• The Journal of Korean Academy of Prosthodontics
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• v.47 no.4
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• pp.365-375
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• 2009

Statement of problems: Stress analysis on implant components of the combined screw- and cement-retained implant prosthesis has not investigated yet. Purpose: The purpose of this study was to assess the load distribution characteristics of implant prostheses with the different prosthodontic retention types, such as cement-type, screw-type and combined type by using 3-dimensional finite element analysis. Material and methods: A 3-dimensional finite element model was created in which two SS II implants (Osstem Co. Ltd.) were placed in the areas of the first premolar and the first molar in the mandible, and three-unit fixed partial dentures with four different retention types were fabricated on the two SS II implants. Model 1 was a cement-retained implant restoration made on two cement-retained type abutments (Comocta abutment; Osstem Co. Ltd.), and Model 2 was a screw-retained implant restoration made on the screw-retained type abutments (Octa abutment; Osstem Co. Ltd.). Model 3 was a combined type implant restoration made on the cement-retained type abutment (Comocta abutment) for the first molar and the screw-retained type abutment (Octa abutment) for the first premolar. Lastly, Model 4 was a combined type implant restoration made on the screw-retained type abutment (Octa abutment) for the first molar and the cement-retained type abutment (Comocta abutment) for the first premolar. Average masticatory force was applied on the central fossa in a vertical direction, and on the buccal cusp in a vertical and oblique direction for each model. Von-Mises stress patterns on alveolar bone, implant body, abutment, abutment screw, and prosthetic screw around implant prostheses were evaluated through 3-dimensional finite element analysis. Results: Model 2 showed the lowest von Mises stress. In all models, the von Mises stress distribution of cortical bone, cancellous bone and implant body showed the similar pattern. Regardless of loading conditions and type of abutment system, the stress of bone was concentrated on the cortical bone. The von-Mises stress on abutment, abutment screw, and prosthetic screw showed the lower values for the screw-retained type abutment than for the cement-retained type abutment regardless of the model type. There was little reciprocal effect of the abutment system between the molar and the premolar position. For all models, buccal cusp oblique loading caused the largest stress, followed by buccal cusp vertical loading and center vertical loading. Conclusion: Within the limitation of the FEA study, the combined type implant prosthesis did not demonstrate more stress around implant components than the cement type implant prosthesis. Under the assumption of ideal passive fit, the screw-type implant prosthesis showed the east stress around implant components.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• The Journal of Korean Obstetrics and Gynecology
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• v.30 no.4
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• pp.18-44
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• 2017

Purpose: The object of this study was to observe the anti-climacterium activity of Gagamguibiondam-tang (GGOT) on ovariectomized (OVX) rats, a well-documented rodent models resembles with women postmenopausal climacterium symptoms, as including cardiovascular diseases, obesity, hyperlipidemia, osteoporosis, organ steatosis and mental disorders. Methods: In this study, anti-climacteric effects were evaluated separated into three categories; 1) anti-obese, 2) anti-uterine atrophy and 3) anti-osteoporotic effects. Five groups were used (8 rats in each group); sham control, OVX control, GGOT 500, 250 and 125 mg/kg administered groups. Twenty-eight days after bilateral OVX surgery, GGOT were orally administered, once a day for 84 days, and then the changes on the body weight and gain during experimental periods, serum estradiol levels, abdominal fat pad and uterus weights with histopathology of abdominal fat pads (total thickness and mean adipocyte diameters) and uterus (total, epithelial and mucosal thickness, percentages of uterine gland regions) for anti-obese and estrogenic effects. In addition, femur, tibia and fourth or fifth lumbar vertebrae (L4 or L5) wet, dry and ash weights, mineral density (BMD), bone strength (failure load), serum osteocalcin and bone specific alkaline phosphatase (bALP) contents, histological and histomorphometrical analyses - bone mass and structure with bone resorption, were monitored for anti-osteoporosis activity. Results: As a result of OVX, noticeable increases of body weight and gains, food and water consumption, weights of abdominal fat pad deposited in dorsal abdominal cavity, serum osteocalcin levels were demonstrated in this experiment with decrease of uterus, femur, tibia and L5 weights, serum bALP and estradiol levels. In addition, marked hypertrophic changes of adipocytes located in deposited abdominal fat pads, uterine disused atrophic changes, decreases of bone mass and structures of femur, tibia and L4 were also observed in OVX control rats with dramatic increases of bone resorption markers, the Ocn and OS/BS at histopathological and histomorphometrical analysis in this study as compared with sham-operated control rats, suggesting the estrogen-deficient climacterium symptoms - obese and osteoporosis were induced by OVX, respectively. However, these estrogen-deficient climacterium symptoms induced by bilateral OVX in rats were significantly inhibited by 84 days of continuous oral treatment of GGOT 500, 250 and 125 mg/kg, respectively. Especially, GGOT 500, 250 and 125 mg/kg showed clear dose-dependent inhibitory activities on the OVX-induced climacterium signs. Conclusion: The results suggest that oral administration of GGOT 500, 250 and 125 mg/kg has clear dose-dependent favorable anti-climacterium effects - estrogenic, anti-obese and anti-osteoporotic activities in OVX rats in this experiment.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.