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Literature review for energy consumption in manufacturing industry from machine to factory level
Energy consumption is one of the most critical issues in the manufacturing
industry. The modeling, analysis and improvement of energy cost and consumption in multistage production system have been widely studied in many research
works. To summarize the latest development of research of energy consumption, a large amount of research work have been investigated. The review work
includes effect of design for the energy consumption and interactions between
many aspects related to industry. This research research combines energy systems from microscopic to macroscopic, which includes machine, manufacturing
line and factory level. This document begins with a review of energy consumption on machine level. Including the schema for machine states and transition of
energy, the process model for energy analysis and improvement methods. These
topics are further discussed in detail for different processes, eg. forming process, additive processes, etc. In the next part of the review the researches on
energy consumption for the multi-machine/manufacturing line level are introduced. Include the define of manufacturing line level, the logical benchmarking
of manufacturing lines, and the utilization of energy flows. At last, the detailed
way for the factory level energy management are studied. Including the factory
energy management system and the method of reduce energy consumption.
MD SAHIDUL ISLAM
A Regression based approach for link residual time prediction in MANETs
Mobile ad-hoc network (MANET) is a collection of mobile
terminals forming an infrastructure less and quick deployable network,
which can communicate to each other via multiple hops or single hop.
Such ad-hoc networks have always been important for various applications like defence applications especially for countries like India having
boundaries and regions with large geographical diversity. Mobility attribute is a notable one in MANETs, as this leads to frequent topology
changes which are the primary cause of route failure. A route is an ordered set of links, hence for predicting future availability of any particular
route, it is important to estimate the availability of its currently available constituent links. This paper explores various link availability prediction model and proposes a least square polynomial regression-based
statistical approach to predict the availability of link. Proposed approach
assumes that movement of nodes are based on column mobility model i.e
each node in the network is linearly moving with constant speed. Each
node in the network periodically broadcasts hello packets to its neighbours to inform it’s availability in the network. Neighbour node receives
hello packet and uses its signal strength to estimate distance between
sender and receiver of hello packet. A monotonically decreasing signal
strength of hello packets at receiver node indicates that nodes are moving away from each other and link between them may break in future so
it starts link residual time prediction algorithm to predict the time when
the distance between them will exceed the pre-defined threshold value.
The proposed algorithm is simulated using NS 2.35. The performance
of the algorithm has been analyzed for identified parameters. The results are also been compared by simulating other existing link prediction
approaches based on interpolation.
Best of the Best: A Comparison of Factor Models
We compare major factor models and find that the Stambaugh and Yuan (2016) four-factor model is the overall winner in the time-series domain. The Hou, Xue, and Zhang (2015) q-factor model takes second place and the Fama and French (2015) five-factor model and the Barillas and Shanken (2018) six-factor model jointly take third place. But the pairwise cross-sectional R2 and the multiple model comparison tests show that the Hou, Xue, and Zhang (2015) q-factor model, the Fama and French (2015) five-factor and four-factor models, and the Barillas and Shanken (2018) six-factor model take equal first place in the horse race.
Shamim Ahmed, Ziwen Bu, Daniel Tsvetanov
Maximum diversity problem
We observe three differents algorithms, GRASP, Tabu Search best and Tabu Search first, for the maximum diversity problem and we get the best of those.
Juan Francisco Gómez González and Miguel Ángel Beltrán Sánchez