• The Journal of Korean Academy of Prosthodontics
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• v.47 no.1
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• pp.12-20
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• 2009

Statement of problem: When an implant is fixed, a fixture comes into contact with a tissue fluid. Adhesion of a tissue fluid to a surface of implant is various case by case. Purpose: The ultimate goal of this work is to analyze a correlation between a surface roughness and wettability of implant specimens. A measurement for wettability is performed considering 4 types of specimen implant with surface treatments different from each other to investigate the change of wettability with the elapse of time. Material and methods: Firstly, 20 specimens of titanium were prepared. The specimen were made of a commercial Titanium Grade IV with the diameter of 10 mm and the thickness of 1 mm. According to the method of surface treatment, the specimens were classified into 4 groups of 5 specimens per group. Group A: Machined Surface Group B: Anodized surface Group C: RBM (HA blasting) surface Group D: CMP (calcium methaphosphate) coating surface. Surface roughness of specimen was measured using SV-3000S4 (Mituyoto, Japan). The measurement was based on the standard of JIS1994. Sessile drop method was used to measure the wettability, which measures contact angle between implant disc and saline with the time interval of 5, 10, and 15 seconds. SPSS 11.0 was used to analyze the collected data. In order to analyze the difference of wettability and surface roughness according to implant surface treatment method. The statistical significance was tested with the confidence level of 95%. Pearson's correlation coefficient was used to evaluate the correlation of surface roughness and wettability. Results: The difference of surface roughness was statistically significant in the order of Group C ($1.69{\pm}0.26$), Group D ($1.58{\pm}0.16$), Group B ($0.78{\pm}0.14$) Group A ($0.18{\pm}0.05$). The wettability has also a statistically significant difference, which was in the order of group B ($17.70{\pm}2.66$), Group C ($27.86{\pm}4.52$), Group D ($66.28{\pm}3.70$) Group A ($70.52{\pm}8.00$). There was no difference in wettability with the passage of time. Conclusions: 1. The surface roughness was high in the order of RBM, CMP, Anodized, Machined group (P<.05). 2. The wettability was high in the order of Anodized, RBM, CMP, Machined group (P<.05). 3. There was no statistical significance in the correlation of surface roughness and wettability.

• Molecules and Cells
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• v.27 no.1
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• pp.83-88
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• 2009

Amino acid homology analysis predicted that rbmD, a putative glycosyltransferase from Streptomyces ribosidificus ATCC 21294, has the highest homology with neoD in neomycin biosynthesis. S. fradiae BS1, in which the production of neomycin was abolished, was generated by disruption of the neoD gene in the neomycin producer S. fradiae. The restoration of neomycin by self complementation suggested that there was no polar effect in the mutant. In addition, S. fradiae BS6 was created with complementation by rbmD in S. fradiae BS1, and secondary metabolite analysis by ESI/MS, LC/MS and MS/MS showed the restoration of neomycin production in S. fradiae BS6. These gene inactivation and complementation studies suggested that, like neoD, rbmD functions as a 2-N-acetlyglucosaminyltransferase and demonstrated the potential for the generation of novel aminoglycoside antibiotics using glycosyltransferases in vivo.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.42 no.1
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• pp.38-42
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• 2016

The purpose of this study is to evaluate the degree of osseointegration of resorbable blasting media (RBM) surface implants retrieved from humans. Three implants in the mandibular molar region that were surface-treated with RBM were retrieved from two patients. The implants were used to manufacture specimens in order to measure the bone-implant contact (BIC) ratio. The BIC ratios of the three implants were found to be an average of $69.0%{\pm}9.1%$. In conclusion, that RBM surface implants are integrated into the host environment with histological significance and the BIC ratio of the RBM surface-treated implant was not significantly different from that of other surface-treated implants.

• The Journal of Advanced Prosthodontics
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• v.5 no.2
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• pp.172-178
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• 2013

PURPOSE. The purpose of this study was to assess the difference in efficacy between calcium metaphosphate (CMP)-coated implant fixtures and conventional resorbable blasted media (RBM) processed implant fixtures. MATERIALS AND METHODS. This study targeted 50 implants from 44 patients who visited Dankook University Dental Hospital. Implantations were done separately for RBM treated and CMP-coated implants, although their design was the same. Calcium metaphosphate has a quicker biodegradation process through hydrolysis compared to other phosphate calcium groups. For the first year of the implantation, the resorption volume of marginal bone analyzed via radiography and perio-test value were measured, under the check plan. Their analyses were composed of a non-inferiority trials test. A 95% level of reliability was used. RESULTS. In the comparative analysis of the resorption volume of marginal bone and the perio-test value, no statistically significant difference was found between the CMP-coated implants and RBM implants. CONCLUSION. One year after the implant placement, CMP-coated implants were found not to be inferior to the conventional RBM implants.

• Smart Media Journal
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• v.12 no.3
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• Journal of Periodontal and Implant Science
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• v.43 no.6
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• pp.301-307
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• 2013

Purpose: This in vitro study was performed to assess the adherence of Porphyromonas gingivalis to a resorbable blast media (RBM) titanium surface pretreated with an ultrasonic scaler or toothbrush and to evaluate the effects of the treatment of the RBM titanium discs on the bacterial removal efficiency of brushing by crystal violet assay and scanning electron microscopy. Methods: RBM titanium discs were pretreated with one of several ultrasonic scaler tips or cleaned with a toothbrush. Then the titanium discs were incubated with P. gingivalis and the quantity of adherent bacteria was compared. The disc surfaces incubated with bacteria were brushed with a toothbrush with dentifrice. Bacteria remaining on the disc surfaces were quantified. Results: A change in morphology of the surface of the RBM titanium discs after different treatments was noted. There were no significant differences in the adherence of bacteria on the pretreated discs according to the treatment modality. Pretreatment with various instruments did not produce significant differences in the bacterial removal efficiency of brushing with dentifrice. Conclusions: Within the limits of this study, various types of mechanical instrumentation were shown to cause mechanical changes on the RBM titanium surface but did not show a significant influence on the adherence of bacteria and removal efficiency of brushing.

• The Journal of Korean Academy of Prosthodontics
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• v.44 no.6
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• pp.722-733
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• 2006

Statement of problem. The use of small diameter implants having less than 3 mm in diameter were restricted because of lack of bonding strength to bone. Purpose. The purpose of this study was to observe how much resorbable blast media pin implants increase the binding force to the bone compared to machined transitional pin implants by measuring removal torque, and whether they can be used as final implants for replacement of small diameter teeth. Material and method. Fifteen rabbits were used in this study. Two kinds of implants (resorbable blast media pin implants and machined transitional pin implants) were inserted in each tibia bicortically. After healing time of 2, 4 and 8 weeks, the removal torque values were recorded and the rabbits were sacrificed for histological analysis. Linear finite element method analyses were conducted to compare bicortical fixation with monocortical fixation. Result and conclusion. Within the limitation of this in vivo study, the following conclusions were drawn: 1) The removal torque value of RBM pin implants showed statistically significant increase compared to machined pin implants at 2, 4, and 8 weeks respectively (p<0.05). 2) The removal torque value of RBM pin implants at 2, 4, and 8 weeks was increased statistically significantly with time (p<0.05). 3) Bicortical fixation showed better stress distribution compared with monocortical fixation in a linear finite element method analysis. 4) RBM pin implants are not recommended as transitional implants because they showed a lot of bone fracture in histologic specimens.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.421-422
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• 2015

Deep learning, a deepen neural network technology that demonstrates the enhanced performance of neural network analysis, has been getting the spotlight in recent years. The present study proposed a process to test the error rates of certain variables and predict big data by using R, a analysis visualization tool based on deep learning, applying the RBM(Restricted Boltzmann Machine) algorithm to deep learning. The weighted value of each dependent variable was also applied after the classification of dependent variables. The investigator tested input data with the RBM algorithm and designed a process to detect error rates with the application of R.

• Journal of the Korean institute of surface engineering
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• v.43 no.1
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• pp.17-24
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• 2010

In this study, surface characteristics of dental implant fixture with various manufacturing process have been researched using electrochemical methods. The dental implant fixture was selected with 5 steps by cleaning, surface treatment and sterilization with same size and screw structure; the 1st step-machined surface, 2nd step-cleaned by thinner and prosol solution, 3th step-surface treated by RBM (resorbable blasting media) method, 4th step-cleaned and dried, 5th step-sterilized by gamma-ray. The electrochemical behavior of dental implant fixture has been evaluated by using potentiostat (EG&G Co, 2273A) in 0.9% NaCl solution at $36.5{\pm}1^{\circ}C$. The corrosion surface was observed using field-emission scanning electron microscopy (FE-SEM) and energy dispersive x-ray spectroscopy (EDS). The step 5 sample showed the cleaner and rougher surface than step 3 sample. The step 5 sample of implant fixture treated by RBM and gamma sterilization showed the low corrosion current density compared to others. Especially, the step 3 sample of implant fixture treated by RBM was presented the lowest value of corrosion resistance and the highest value of corrosion current density. The step 3 sample showed the low value of polarization resistance compared to other samples. In conclusion, the implant fixture treated with RBM and gamma sterilization has the higher corrosion resistance, and corrosion resistance depends on the step of manufacturing process.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.380-384
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• 2007

MLP(Multi-Layer Perceptron)를 이용한 학습은 간단한 구조에도 비선형 분류가 가능하다는 장점을 가지고 있다. 하지만 오류역전파 알고리즘을 사용함으로써 시간의 소모가 크고 원치 않는 결과값으로의 수렴가능성을 배제할 수 없다는 단점을 가지고 있다. 이는 초기설정의 의존도가 높기 때문에 발생하는 문제들로 좋은 결과값에 근접한 곳으로 초기화가 이루어지면 좋은 학습 성능을 보이지만 반대로 좋은 결과값으로부터 멀리 떨어진 곳으로 신경망의 초기화가 이루어지면 학습 성능이 현저히 낮아지는 현상을 보인다. 본 논문에서는 MLP 전체의 층을 대상으로 하는 본 학습이 이루어지기 전에 RBM(Restricted Boltzmann Machine)을 이용, 층간 선행학습을 행하고 그 결과로 얻어지는 가중치와 바이어스를 본 MLP 학습의 초기화 데이터로 사용하는 개선 MLP 학습 알고리즘을 제안한다. 이 방법을 사용함으로써 MLP 학습 속도향상은 물론 원치 않는 지역해로의 수렴까지 방지할 수 있어 전체적인 학습 성능향상이 가능하게 된다.