• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• The Korean Journal of Petroleum Geology
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• v.14 no.1
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• pp.1-11
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• 2008

As conventional oil and gas reservoirs become depleted, interests for oil sands has rapidly increased in the last decade. Oil sands are mixture of bitumen, water, and host sediments of sand and clay. Most oil sand is unconsolidated sand that is held together by bitumen. Bitumen has hydrocarbon in situ viscosity of >10,000 centipoises (cP) at reservoir condition and has API gravity between $8-14^{\circ}$. The largest oil sand deposits are in Alberta and Saskatchewan, Canada. The reverves are approximated at 1.7 trillion barrels of initial oil-in-place and 173 billion barrels of remaining established reserves. Alberta has a number of oil sands deposits which are grouped into three oil sand development areas - the Athabasca, Cold Lake, and Peace River, with the largest current bitumen production from Athabasca. Principal oil sands deposits consist of the McMurray Fm and Wabiskaw Mbr in Athabasca area, the Gething and Bluesky formations in Peace River area, and relatively thin multi-reservoir deposits of McMurray, Clearwater, and Grand Rapid formations in Cold Lake area. The reservoir sediments were deposited in the foreland basin (Western Canada Sedimentary Basin) formed by collision between the Pacific and North America plates and the subsequent thrusting movements in the Mesozoic. The deposits are underlain by basement rocks of Paleozoic carbonates with highly variable topography. The oil sands deposits were formed during the Early Cretaceous transgression which occurred along the Cretaceous Interior Seaway in North America. The oil-sands-hosting McMurray and Wabiskaw deposits in the Athabasca area consist of the lower fluvial and the upper estuarine-offshore sediments, reflecting the broad and overall transgression. The deposits are characterized by facies heterogeneity of channelized reservoir sands and non-reservoir muds. Main reservoir bodies of the McMurray Formation are fluvial and estuarine channel-point bar complexes which are interbedded with fine-grained deposits formed in floodplain, tidal flat, and estuarine bay. The Wabiskaw deposits (basal member of the Clearwater Formation) commonly comprise sheet-shaped offshore muds and sands, but occasionally show deep-incision into the McMurray deposits, forming channelized reservoir sand bodies of oil sands. In Canada, bitumen of oil sands deposits is produced by surface mining or in-situ thermal recovery processes. Bitumen sands recovered by surface mining are changed into synthetic crude oil through extraction and upgrading processes. On the other hand, bitumen produced by in-situ thermal recovery is transported to refinery only through bitumen blending process. The in-situ thermal recovery technology is represented by Steam-Assisted Gravity Drainage and Cyclic Steam Stimulation. These technologies are based on steam injection into bitumen sand reservoirs for increase in reservoir in-situ temperature and in bitumen mobility. In oil sands reservoirs, efficiency for steam propagation is controlled mainly by reservoir geology. Accordingly, understanding of geological factors and characteristics of oil sands reservoir deposits is prerequisite for well-designed development planning and effective bitumen production. As significant geological factors and characteristics in oil sands reservoir deposits, this study suggests (1) pay of bitumen sands and connectivity, (2) bitumen content and saturation, (3) geologic structure, (4) distribution of mud baffles and plugs, (5) thickness and lateral continuity of mud interbeds, (6) distribution of water-saturated sands, (7) distribution of gas-saturated sands, (8) direction of lateral accretion of point bar, (9) distribution of diagenetic layers and nodules, and (10) texture and fabric change within reservoir sand body.

• Journal of Korean Society of Forest Science
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• v.21 no.1
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• pp.1-34
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• 1974

The author intended to investigate external and internal changes in the cone structure, changes in water content, sugar, fat and protein during the period of seed maturation which bears a proper germinability. The experimental results can be summarized as in the following. 1. Male flowers 1) Pollen-mother cells occur as a mass from late in April to early in May, and form pollen tetrads through meiosis early and middle of May. Pollen with simple nucleus reach maturity late in May. 2) Stamen number of a male flower is almost same as the scale number of cone and is 69-102 stamens. One stamen includes 5800-7300 pollen. 3) The shape is round and elliptical, both of a pollen has air-sac with $80-91{\mu}$ in length, and has cuticlar exine and cellulose intine. 4) Pollen germinate in 68 hours at $25^{\circ}C$ with distilled water of pH 6.0, 2% sugar and 0.8% agar. 2. Female flowers 1) Ovuliferous scales grow rapidly in late April, and differentiation of ovules begins early in May. Embryo-sac-mother cells produce pollen tetrads through meiosis in the middle of May, and flower in late May. 2) The pollinated female flowers show repeated divisions of embryo-sac nucleus, and a great number of free nuclei form a mass for overwintering. Morphogenesis of isolation in the mass structure takes place from the middle of March, and that forms albuminous bodies of aivealus in early May. 3. Formation of pollinators and embryos. 1) Archegonia produce archegonial initial cells in the middle and late April, and pollinators are produced in the late April and late in early May. 2) After pollination, Oespore nuclei are seen to divide in the late May forming a layer of suspensor from the diaphragm in early June and in the middle of June. Thus this happens to show 4 pro-embryos. The organ of embryos begins to differentiate 1 pro-embryo and reachs perfect maturation in late August. 4. The growth of cones 1) In the year of flowering, strobiles grow during the period from the middle of June to the middle of July, and do not grow after the middle of August. Strobiles grow 1.6 times more in length 3.3 times short in diameter and about 22 times more weight than those of female flower in the year of flowering. 2) The cones at the adult stage grow 7 times longer in diameter, 12-15 times shorter diameter than those of strobiles after flowering. 3) Cone has 96-133 scales with the ratio of scale to be 69-80% and the length of cone is 11-13cm. Diameter is 5-8cm with 160-190g weight, and the seed number of it is 90-150 having empty seed ratio of 8-15%. 5. Formation of seed-coats 1) The layers of outer seed-coat become most for the width of $703{\mu}$ in the middle of July. At the adult stage of seed, it becomes $550-580{\mu}$ in size by decreasing moisture content. Then a horny and the cortical tissue of outer coats become differentiated. 2) The outer seed-coat of mature seeds forms epidermal cells of 3-4 layers and the stone cells of 16-21 layers. The interior part of it becomes parenchyma layer of 1 or 2 rows. 3) Inner seed-coat is formed 2 months earlier than the outer seed-coat in the middle of May, having the most width of inner seed-coat $667{\mu}$. At the adult stage it loses to $80-90{\mu}$. 6. Change in moisture content After pollination moisture content becomes gradually increased at the top in the early June and becomes markedly decreased in the middle of August. At the adult stage it shows 43~48% in cone, 23~25% in the outer seed-coat, 32~37% in the inner seed-coat, 23~26% in the inner seed-coat and endosperm and embryo, 21~24% in the embryo and endosperm, 36~40% in the embryos. 7. The content compositions of seed 1) Fat contents become gradually increased after the early May, at the adult stage it occupies 65~85% more fat than walnut and palm. Embryo includes 78.8% fat, and 57.0% fat in endosperm. 2) Sugar content after pollination becomes greatly increased as in the case of reducing sugar, while non-reducing sugar becomes increased in the early June. 3) Crude protein content becomes gradually increased after the early May, and at the adult stage it becomes 48.8%. Endosperm is made up with more protein than embryo. 8. The test of germination The collected optimum period of Pinus koraiensis seeds at an adequate maturity was collected in the early September, and used for the germination test of reduction-method and embryo culture. Seeds were taken at the interval of 7 days from the middle of July to the middle of September for the germination test at germination apparatus.