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RISS 인기검색어
- 검색키워드
- 저자 : A. K. Sachan (검색결과 2 건)
- 무료
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- 유료
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Interfacial rheology of coexisting solid and fluid monolayers
Sachan, A. K.,Choi, S. Q.,Kim, K. H.,Tang, Q.,Hwang, L.,Lee, K. Y.,Squires, T. M.,Zasadzinski, J. A. Royal Society of Chemistry 2017 SOFT MATTER Vol.13 No.7
<P>Biologically relevant monolayer and bilayer films often consist of micron-scale high viscosity domains in a continuous low viscosity matrix. Here we show that this morphology can cause the overall monolayer fluidity to vary by orders of magnitude over a limited range of monolayer compositions. Modeling the system as a two-dimensional suspension in analogy with classic three-dimensional suspensions of hard spheres in a liquid solvent explains the rheological data with no adjustable parameters. In monolayers with ordered, highly viscous domains dispersed in a continuous low viscosity matrix, the surface viscosity increases as a power law with the area fraction of viscous domains. Changing the phase of the continuous matrix from a disordered fluid phase to a more ordered, condensed phase dramatically changes the overall monolayer viscosity. Small changes in the domain density and/or continuous matrix composition can alter the monolayer viscosity by orders of magnitude.</P>
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A Novel Searching Algorithm based on Reinforcement Learning
Anil Kumar Yadav,A. K. Sachan 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.6
We introduce an application-oriented reinforcement learning searching algorithm designed for problem with fast learning and capturing goal in less amount of time especially in robotics and games. The importance of game playing in machine learning is an exhaustive application of autonomous agent in real-world problem domain. In our previous published article represent that how autonomous agent learned through self-training and successful trained agent ready for execution [11].In this paper, we design and proposed a new application-oriented searching algorithm especially for game playing in grid world problem. In which first of all agents train all state and able to capture goal successfully. Reinforcement learning is a type of decision making system that takes decision on the basis of reward or penalty signal and learned from environment. Many games, there are no such things that follow fast learning as well as searching and genuine movement for each step. For every state action agent stored previous values in terms of q values in a look-up table. It helps for agent decision making capability during goal hitting or pray captured in the real-world game. In order to access and simulate new searching algorithms in mat lab and evaluated by comparison with different RL techniques [2, 11-12].
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