• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.1
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• pp.1-8
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• 2015

To evaluate accuracy and precision of determination of total alkalinity ($Alk_T$) and carbon dioxide ($TCO_2$) derived from present study, experiment was applied with $CO_2$ CRM (Batch 132, Scripps Institution of Oceanography; $Alk_T=2229.24{\pm}0.39{\mu}mol/kg$, $TCO_2=2032.65{\pm}0.45{\mu}mol/kg$). As the result, average concentration of $Alk_T$ and $TCO_2$ was $2354.09{\mu}mol/kg$ (~5.6% difference with $CO_2$ CRM) and $2089.60{\mu}mol/kg$ (~2.3% difference with $CO_2$ CRM), respectively. For previous method (Gran Titration) by addition $NaHCO_3$ to deionized water($Alk_T$ $2023.33{\mu}mol/kg$), average concentration was $2193.39{\mu}mol/kg$ (sd=57.15, n=7). Whereas, average concentration was $2017.02{\mu}mol/kg$ (sd=10.98, n=7) for the present study. Recovery yield experiments of total alkalinity in deionized water and seawater were implemented by addition of $NaHCO_3$. The recovery yield of deionized water in the range 0 to $4952.39{\mu}mol/kg$ was 100.8% ($R^2$=0.999), and seawater in the range 0 to $2041.32{\mu}mol/kg$ was 102.3% ($R^2$=0.999). Comparison of $pCO_2$ sensor (PSI $CO_2-Pro^{TM}$) with present method showed very meaningful correlation coefficient ($R^2$=0.977) in the range of 427 to $705{\mu}atm$ and 9.16 to $15.24{\mu}mol/kg$ throught elapsed time for two weeks. Field experiment of diurnal variation of total carbon dioxide was accomplished at Sachon harbor in the coastal waters of East Sea of Korea. Concentration of $Alk_T$ and $TCO_2$ was increased during night, and decreased during daylight hours. The results showed mirror type between $TCO_2$ and dissolved oxygen, which was attributable to photosynthesis and respiration of phytoplankton. Also, open ocean field study was performed to obtain vertical profile of $Alk_T$ and $TCO_2$ in C-C zone (Clarion-Clipperton Fracture Zone), Northeastern Pacific. Average concentrations of $Alk_T$ in the surface mixed layer (0~60 m) and deeper layer below 200 m were $2422.38{\mu}mol/kg$ (sd=78.73, n=20) and $2465.87{\mu}mol/kg$ (sd=57.68, n=103), respectively. And average concentrations of $TCO_2$ were $2134.47{\mu}mol/kg$ (sd=65.4, n=20) and $2431.87{\mu}mol/kg$ (sd=65.02, n=103) in the same depth ranges such as $Alk_T$. Vertical distributions of $Alk_T$ and $TCO_2$ concentrations tended to increase with depth, and analyzed concentrations showed slightly higher than those of previous studies in this area.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.645-658
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• 2004

To investigate the effects of conjugated linoleic acid added diet feeding on CLA accumulation and quality characteristics of manufactured press ham using CLA accwnulated pork loin meat. The CLA used to add in diet was chemically synthesized by alkaline isomerization method with com oil. Pigs were divided into 5 treatment groups(4 pigs/group) and subjected to one of five treatment diets(0, 1.25% CLA for 2weeks, 2.5% CLA for 2weeks, 1.25% CLA for 4weeks and 2.5% CLA for 4weeks, CLA diets; total fed diets) before slaughter. Pork loin were collected from the animals(110kg body weight) slaughtering at the commercial slaughter house. Manufacture press ham using CLA accumulated pork loin meat were vacuum packaged and then stored during 1, 7, 14, 21 and 28 days at 4$^{\circ}C$. Samples were analyzed for general compositions, physico-chemical properties(pH, color, shear force value), TBARS. pH value of CLA treatment(T4) was increased significantly than that of oontrol(P<0.05). pH of control and CLA treatments were increased significantly as the storage period passed(P< 0.05). Crude fat content of CLA treatment groups was significantly higher than the control pork (P<0.05). Meat color(CIE $L^*$, $a^*$$b^*$

• Journal of the Korean Vacuum Society
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• v.15 no.6
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• pp.563-575
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• 2006

Ti(C,N) films are synthesized by pulsed DC plasma enhanced chemical vapor deposition (PEMOCVD) using metal-organic compounds of tetrakis diethylamide titanium at $200-300^{\circ}C$. To compare plasma parameter, in this study, $H_2$ and $He/H_2$ gases are used as carrier gas. The effect of $N_2\;and\;NH_3$ gases as reactive gas is also evaluated in reduction of C content of the films. Radical formation and ionization behaviors in plasma are analyzed in-situ by optical emission spectroscopy (OES) at various pulsed bias voltages and gas species. He and $H_2$ mixture is very effective in enhancing ionization of radicals, especially for the $N_2$. Ammonia $(NH_3)$ gas also highly reduces the formation of CN radical, thereby decreasing C content of Ti(C, N) films in a great deal. The microhardness of film is obtained to be $1,250\;Hk_{0.01}\;to\;1,760\;Hk_{0.01}$ depending on gas species and bias voltage. Higher hardness can be obtained under the conditions of $H_2\;and\;N_2$ gases as well as bias voltage of 600 V. Hf(C, N) films were also obtained by pulsed DC PEMOCYB from tetrakis diethyl-amide hafnium and $N_2/He-H_2$ mixture. The depositions were carried out at temperature of below $300^{\circ}C$, total chamber pressure of 1 Torr and varying the deposition parameters. Influences of the nitrogen contents in the plasma decreased the growth rate and attributed to amorphous components, to the high carbon content of the film. In XRD analysis the domain lattice plain was (111) direction and the maximum microhardness was observed to be $2,460\;Hk_{0.025}$ for a Hf(C,N) film grown under -600 V and 0.1 flow rate of nitrogen. The optical emission spectra measured during PEMOCVD processes of Hf(C, N) film growth were also discussed. $N_2,\;N_2^+$, H, He, CH, CN radicals and metal species(Hf) were detected and CH, CN radicals that make an important role of total PEMOCVD process increased carbon content.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.215-235
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• 1996

The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad ＆ fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH＋7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad ＆ fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).

• Magazine of the Korean Society of Agricultural Engineers
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• v.11 no.1
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• pp.1534-1548
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• 1969

This experiment was conducted, making use of the 'NONG-RIM6' arecommended variety of rice for the year of 1968. Main purposes of the experiment are to explore possibilities of; a) ways and means of saving irringation water and, b) overcoming drought at the same time so that an increased yield in rice could be resulted in. Specifically, it was tried to determine the effects of the Rotation irrigation method combined with differentiated thickness of lining upon the growth and yield of rice. Some of the major findings are summarized in the following. 1) The different thicknesses show a significant relationship with the weight of 1,000 grains. In the case of 9cm lined plot, the grain weight is 23.5grams, the heaviest. Next in order is 3cm lined plot, 6cm lined plot, control plot, and wheat straw lined-plot. 2) In rice yield, it is found that there is a considerably moderate significant relationship with both the different thickness of lining and the number of irrigation, as shown in the table. 3) There is little or no difference among different plots in terms of a) physical and chemical properties of soil, b) quality of irrigation water, c) climatic conditions, and rainfalls. 4) It is found that there is a significant relationship between differences in the method of rotation irrigation and the number of ears per hill. The plot irrigated at an interval of 7 days shows 17.4 ears and plot irrigated at an interval of 6 days, 16.3 5) In vinyl-treated plots, it is shown that both yield and component elements are greatest in the case of the plot ith whole of $3cm/m^2$ Next in order are the plot with a hole of $2cm/m^2$ the plot with a hole of $1cm/m^2$ In the case of the plot with no hole it is found that both yield and component elements are decreased as compared to the control plot. 6) The irrigation water reqirement is measured for the actual irrigation days of 72 which are the number subtracted the days of rainfall of 30 from the total irrigation days of 102. It is found that the irrigation water requirement for the uncontrol plot is 1,590mm as compared to 876mm(44.9% saved) for the 9cm-lined plot, 959mm(39.7% saved) for the 6cm-lined plot 1,010mm(36% saved) for the 3cm-lined plot and 1,082mm(32% saved) for the wheat straw lined plot. In the case of the Rotation irrigation method it is found that the water requirement for the plot irrigated at an interval of 8 days is 538mm(65% saved), as compared to 617mm(61.6% saved) for plot irrigated at an interval of 7 day 672mm(57.7% saved) for plot irrigated at an interval of 6day, 746mm(53.0% saved) for the plot irrigated at an interval of 5 days, 890mm 44.0% saved) for the plot irrigated at an interval of 4 days, and 975mm(38.6% saved) for the plot irrigated at an interval of 3 days. 7) The rate of evapotranspiration is found 2.8 around the end of month of July, as compared to 2.6 at the begining of August 3.4 around the end of August and 2.6 at the begining of August 3.4 around the end of August and 2.6 at the begining of September. 8) It is found that the saturation quantity of 30mm per day is decreased to 20mm per day though the use of vinyl covering. 9) The husking rate shows 75 per cent which is considered better.