Microstructure evolution, texture development, and mechanical properties of hot-rolled 5052 aluminum alloy followed by annealing

Aluminum alloys, especially the 5000 series, have drawn the attention of the transportation industry due to their lightweight and consequently reduced fuel consumption. In this regard, one of the major problems of this alloy is its low strength and ductility that can be solved using rolling and post-annealing. Accordingly, the present study concentrates on this issue. Microstructural images showed that the rolling process develops a lot of tangled and trapped dislocations in the sample, which gradually lead to the formation of dislocation bundles and networks. Subsequent annealing can produce a more homogeneous structure with clear grain boundaries and low dislocation density in the inner region of the grains. However, grain refinement efficiency through rolling is retained even after annealing. Initial and rolled Al5052 with the maximum intensity of 2.87 and 6.33 possess the lowest and highest overall texture. Also, post-annealing decreases the texture intensity to 6.33 and 4.87 at 150 and 200 °C, respectively. In this context, deformation texture components strengthen considerably after the rolling process due to the formation of shear bands, and they slightly weaken during heat treatment. Although the initial annealing of the as-received material does not cause discontinuous recrystallization during rolling, it may facilitate the material recovery before rolling. Post-annealing was found to decrease the improved effect of strength by rolling and increase the negative influence of ductility due to the inhibition of dislocation strengthening. The results showed that both dislocation density and the precipitation of Mg atoms are influential for electrical resistivity

Security System for Industrial Gate And Generation of Gate Pass

This paper gives description of face recognition system which automatically identifies and/or verifies the identity of a person from digital images. The basic flow of system is the image is captured by camera. The PCA algorithm detects the face and extracts its features. After the extraction, system compares the captured images with data base images. When the system found the person to be authorized then the system opens the gate automatically. But if the person is unauthorized then the system does not allow to entering in the industrial campus as well as it will generate the gate pass for the person

Adaptive-Aggressive Traders Don't Dominate

For more than a decade Vytelingum's Adaptive-Aggressive (AA) algorithm has been recognized as the best-performing automated auction-market trading-agent strategy currently known in the AI/Agents literature; in this paper, we demonstrate that it is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios. The novel step taken here is to use large-scale compute facilities to brute-force exhaustively evaluate AA in a variety of market environments based on those used for testing it in the original publications. Our results show that even in these simple environments AA is consistently out-performed by IBM's GDX algorithm, first published in 2002. We summarize here results from more than one million market simulation experiments, orders of magnitude more testing than was reported in the original publications that first introduced AA. A 2019 ICAART paper by Cliff claimed that AA's failings were revealed by testing it in more realistic experiments, with conditions closer to those found in real financial markets, but here we demonstrate that even in the simple experiment conditions that were used in the original AA papers, exhaustive testing shows AA to be outperformed by GDX. We close this paper with a discussion of the methodological implications of our work: any results from previous papers where any one trading algorithm is claimed to be superior to others on the basis of only a few thousand trials are probably best treated with some suspicion now. The rise of cloud computing means that the compute-power necessary to subject trading algorithms to millions of trials over a wide range of conditions is readily available at reasonable cost: we should make use of this; exhaustive testing such as is shown here should be the norm in future evaluations and comparisons of new trading algorithms.Comment: To be published as a chapter in "Agents and Artificial Intelligence" edited by Jaap van den Herik, Ana Paula Rocha, and Luc Steels; forthcoming 2019/2020. 24 Pages, 1 Figure, 7 Table

Students’ perspective on absenteeism: a cross-sectional study among students at government medical colleges of Western Maharashtra

Background: The professional courses as undergraduate medical education need high theoretical and clinical classes attendance as those students will be future doctors and will deal with the health and disease of the public. In spite of implementing strict policies regarding student attendance, the rate of absenteeism in medical colleges remains high and is a growing apprehension a phenomenon that is also on the rise in universities worldwide. Methods: A cross-sectional study in few Governments Medical Colleges of Western region of Maharashtra using a self-administered questionnaire. Data was collected and analysed through Google forms. Results: Preparatory leave before every examination required (50.7%), air-conditioned classrooms (49.7%), good ventilated classrooms (44.1%), interactive teachers (41.8%), decrease lecture duration (38%), more practical less theory classes (37.6%) and good transportation facilities (34.3%) were various major suggestions given by students to increase the overall attendance. Conclusions: Feedback from students must be repeatedly considered while designing and revising the curriculum to reduce absenteeism

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Increasing negotiation performance at the edge of the network

Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Offers Protocol (AOP). Under this protocol, agents cannot express any additional information to each other besides a counter offer. This can lead to unnecessarily long negotiations when, for example, negotiations are impossible, risking to waste bandwidth that is a precious resource at the edge of the network. While alternative protocols exist which alleviate this problem, these solutions are too complex for low power devices, such as IoT sensors operating at the edge of the network. To improve this bottleneck, we introduce an extension to AOP called Alternating Constrained Offers Protocol (ACOP), in which agents can also express constraints to each other. This allows agents to both search the possibility space more efficiently and recognise impossible situations sooner. We empirically show that agents using ACOP can significantly reduce the number of messages a negotiation takes, independently of the strategy agents choose. In particular, we show our method significantly reduces the number of messages when an agreement is not possible. Furthermore, when an agreement is possible it reaches this agreement sooner with no negative effect on the utility.Comment: Accepted for presentation at The 7th International Conference on Agreement Technologies (AT 2020

Learning to trade in an unbalanced market

We study the evolution of trading strategies in double auctions as the size of the market gets larger. When the number of buyers and sellers is balanced, Fano et al.~(2011) show that the choice of the order-clearing rule (simultaneous or asynchronous) steers the emergence of fundamentally different strategic behavior. We extend their work to unbalanced markets, confirming their main result as well as that allocative inefficiency tends to zero. On the other hand, we discover that convergence to the competitive outcome takes place only when the market is large and that the long side of the market is more effective at improving its disadvantaged terms of trade under asynchronous order-clearing