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| 1 | Optimizing pulsed current gas tungsten arc welding parameters of AA6061 aluminium alloy using Hooke and Jeeves algorithm显示文摘Though the preferred welding process to weld aluminium alloy is frequently constant current gas tungsten arc welding (CCGTAW),it resulted in grain coarsening at the fusion zone and heat affected zone(HAZ).Hence,pulsed current gas tungsten arc welding(PCGTAW) was performed,to yield finer fusion zone grains,which leads to higher strength of AA6061 (Al-Mg-Si) aluminium alloy joints.In order to determine the most influential control factors which will yield minimum fusion zone grain size and maximum tensile strength of the joints,the traditional Hooke and Jeeves pattern search method was used.The experiments were carried out based on central composite design with 31 runs and an algorithm was developed to optimize the fusion zone grain size and the tensile strength of pulsed current gas tungsten arc welded AA6061 aluminium alloy joints.The results indicate that the peak current (Ip) and base current (IB) are the most significant parameters,to decide the fusion zone grain size and the tensile strength of the AA6061 aluminum alloy joints. | S.BABU T.SENTHIL KUMAR V.BALASUBRAMANIAN | 2008 | 中国有色金属学会会刊:英文版2008，18，5： | 8 |
| 2 | Effect of Pulsed Current TIG Welding Parameters on Pitting Corrosion Behaviour of AA6061 Aluminium Alloy显示文摘中等力量铝合金( Al-Mg-Si 合金)收集了轻重量的制造组织要求高力量重量比率的宽 acceptancein ,如此的 astransportable 桥 girders ,军事车辆,为铝合金焊接过程的道路油轮和铁路运输 systems.Thepreferred 经常是 TIG (钨惰性的气体)焊接到期的 toits 比较地更容易的适用性和更好的 economy.In 焊接这合金的 ofthinner 节的单个通行证 TIG 的盒子。 | T.Senthil Kumar V.Balasubramanian M.Y.Sanavullah S.Babu | 2007 | Journal of Materials Science & Technology2007，23，2： | 5 |
| 3 | 3D Reconstruction of Face from 2D CT Scan Images显示文摘 | T.Senthil Kumar Anupa Vijai | 2012 | Procedia Engineering2012，，： | 1 |
| 4 | THRFuzzy:Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams显示文摘The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers. | Jagannath E.Nalavade T.Senthil Murugan | 2017 | Journal of Central South University2017，24，8： | 1 |
| 5 | Genetic grey wolf optimization and C-mixture for collaborative data publishing显示文摘Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts.The concept of data publishing faces a lot of security issues,indicating that when any trusted organization provides data to a third party,personal information need not be disclosed.Therefore,to maintain the privacy of the data,this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer(Genetic GWO)algorithm for which a C-mixture parameter is used.The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints,such as the k-anonymity,l-diversity and the m-privacy.A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size.The minimum value of the fitness ensures the maximum utility and the maximum privacy.Experimentation was carried out using the adult dataset,and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9,respectively. | Yogesh R.Kulkarni T.Senthil Murugan | 2018 | International Journal of Modeling, Simulation, and Scientific Computing2018，9，6： | 0 |
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