45,550 research outputs found
URL Recommender using Parallel Processing
The main purpose of this project is to section similar news and articles from a vast variety of news articles. Let’s say, you want to read about latest news related to particular topic like sports. Usually, user goes to a particular website and goes through some news but he won’t be able to cover all the news coverage in a single website. So, he would be going through some other news website to checking it out and this continues. Also, some news websites might be containing some old news and the user might be going through that. To solve this, I have developed a web application where in user can see all the latest news from different websites in a single place. Users are given choice to select the news websites from which they want to view the latest news. The articles which we get from news websites are very random and we will be applying the DBSCAN algorithm and place the news articles in the cluster form for each specific topic for user to view. If the user wants to see sports, he will be provided with sports news section. And this process of extracting random news articles and forming news clusters are done at run time and at all times we will get the latest news as we will be extracting the data from web at run time. This is an effective way to watch all news at single place. And in turn this can be used as articles (URL) recommender as the user has to just go through the specific cluster which interests him and not visit all news websites to find articles. This way the user does not have to visit different sites to view all latest news. This idea can be expanded to not just news articles but also in other areas like collecting statistics of financial information etc. As the processing is done at runtime, the performance has to be improved. To improve the performance, the distributed data mining is used and multiple servers are being used which communicate with each other
Noise resistant generalized parametric validity index of clustering for gene expression data
This article has been made available through the Brunel Open Access Publishing Fund.Validity indices have been investigated for decades. However, since there is no study of noise-resistance performance of these indices in the literature, there is no guideline for determining the best clustering in noisy data sets, especially microarray data sets. In this paper, we propose a generalized parametric validity (GPV) index which employs two tunable parameters α and β to control the proportions of objects being considered to calculate the dissimilarities. The greatest advantage of the proposed GPV index is its noise-resistance ability, which results from the flexibility of tuning the parameters. Several rules are set to guide the selection of parameter values. To illustrate the noise-resistance performance of the proposed index, we evaluate the GPV index for assessing five clustering algorithms in two gene expression data simulation models with different noise levels and compare the ability of determining the number of clusters with eight existing indices. We also test the GPV in three groups of real gene expression data sets. The experimental results suggest that the proposed GPV index has superior noise-resistance ability and provides fairly accurate judgements
Order parameter model for unstable multilane traffic flow
We discuss a phenomenological approach to the description of unstable vehicle
motion on multilane highways that explains in a simple way the observed
sequence of the phase transitions "free flow -> synchronized motion -> jam" as
well as the hysteresis in the transition "free flow synchronized motion".
We introduce a new variable called order parameter that accounts for possible
correlations in the vehicle motion at different lanes. So, it is principally
due to the "many-body" effects in the car interaction, which enables us to
regard it as an additional independent state variable of traffic flow. Basing
on the latest experimental data (cond-mat/9905216) we assume that these
correlations are due to a small group of "fast" drivers. Taking into account
the general properties of the driver behavior we write the governing equation
for the order parameter. In this context we analyze the instability of
homogeneous traffic flow manifesting itself in both of the mentioned above
phase transitions where, in addition, the transition "synchronized motion ->
jam" also exhibits a similar hysteresis. Besides, the jam is characterized by
the vehicle flows at different lanes being independent of one another. We
specify a certain simplified model in order to study the general features of
the car cluster self-formation under the phase transition "free flow
synchronized motion". In particular, we show that the main local parameters of
the developed cluster are determined by the state characteristics of vehicle
motion only.Comment: REVTeX 3.1, 10 pages with 10 PostScript figure
Upgrade of the ALICE Inner Tracking System
During the Long Shutdown 2 of the LHC in 2018/2019, the ALICE experiment
plans the installation of a novel Inner Tracking System. It will replace the
current six layer detector system with a seven layer detector using Monolithic
Active Pixel Sensors. The upgraded Inner Tracking System will have
significantly improved tracking and vertexing capabilities, as well as readout
rate to cope with the expected increased Pb-Pb luminosity of the LHC. The
choice of Monolithic Active Pixel Sensors has been driven by the specific
requirements of ALICE as a heavy ion experiment dealing with rare processes at
low transverse momenta. This leads to stringent requirements on the material
budget of 0.3 per layer for the three innermost layers. Furthermore,
the detector will see large hit densities of on average for minimum-bias events in the
inner most layer and has to stand moderate radiation loads of 700 kRad TID and
1 MeV n NIEL at maximum. The
Monolithic Active Pixel Sensor detectors are manufactured using the TowerJazz
0.18 m CMOS Imaging Sensor process on wafers with a high-resistivity
epitaxial layer. This contribution summarises the recent R&D activities and
focuses on results on the large-scale pixel sensor prototypes.Comment: 10 pages, 8 figures, proceedings of VERTEX 2014, 15-19 September 201
Automatic Color Inspection for Colored Wires in Electric Cables
In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions
Spectral Unmixing with Multiple Dictionaries
Spectral unmixing aims at recovering the spectral signatures of materials,
called endmembers, mixed in a hyperspectral or multispectral image, along with
their abundances. A typical assumption is that the image contains one pure
pixel per endmember, in which case spectral unmixing reduces to identifying
these pixels. Many fully automated methods have been proposed in recent years,
but little work has been done to allow users to select areas where pure pixels
are present manually or using a segmentation algorithm. Additionally, in a
non-blind approach, several spectral libraries may be available rather than a
single one, with a fixed number (or an upper or lower bound) of endmembers to
chose from each. In this paper, we propose a multiple-dictionary constrained
low-rank matrix approximation model that address these two problems. We propose
an algorithm to compute this model, dubbed M2PALS, and its performance is
discussed on both synthetic and real hyperspectral images
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