• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.5 no.1
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• pp.1-35
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• 1969

Attempts were made to obtain the fundamental knowledge on the quantitative constitution status of leaves and stem of elongating node-part, and the relationships between these morphological characteristics along with the nitrogen contents of leaves and grain yield were examined varing application amounts of nitrogen in rice plant. I. The agronomic characteristics of leaves and nodes of elongation node-part (4-node parts from the top of stem) were observed at heading stage with 20 leading rice varieties of Kang Won district. The results are summarized as follows: 1. Leaf area magnitude of the flag and the fourth leaf was smaller than that of the second and the third with the average value of flag leaf 18.61 $cm^2$, the second leaf 21.84 $cm^2$, the third 21.52 $cm^2$ and the fourth 18.56 $cm^2$. The weight of leaf blade showed an isotonic tendency with the magnitude of leaf area with the value of the flag leaf 97.0 mg, the second leaf 117.1 mg, the third 115.4 mg, and the fourth 95.3 mg. The weight of each leaf sheath was remarkably larger at the higher node-part than at the lower node-part of the stem with the value of flag leaf sheath 176.3 mg, the second 163.7 mg, the third 163.4 mg and the fourth 123.9 mg. Accordingly, the total leaf weight of each part was larger at the second and the third leaf than at the first and the fourth. Total plant weight of each part (weight of leaf blade, leaf sheath, and culm) also was larger at the middle node-part. 2. Coefficients of variation for the varietal differences of the morphological characteristics of elongating node-part were 12.75% for the leaf area, 15.29% for the weight of leaf blade, 15.90%, for the weight of leaf sheath, 11.42% for the weight of internode, 15.45% for the leaf weight (leaf blade ＆ leaf sheath) and 13.24% for the straw weight. And these coefficient values of the most characteristics were, on the whole, smaller at the second and the third node-part than at the first and the fourth node-part, but the coefficient value of the internode weight was rather small at the third and fourth node-part. 3. Constitutional ratio of each plant organ to the total plant weight in term of dry matter weight (excluding head and root wight) was 39.2% for the leaf sheath, 34.2% for the culm, 26.6% for the leaf blade. And ocnstitutional ratio of leaf sheath in term of dry matter weight was larger at the higher position in contrast with that of culm. 4. Average weight ration of leaf blade to culm, leaf sheath to culm, leaf blades to sheath and the leaf blades to culm plus leaf sheath were 77.7 %, 114.5%, 67.9% and 36.2%, respectively. With regard to the position of the plant organ, the weight ratio of leaf blade to culm and that of leaf sheath to culm were larger at higher part in contrast with that of leaf blade to leaf sheath. 5. Generally, there founded deep relationships between grain yield and each morphological characteristics of plant organ of elongating node-part as follows; Correlation coefficient between total area of 4 leaves (from flag to the fourth leaf) and grain yield was ${\gamma}$=0.666$^{**}$ In regard to the position of leaves, correlation coefficient values of flag, the second, the third and the fourth leaf were ${\gamma}$=0.659$^{**}$, ${\gamma}$=0.609$^{**}$, ${\gamma}$=0.464$^{*}$ and ${\gamma}$=0.523$^{*}$, respectively. Correlation coefficient between total weight of leaf blades and the grain yield was ${\gamma}$=0.678$^{**}$. In regard to the position of leaves, that of flag leaf was ${\gamma}$=0.691$^{**}$, and ${\gamma}$=0.654$^{**}$ for the second leaf, ${\gamma}$=0.570$^{**}$ for the third, and ${\gamma}$=0.544$^{**}$ for the fourth. Correlation between the weight of leaves (blade weight plus sheath weight) and the grain yield showed similar values. In the relationship between plant weight and grain yield there also was significant correlation, but with highly significant value only for the first node-part. There appeared correlation between total weight of leaf sheath and grain yield with the value of ${\gamma}$=0.572$^{**}$ and in regard to the position of each leaf sheath the values were ${\gamma}$=0.623$^{**}$ for the flag leaf, ${\gamma}$=0.486$^{**}$ for the second leaf, ${\gamma}$=0.513$^{**}$ for the third, ${\gamma}$=0.450$^{**}$ for the fourth. However, there was no significant correlation between culm weight and grain yield. 6. With respect to in gain yield, varietal differences in magnitude of leaf area, weight of leaf blade, leaf weight per unit area, weight of leaf sheath, culm weight, total leaf and stem weight were larger in the case of high yielding varieties and decreased in accordance with decreasing yield. And this tendency also was shown in the varietal differences of magnitude of each part. Variation in magnitude of each part for the leaf area, weight of leaf blade, culm weight was significantly small in high yielding varieties compared to low yielding varieties. 7. Plant constitutional ratio of each organ of the elongating node-part in term of weight magnitnde varied to som extent according to varieties indicating leaf blade 27.6%, leaf sheath 39.5%, culm 32.9% in the case of high yielding varieties, leaf blade 25.5%, leaf sheath 38.1%, culm 36.4% in the case of low yielding varieties, and medium yielding varieties showed intermadiate values. 8. Far higher values of the weight ration of leaf blade to culm and leaf sheath to culm were given to the high yielding varieties compared to low yielding varieties. And medium yielding varieties showed intermadiate values. II. Effects of application rate of nitrogen on the morphological characteristics of the elongating node-part, nitrogen content of leaf blade, and their relation with the grain yield of the rice were observed with 3 rice varieties; Shin No.2, Shirogane, and Jinheung varying application amounts of nitrogen as 8kg, 12kg and 16kg per 10 are. 1. As for the variation of morphological magnitude s affected by the amounts of nitrogen application, total leaf area (4 leaves from the flag leaf) increased to 16.5% at 12kg N plot, and about 30% at 16kg N polt compared to 8kg N plot and total weight of leaf blade also increased to similar extent, respectively, in contrast with weight of leaf sheath increasing 4.9% and 7.8%, respectively. However, the weight of culm decreased to 1.5% and 11.2%at the 12kg N plot and 16kg N plot, respectively, and these decreasing rate was noted at the nodes of lower part. 2. As for the verietal differences in variation of morphological magnitude as affected by the amount of nitrogen fertilization, leaf area coefficient value of variation of the total leaf area was 15.40% for Shin No. 2, 12.87% for Shirogane, and 10.99% for Jinheung. With respect to the position of nodes, the largest variation of leaf blade magnitude was observed at the fourth for Shin No. 2, the second for Shirogan, and flag leaf for Jinheung. And there also was an isotonic varietal difference in the weight of leaf blade. Variation in total culm weight showed varietal differences with the coefficient value of 7.72% for Shin No.2, 12.11% for Shirogane, and 0.94% for Jinheung. There also was varietal differences in the variation according to the position of nodes. 3. Variation of each elongating node-part related to the fertilization amount decreased with the increase of fertilization amount in the items of leaf area, weight of leaf sheath, culm weight, but weight of leaf sheath varied more at heavier fertilization than at others. 4. Constitutional ratio of each organ excluding head also varied with fertilization amount; constitutional ratio of leaf blade increased much with the increasing amount of fertilization in contrast with the response of culm eight. However, constitutional ration of the weight of leaf sheath was not much affected. 5. Lower value of the ration of leaf blade to culm was given to the 8kg N per 10 are plot, and the ratio of leaf blade to leaf sheath decreased with the increasing amount of fertilization in contrast with the increase in the ratio of leaf sheath to culm. however, the ration of leaf blade to culm plus leaf sheath decreased. 6. With the increase of nitrogen fertilization, leaf area, weight of leaf blade and leaf sheath increased. Accordingly, grin yield also increased to some extent. It was noted that culm weight was changed inversely to the changes in grain yield, but the degree of this variation varied with varietal characteristics. 7. Nitrogen content of leaves at heading and fruiting stage varied with the fertilization amount, and average nitrogen content of leaves of the varieties used 2.19%, 2.49% and 2.74% at the plot of 8kg N, and 12kg N and 16kg N per 10 are, respectively, at heading time, and 0.80%, 0.92% and 1.03% at each plot at fruiting stage. Thus, nitrogen content of leaves increased much with the increasing amount of fertilization, and higher value was given to the leaves on the higher position of elongating node-part. 8. There also was variation of nitrogen content of leaves in accordance with the varieties. However higher grain yield was obtained from the plants retaining higher nitrogen content in leaves at heading or fruiting stage.