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- 검색키워드
- 저자 : Telue, Berekjan (검색결과 4 건)
- 무료
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Design of Cold-formed Steel Wall Frames Lined with Pasterboard on One Side Under Axial Compression
Yaip Telue,Mahen Mahendran 한국강구조학회 2006 International Journal of Steel Structures Vol.6 No.3
plasterboard lining and are used in the construction of both the load bearing and non-load bearing walls in residential, industrialand comercial buildings. However, the design of these wall frames does not utilise the strengthening effects of theplasterboard lining in carrying axial loads. An experimental study has shown that the strength of the studs in compression wasincreased significantly when they were lined with plasterboard one or both sides. In order to fuly understand the behaviourmodel of one side lined wall frame was developed and validated using experimental results. This paper presents the details ofthe finite element modelling of one side lined steel wall frames and the results. A design method based on apropriate effectivelength factors was developed within the provisions of Australian/New Zealand standard for cold-formed steel structures topredict the ultimate loads and failure modes of one side lined stel wall frames.
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Estimation of Lower Jaw Density using CT data
Jargalsaikhan, Ariunbold,Sengee, Nyamlkhagva,Telue, Berekjan,Ochirkhvv, Sambuu Korea Multimedia Society 2019 The journal of multimedia information system Vol.6 No.2
Bone density is one of the factors in the early failure of dental implants and doctors should make a preoperative assessment of jaw bone density using patient's CT data before dental implant surgery in order to find out whether the patient has osteoporosis and osteopenia. The main goal of this study was to propose a method that based on image processing techniques in order to provide accurate information about where to drill and place an abutment screw of implants in the jaw bone for doctors and reduce human activity for the estimation of the local cancellous bone density of mandible using CT data. The experiment was performed on a computed tomography data of the jaw bone of two different individuals. We assumed that the result of the estimation of jaw bone density depends on the angle of drilling and average HU (Hounsfield Unit) values were used to evaluate the quality of local cancellous bone density of mandible. As a result of this study, we have been developed a toolbox that can be used to estimate jaw bone density automatically and found a positive correlation between the angle of the drill and time complexity but a negative correlation between the diameter of the drill and time complexity.
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Hierarchical Cluster Analysis Histogram Thresholding with Local Minima
Sengee, Nyamlkhagva,Radnaabazar, Chinzorig,Batsuuri, Suvdaa,Tsedendamba, Khurel-Ochir,Telue, Berekjan Korea Multimedia Society 2017 The journal of multimedia information system Vol.4 No.4
In this study, we propose a method which is based on "Image segmentation by histogram thresholding using hierarchical cluster analysis"/HCA/ and "A nonparametric approach for histogram segmentation"/NHS/. HCA method uses that all histogram bins are one cluster then it reduces cluster numbers by using distance metric. Because this method has too many clusters, it is more computation. In order to eliminate disadvantages of "HCA" method, we used "NHS" method. NHS method finds all local minima of histogram. To reduce cluster number, we use NHS method which is fast. In our approach, we combine those two methods to eliminate disadvantages of Arifin method. The proposed method is not only less computational than "HCA" method because combined method has few clusters but also it uses local minima of histogram which is computed by "NHS".
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Vinay Padimi,Venkata Sravan Telu,Devarani Devi Ningombam 한국전자통신연구원 2023 ETRI Journal Vol.45 No.6
Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795,000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.
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