12,471 research outputs found
Design pipe bracket for vessel with using Titanium metal in marine environment
The research investigates the design and utilization of the pipe bracket with titanium metal for the ocean going vessel to confront marine environment. The main aim of this report is to study the performance of titanium metal compared with other materials when they are being used in marine environment .Another aim of this report is to design pipe bracket for the ocean going vessel, then did the simulation and calculation of the loads which applied on the pipe bracket. The studying of my aims were targeted during all the phases of this project.
This report has gone through several stages so that be achieved. The first phase was referring the gathering information about the primary mechanical properties of titanium metal as light weight, flexible and strong resistance to corrosion. The different corrosion properties of pipe material and how they interact together with titanium metal or sea water. The second phase was concerning three different pipe types (rigid support, adjustable support, elastic support) and choose the type of adjustable due to it makes easily assemble due to nuts and bolts could be rearranged for adjusting the support when using on the vessel. Mention the Standard pipe size for using in different place and having a design drawing of my pipe bracket. The next phase was doing mechanical analysis of my bracket model on the Solidworks program and maximum loads which were applied on the bracket were calculated by using related formulas. The last phase was considering the manufacturing process for the pipe bracket and having the primary cost for making and selling it
Hunting for New Physics with Unitarity Boomerangs
Although the unitarity triangles () carry information about the
Kobayashi-Maskawa (KM) quark mixing matrix, it explicitly contains just three
parameters which is one short to completely fix the KM matrix. It has been
shown recently, by us, that the unitarity boomerangs () formed using two
, with a common inner angle, can completely determine the KM matrix and,
therefore, better represents, quark mixing. Here, we study detailed properties
of the , of which there are a total 18 possible. Among them, there is only
one which does not involve very small angles and is the ideal one for practical
uses. Although the have different areas, there is an invariant quantity,
for all , which is equal to a quarter of the Jarlskog parameter
squared. Hunting new physics, with a unitarity boomerang, can reveal more
information, than just using a unitarity triangle.Comment: Latex 9 pages with two figures. References updated
EEF: Exponentially Embedded Families with Class-Specific Features for Classification
In this letter, we present a novel exponentially embedded families (EEF)
based classification method, in which the probability density function (PDF) on
raw data is estimated from the PDF on features. With the PDF construction, we
show that class-specific features can be used in the proposed classification
method, instead of a common feature subset for all classes as used in
conventional approaches. We apply the proposed EEF classifier for text
categorization as a case study and derive an optimal Bayesian classification
rule with class-specific feature selection based on the Information Gain (IG)
score. The promising performance on real-life data sets demonstrates the
effectiveness of the proposed approach and indicates its wide potential
applications.Comment: 9 pages, 3 figures, to be published in IEEE Signal Processing Letter.
IEEE Signal Processing Letter, 201
Tensor Product Generation Networks for Deep NLP Modeling
We present a new approach to the design of deep networks for natural language
processing (NLP), based on the general technique of Tensor Product
Representations (TPRs) for encoding and processing symbol structures in
distributed neural networks. A network architecture --- the Tensor Product
Generation Network (TPGN) --- is proposed which is capable in principle of
carrying out TPR computation, but which uses unconstrained deep learning to
design its internal representations. Instantiated in a model for image-caption
generation, TPGN outperforms LSTM baselines when evaluated on the COCO dataset.
The TPR-capable structure enables interpretation of internal representations
and operations, which prove to contain considerable grammatical content. Our
caption-generation model can be interpreted as generating sequences of
grammatical categories and retrieving words by their categories from a plan
encoded as a distributed representation
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