• Restorative Dentistry and Endodontics
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• v.32 no.3
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• pp.222-235
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• 2007

The objectives of this study was to evaluate the effect of thermocycling on the ${\mu}TBS$ (microtensile bond strength) to dentin with four different adhesive systems to examine the bonding durability. Freshly extracted $3^{rd}$ molar teeth were exposed occlusal dentin surfaces, and randomly distributed into 8 adhesive groups 3-steps total-etching (Scotchbond Multi-Purpose Plus; SM, All Bond-2; AB), 2-steps total-etching (Single Bond; SB, One Step plus; OS), 2-steps self-etching (Clearfil SE Bond; SE, AdheSE AD) and single-step self-etching systems (Promp L-Pop; PL, Xeno III; XE) Each adhesive system in 8 adhesives groups was applied on prepared dentin surface as an instruction and resin composite (Z250) was placed incrementally and light-cured. The bonded specimens were sectioned with low-speed diamond saw to obtain $1\times1mm$ sticks after 24 hours of storage at $37^{\circ}C$ distilled water and proceeded thermocycling at the pre-determined cycles of 0, 1,000 and 2,000. The ${\mu}TBS$ test was carried out with EZ-tester at 1mm/min. The results of bond strength test were statistically analyzed using one-way ANOVA/ Duncan's test at the a < 0.05 confidence level. Also, the fracture mode of debonded surface and the interface were examined under SEM. The results of this study were as follows ; 1. 3-step total etching adhesives showed stable, but bond strength of 2-step adhesives were decreased as thermocycling stress. 2. SE showed the highest bond strength, but single step adhesives (PL, XE) had the lowest value both before and after thermocycling. 3 Most of adhesives showed adhesive failure. The total-etching systems were prone to adhesive failure and the single-step systems were mixed failure after thermocycling. Within limited results of this study, the bond strength of adhesive system was material specific and the bonding durability was affected by the bonding step/ procedure of adhesive Simplified bonding procedures do not necessarily imply improved bonding performance.

• Archives of design research
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• v.19 no.2 s.64
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• pp.73-80
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• 2006

The main purpose of this study is to help develop the productional system of the three dimensional digital animation domestically. Many 30 digital animation projects are being developed and finished with foreign computer graphics technology. However, the 3D animation market in Korea is not as good as the ones in other countries. One of the most significant problems is the lack of a program of study in Korea concerning productional systems used to create 3D animation. We previously imported an advanced productional system from the U.S., which was adaptable to big projects, and consequently, expanded the international digital animation market. Now, we need to develop a new production system which is fully adaptable to the Korean market. A non-sequential system of 3D digital animation tailored for small projects is more adaptable to domestic industry than the sequential system known as the 'Pipeline system.' A non-sequential system is good for small productions that are trying to create animation on a limited budget. The workers in this system can learn to make animation fast and effectively. They can also learn how to discuss with the others in order to avoid duplication of action. In contrast, in the Pipeline System, many workers repeat confirmation steps during the process for the same animations. The benefit of the non-sequential system is that the worker's interest and education can speed up production, because he/she can quickly learn every step of the animation process instead of just doing a particular job which does not allow him/her to observe the other steps involved. The 'animatic' step is the most important in the non-sequential system. The final animation is produced from the animatic. It is developed from scratch through to the final product but only after the agreement of all the workers, including a director.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4291-4297
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• 2015

EEG has been used to measure the emotion of amenity and discomfort in the interior space. EEG is limited to the experiment, because it is a equipment of contact type. However, Vibraimage can measure the emotion with a web camera. Because Vibraimage is a equipment of non-contact type, it is more suitable for the interior space than EEG. Therefor, it tries to find a correlation variable between EEG and Vibraimage to measure the human emotions. In this study, it were analyzed correlation of EEG and vibraimage due to variation of loudness 60[dB], 90[dB] and rosemary, jasmine scents. Check the health status of subjects who were selected 3 male students, and the period of this experiment was about months. The condition of the environmental test room was in temperature 25[$^{\circ}C$], relative humidity 50[RH%], air current speed 0.02[m/s] and illuminance 1000[lux]. It were analyzed correlation of twenty-three index of EEG(absolute ${\theta}$, relative ${\theta}$, absolute $S{\alpha}$, relative $S{\alpha}$, absolute ${\alpha}$, relative ${\alpha}$, absolute ${\beta}$, relative ${\beta}$, absolute $\gamma$, relative $\gamma$, absolute $F{\alpha}$, relative $F{\alpha}$, absolute SMR, relative SMR, $SMR/{\theta}$, $SMR+M{\beta}/{\theta}$, absolute $H{\beta}$, relative $H{\beta}$, $H{\beta}/{\alpha}$, absolute $M{\beta}$, relative $M{\beta}$, SEF50, ASEF50) and ten index of Vibraimage(Aggression, Stress, Tension/Anxiety, Suspect, Balance, Charm, Energy, Self regulation, Inhibition, Neuroticism). As a result, I was found that relative ${\gamma}$ index of EEG and neuroticism index of Vibraimage have a high correlation as (${\pm}$).414 and (${\pm}$).424.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.727-736
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• 2021

Road tunnel lengths are increasing. Some 1,300 tunnels with 1,102 km in length had been increased till 2019 from 2010. There are 64 tunnels over 3,000 m in length, with their total length adding up to 276.7 km. Safety facilities in the event of a tunnel fire are critical so as to prevent large-scale casualties. Standards for installing disaster prevention facilities are being proposed based on the guidelines of the Ministry of Land, Infrastructure and Transport, but they may be limited to deep underground tunnels. This study was undertaken to provide guidelines for the spacing of evacuation connection passages and the widths of evacuation connection doors. Evacuation with various spacing and widths was simulated in regards to evacuation time, which is the measure of safety, using the evacuation analysis simulation software EXODUS Ver.6.3 and the fire/smoke analysis software SMARTFIRE Ver.4.1. Evacuation connection gates with widths of 0.9 m and 1.2 m, and spacings of 150 m to 250 m, were set to every 20 m. In addition, longitudinal slopes of 6 % and 0 % were considered. It was determined to be safe when the evacuation completion time was shorter than the delay diffusion time. According to the simulation results, all occupants could complete evacuation before smoke spread regardless of the width of the evacuation connection door when the longitudinal slope was 6 % and the interval of evacuation connection passage was 150 m. When the evacuation connection passage spacing was 200 m and the evacuation connection gate width was 1.2 m, all occupants could evacuate when the longitudinal slope was 0 %. Due to difference in evacuation speed according to the longitudinal slope, the evacuation time with a 6 % slope was 114 seconds shorter (with the 190 m connection passage) than with a 0 % slope. A shorter spacing of evacuation connection passages may reduce the evacuation time, but this is difficult to implement in practice because of economic and structural limitations. If the width of the evacuation junction is 1.2 m, occupants could evacuate faster than with a 0.9 m width. When the width of a connection door is 1.2 m with appropriate connection passage spacing, it might provide a means to increase economic efficiency and resolve structural limitations while securing evacuation safety.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.73-78
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• 2022

This experiment was conducted to establish the technology for artificial hay preparation in Korea. Using far-infrared heater, a device that can control temperature, airflow, and far-infrared radiation was produced and conducted on the fourth harvested alfalfa. The drying conditions were carried out by selecting a total of four conditions. For each condition, the radiation rate was set to around 40% (33-42%), and the temperature was set at 58~65℃, and the speed of the airflow was fixed at 60m/s. The overall drying time was set to 30 min in the single and 60 min (30-30 min) and 90 min (30-30-30 min) in the complex condition, and the radiation rate and temperature were changed by time period. In the case of drying condition 1, the final dry matter (DM) content was 46.26%, which did not reach a DM suitable for hay. However, all of the alfalfa corresponding to the remaining drying conditions 2 to 7 showed a DM content of 80% or more, resulting in optimal alfalfa hay production. In power consumption according to the drying conditions, the second drying condition showed the lowest at 4.7 KW, and the remaining drying conditions were as high as 6.5 to 7.1 KW. The crude protein content was found to be high at an average of 25.91% and it showed the highest content in the 5th drying condition (26.93%) and the lowest value in the 6th drying condition (25.16%). The digestibility showed a high value with an average of 84.90%, and there was no significant difference among treatments (p>0.05). Considering the above results, it was judged that drying condition 2 was the most advantageous.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.