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Madhu H. C. Edachery Vimal Lijesh K. P. Perugu Chandra Shekhar Kailas Satish V. 《Metallurgical and Materials Transactions A》2020,51(8):4086-4099
Metallurgical and Materials Transactions A - Hybrid nanocomposites have potential as wear-resistant materials. However, synthesizing these nanocomposites by conventional molten state methods result... 相似文献
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In this work, we designed, fabricated and tested a disposable, flow-through amperometric sensor for free chlorine determination in water. The sensor is based on the principle of an electrochemical cell. The substrate, as well as the top microfluidic layer, is made up of a polymer material. The advantages include; (a) disposability from low cost; (b) stable operation range from three-electrode design; (c) fluidic interconnections that provide on line testing capabilities; and (d) transparent substrate which provides for future integration of on-chip optics. The sensor showed a good response and linearity in the chlorine concentration ranging from 0.3 to 1.6 ppm, which applies to common chlorination process for drinking water purification. 相似文献
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Yadav Rakesh Kumar Abhishek Yadav Vijay Kumar Verma Shekhar Venkatesan S. 《Pattern Analysis & Applications》2021,24(4):1667-1683
Pattern Analysis and Applications - The performance of graph-based learning techniques largely relies on the edges defined between the vertices of the graph. These edges that represent the affinity... 相似文献
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Yadav Vijay Kumar Yadav Rakesh Kumar Verma Shekhar Venkatesan S. 《The Journal of supercomputing》2022,78(2):2386-2416
The Journal of Supercomputing - In the Attribute-Based Encryption (ABE) scheme, patients encrypt their electronic health record (EHR), attach the appropriate attributes with it, and outsource them... 相似文献
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Soft computing-based approaches have been developed to predict specific energy consumption and stability margin of a six-legged robot ascending and descending some gradient terrains. Three different neuro-fuzzy and one neural network-based approaches have been developed. The performances of these approaches are compared among themselves, through computer simulations. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference system is found to perform better than other three approaches for predicting both the outputs. This could be due to a more exhaustive search carried out by the genetic algorithm in comparison with back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs. A designer may use the developed soft computing-based approaches in order to predict specific energy consumption and stability margin of the robot for a set of input parameters, beforehand. 相似文献
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Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot 总被引:2,自引:0,他引:2
Shibendu Shekhar RoyAuthor Vitae 《Robotics and Autonomous Systems》2012,60(1):72-82
Minimization of energy consumption plays a key role in the locomotion of a multi-legged robot used for various purposes. Turning gaits are the most general and important factors for omni-directional walking of a six-legged robot. This paper presents an analysis on energy consumption of a six-legged robot during its turning motion over a flat terrain. An energy consumption model is developed for statically stable wave gaits in order to minimize dissipating energy for optimal feet forces distributions. The effects of gait parameters, namely angular velocity, angular stroke and duty factors are studied on energy consumption, as the six-legged robot walks along a circular path of constant radius with wave gait. The variations of average power consumption and energy consumption per unit weight per unit traveled length with the angular velocity and angular stroke are compared for the turning gaits of a robot with four different duty factors. Computer simulations show that wave gait with a low duty factor is more energy-efficient compared to that with a high duty factor at the highest possible angular velocity. A stability analysis based on normalized energy stability margin is performed for turning motion of the robot with four duty factors for different angular strokes. 相似文献
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The rapid growth in the performance of graphics hardware, coupled with recent improvements in its programmability has lead to its adoption in many non-graphics applications, including a wide variety of scientific computing fields. At the same time, a number of important dynamic optimal policy problems in economics are athirst of computing power to help overcome dual curses of complexity and dimensionality. We investigate if computational economics may benefit from new tools on a case study of imperfect information dynamic programming problem with learning and experimentation trade-off, that is, a choice between controlling the policy target and learning system parameters. Specifically, we use a model of active learning and control of a linear autoregression with the unknown slope that appeared in a variety of macroeconomic policy and other contexts. The endogeneity of posterior beliefs makes the problem difficult in that the value function need not be convex and the policy function need not be continuous. This complication makes the problem a suitable target for massively-parallel computation using graphics processors (GPUs). Our findings are cautiously optimistic in that the new tools let us easily achieve a factor of 15 performance gain relative to an implementation targeting single-core processors. Further gains up to a factor of 26 are also achievable but lie behind a learning and experimentation barrier of their own. Drawing upon experience with CUDA programming architecture and GPUs provides general lessons on how to best exploit future trends in parallel computation in economics. 相似文献