• Cartoon and Animation Studies
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• s.43
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• pp.321-342
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• 2016

The "uncanny valley" curve describes the measured results of the negative emotion response which depends on the similarity between the artificially created character and the real human shape. The "uncanny valley" effect that usually appears in the animation character design induces negative response such as fear and hatred feeling, and anxiety, which is not expected by designers. Especially, in the case of the commercial animation which mostly reply on public response, this kind of negative response is directly related to the failure of artificially created character. Accordingly, designers adjust the desirability of the character design by avoiding or utilizing the "uncanny valley" effect, inducing certain character effect that leads to the success in animation work. This manuscript confirmed the "uncanny valley" coefficient of the positive emotion character design which was based on the actual character design and animation analysis. The "uncanny valley" concept was firstly introduced by a medical scientist Ernst Jentsch in 1906. After then, a psychologist Freud applied this concept to psychological phenomenon in 1919 and a Japanese robert expert Professor Masahiro Mori presented the "uncanny valley" theory on the view of the recognition effect. This paper interpreted the "uncanny valley" effect based on these research theory outcomes in two aspects including sensation production and emotion expression. The mickey-mouse character design analysis confirmed the existence basis of the "uncanny valley" effect, which presented how mickey-mouse human shape image imposed the "uncanny valley" effect on audience. The animation work analysis investigated the reason why the produced 3D animation character should not be 100% similar to the real human by comparing the animation baby character produced by Pix company as the experimental subject to the data of the real baby with the same age. Therefore, the examples of avoiding or utilizing the "uncanny valley" effect in animation character design was discussed in detail and the four stages of sensation production and emotional change of audience due to this kind of effect was figured out. This research result can be used as an important reference in deciding the desirability of the animation character.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.139-148
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• 2020

Infectious diseases have long plagued mankind, and predicting and preventing them has been a big challenge for mankind. For this reasen, various studies have been conducted so far to predict infectious diseases. Most of the early studies relied on epidemiological data from the Centers for Disease Control and Prevention (CDC), and the problem was that the data provided by the CDC was updated only once a week, making it difficult to predict the number of real-time disease outbreaks. However, with the emergence of various Internet media due to the recent development of IT technology, studies have been conducted to predict the occurrence of infectious diseases through web data, and most of the studies we have researched have been using single Web data to predict diseases. However, disease forecasting through a single Web data has the disadvantage of having difficulty collecting large amounts of learning data and making accurate predictions through models for recent outbreaks such as "COVID-19". Thus, we would like to demonstrate through experiments that models that use multiple Web data to predict the occurrence of infectious diseases through LSTM models are more accurate than those that use single Web data and suggest models suitable for predicting infectious diseases. In this experiment, we predicted the occurrence of "Malaria" and "Epidemic-parotitis" using a single web data model and the model we propose. A total of 104 weeks of NEWS, SNS, and search query data were collected, of which 75 weeks were used as learning data and 29 weeks were used as verification data. In the experiment we predicted verification data using our proposed model and single web data, Pearson correlation coefficient for the predicted results of our proposed model showed the highest similarity at 0.94, 0.86, and RMSE was also the lowest at 0.19, 0.07.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.181-199
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• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Korean Journal of Weed Science
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• v.31 no.1
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• pp.41-48
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• 2011

This survey of weed population by different soil moisture with paddy-upland rotation was conducted to investigate information on weed flora and its ecology between two years. Weed species were assessed in April : 2009, 2010. Soil samples were taken from each study plot to assess the impact soil moisture on the occurrence and abundance of dominance weed species. Soil water of poorly drained field ranged from 10.2~18.2% more than 11.8~14.3% of somewhat poorly drained field. Weeds of fields composed of 19 species belonging to 12 families. Among 12 families, 6 weed species belonged to Compositae (31.6%) were the biggest family, Cruciferae were 2 species (10.5%), and Rubiaceae were 2 species (10.5%), respectively. Youngia japonica had the highest summed dominance ratio(SDR) (97.0%) and followed by Conyza canadensis (92.1%), Galium spurium (35.3%) and Hemistepta lyrata (28.4%) of somewhat poorly drainage in 2009. Artemisia princeps the highest SDR (100%) and followed by Stellaria alsine(55.2%), Y. japonica (38.3%) and Nasturtium officinale (28.5%) of poorly drainage in 2009. And, Stellaria alsine had the highest SDR (86.8%) and followed by Alopecurus aequalis (77.7%), Astragalus sinicus (68.7%) and Y. japonica (46.3%) of somewhat poorly drainage in 2010. S. alsine the highest SDR (93.7%) and followed by A. aequalis (78.6%), Nasturtium officinale (31.3%) and Y. japonica (30.4%) of poorly drainage in 2010. Simpson's index was calculated to 0.12~0.23, which showed that weed occurrence with different soil moisture in paddy-upland rotation and between years was various. Similarity coefficient between years was 43.0% (2009) and 74.2% (2010), which indicate a low diversity because of the moisture in the agro-ecosystem.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.99-108
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• 2021

Purpose: The goal of this study is to evaluate usefulness of noninvasive method instead of previous inserting Fiducial Marker Method when performing Stereotactic Partial Breast Irradiation in CyberKnife. Material and methods: For consistency of Imaging Center, we evaluated both oblique images at angle 45 and 315 acquired from 2D Simulator and CyberKnife quantitatively through dice similarity coefficient. Also, location reproducibility of Surface Fiducial Marker was analyzed from 2D Simulator, treatment plans and CyberKinfe images by using 8 Fiducial Markers made of gold attached to ATOM Phantom based on our institution's protocols. Results: The results of the estimated consistency were 0.87 and 0.9 at the oblique angle 45 and 315, respectively. For location consistency of Surface Fiducial Markers, values of horizontal vertical direction of left breast were Superior/Inferior 0.3 mm, Left/Right -0.3 mm, Anterior/Posterior 0.4 mm, and the values of rotational direction were Roll 0.3 °, Pitch 0.2 °, Yaw 0.4 °. The values of horizontal vertical direction of right breast were Superior/Inferior -0.1 mm, Left/Right -0.1 mm, Anterior/Posterior -0.1 mm, and the values of rotational direction were Roll 0.2°, Pitch 0.1°, Yaw 0.1°. Conclusions: We expect that the protocols used by Surface Fiducial Markers when performing Stereotactic Partial Breast Irradiation in CyberKnife will provide protection from pain and cut expenses for treatment and reduce treatment errors and make treatment more accurate by suggesting treatment protocols based on high consistency of Imaging Center and reproducibility of Fiducial Markers.

• Journal of Korean Society of Forest Science
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• v.70 no.1
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• pp.7-16
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• 1985

To grasp canonical correlations, their related backgrounds in various growth factors of stem, the characteristics of stem by synthetical dispersion analysis, principal component analysis and canonical correlation analysis as optimum method were applied to Larix leptolepis. The results are as follows; 1) There were high or low correlation among all factors (height ($x_1$), clear height ($x_2$), form height ($x_3$), breast height diameter (D. B. H.: $x_4$), mid diameter ($x_5$), crown diameter ($x_6$) and stem volume ($x_7$)) except normal form factor ($x_8$). Especially stem volume showed high correlation with the D.B.H., height, mid diameter (cf. table 1). 3) (1) Canonical correlation coefficients and canonical variate between stem volume and composite variate of various height growth factors ($x_1$, $x_2$ and $x_3$) are ${\gamma}_{u1,v1}=0.82980^{**}$, $\{u_1=1.00000x_7\\v_1=1.08323x_1-0.04299x_2-0.07080x_3$. (2) Those of stem volume and composite variate of various diameter growth factors ($x_4$, $x_5$ and $x_6$) are ${\gamma}_{u1,v1}=0.98198^{**}$, $\{{u_1=1.00000x_7\\v_1=0.86433x_4+0.11996x_5+0.02917x_6$. (3) And canonical correlation between stem volume and composite variate of six factors including various heights and diameters are ${\gamma}_{u1,v1}=0.98700^{**}$, $\{^u_1=1.00000x_7\\v1=0.12948x_1+0.00291x_2+0.03076x_3+0.76707x_4+0.09107x_5+0.02576x_6$. All the cases showed the high canonical correlation. Height in the case of (1), D.B.H. in that of (2), and the D.B.H, and height in that of (3) respectively make an absolute contribution to the canonical correlation. Synthetical characteristics of each qualitative growth are largely affected by each factor. Especially in the case of (3) the influence by the D.B.H. is the most significant in the above six factors (cf. table 2). 3) Canonical correlation coefficient and canonical variate between composite variate of various height growth factors and that of the various diameter factors are ${\gamma}_{u1,v1}=0.78556^{**}$, $\{u_1=1.20569x_1-0.04444x_2-0.21696x_3\\v_1=1.09571x_4-0.14076x_5+0.05285x_6$. As shown in the above facts, only height and D.B.H. affected considerably to the canonical correlation. Thus, it was revealed that the synthetical characteristics of height growth was determined by height and those of the growth in thickness by D.B.H., respectively (cf. table 2). 4) Synthetical characteristics (1st-3rd principal component) derived from eight growth factors of stem, on the basis of 85% accumulated proportion aimed, are as follows; Ist principal component ($z_1$): $Z_1=0.40192x_1+0.23693x_2+0.37047x_3+0.41745x_4+0.41629x_5+0.33454x_60.42798x_7+0.04923x_8$, 2nd principal component ($z_2$): $z_2=-0.09306x_1-0.34707x_2+0.08372x_3-0.03239x_4+0.11152x_5+0.00012x_6+0.02407x_7+0.92185x_8$, 3rd principal component ($z_3$): $Z_3=0.19832x_1+0.68210x_2+0.35824x_3-0.22522x_4-0.20876x_5-0.42373x_6-0.15055x_7+0.26562x_8$. The first principal component ($z_1$) as a "size factor" showed the high information absorption power with 63.26% (proportion), and its principal component score is determined by stem volume, D.B.H., mid diameter and height, which have considerably high factor loading. The second principal component ($z_2$) is the "shape factor" which indicates cubic similarity of the stem and its score is formed under the absolute influence of normal form factor. The third principal component ($z_3$) is the "shape factor" which shows the degree of thickness and length of stem. These three principal components have the satisfactory information absorption power with 88.36% of the accumulated percentage. variance (cf. table 3). 5) Thus the principal component and canonical correlation analyses could be applied to the field of forest measurement, judgement of site qualities, management diagnoses for the forest management and the forest products industries, and the other fields which require the assessment of synthetical characteristics.