed, the greater the ash content increased. The contents of cellulose and lignin increased, but that of hemicellulose decreased during the composting period. 7) Nitrogen contents were in the range of 3.3-6.8% and especially high in summer. After ammonium contents increased at the early stage of the composting period, they decreased. The maximum ammonium-nitrogen content was 2,404mg/kg after 8 weeks in winter, 12,400mg/kg after 3 weeks in spring and 20,718mg/kg after 3 weeks in summer. C/N-ratios decreased with the lapse of composting time, but they were not much changed. Nitrification occurred actively in summer. 8) The contents of volatile and higher fatty acids increased at the early stage of composting and reduced after that. The maximum content of total fatty acid was 9.7% after 6 weeks in winter, 14.8% after 6 weeks in spring and 15.8% after 2 weeks in summer. 9) The contents of inorganic components were not accumulated as composting proceeded. They were in the range of 0.9-4.4% $P_2O_5$, 1.6-2.4% $K_2O$, 2.2-5.4% CaO and 0.30-0.61% MgO. 10) CN and heavy metal contents did not show any tendency. They were in the range of 0.21-14.55mg/kg CN, 11-166mg/kg Zn, 5-65mg/kg Cu, 0.5-10.8mg/kg Cd, 6- 35mg/kg Pb, ND-33 mg/kg Cr and ND-302.04 g/kg Hg.

• Journal of Wetlands Research
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• v.17 no.4
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• pp.391-401
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• 2015

In this study, divided into small category groups for the residential area it was carried out monitoring for the runoff during precipitation. Based on the results analyzed according to the nonpoint sources Housing leakage characteristics. Analysis of the rainfall runoff and concentration of each type of exclusive detached house with apartments, in the majority of precipitation types runoff concentrations were higher in early. In the case of a difference of two points per runoff rate rainfall it was largely investigation. The average runoff is estimated loadings of BOD $101.1kg/km^2$, SS $232.2kg/km^2$, T-N $18.2kg/km^2$, T-P $2.0kg/km^2$ detached house case, if the apartment was estimated at point BOD $108.82kg/km^2$, SS $329.18kg/km^2$, T-N $57.67kg/km^2$, T-P $4.21kg/km^2$. The average EMCs is BOD BOD 6.6 mg/L, SS 12.8 mg/L, T-N 1.518 mg/L, T-P 0.099 mg/L detached house case, if the apartment was estimated at point BOD 6.3 mg/L, COD 11.2mg/L, SS 14.5 mg/L, T-N 3.1 mg/L, T-P 0.2 mg/L. The initial 30 percentage calculated based on the initial results, the total flow of 30% if the outflow of detached house showed a net percentage difference to T-P 1.04 > T-N 0.97 > BOD 0.90 > SS 0.80. The apartment area showed the percentage difference in the water quality in the order of BOD 1.49 > T-P 1.40 > SS 1.30 > T-N 0.96 per item.

• Korean Journal of Microbiology
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• v.41 no.1
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• pp.67-73
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• 2005

Photoautotrophic bacteria are promising candidates for the production of poly(3-hydroxybutyrate) (PHB) since they can address the critical problem of substrate costs. In this study, we isolated 25 Tn5-inserted mutants of the Synechocystis sp. PCC 6803 which showed enhanced PHB accumulation compared to the wild-type strain. After 5-days cultivation under nitrogen-limited mixotrophic conditions, the intracellular levels of PHB content in these mutants reached up to $10-30\%$ of dry cell weight (DCW) comparable to $4\%$ of DCW in the wild-type strain. Using the method of inverse PCR, the affected genes of the mutants were mapped on the completely known genome sequence of Synechocystis sp. PCC 6803. As a result, the increased PHB accumulation in 5 mutants were found to be resulted from defects of genes coding for NADH-ubiquinone oxidoreductase, O-succinylbenzoic-CoA ligase, photosystem II PsbT protein or histidine kinase, which are involved in photosystem in thylakoid inner membrane of the cell. The values of $NAD(P)H/NAD(P)^+$ ratio in the cells of these mutants were much higher than that of the wild-type strain as measured by using pulse-amplitude modulated fluorometer, suggesting that PHB synthesis could be enhanced by increasing the level of cellular NAD(P)H which is a limiting substrate for NADPH-dependent acetoacetyl-CoA reductase. From these results, it is likely that NAD(P)H would be a limiting factor for PHB synthesis in Synechocystis sp. PCC 6803.