A Dynamic Clustering Querying Algorithm Based on Grid in Manufacturing System

2011 ◽  
Vol 323 ◽  
pp. 89-93
Author(s):  
Jun Zeng
Keyword(s):  
Processing Speed ◽  
Manufacturing System ◽  
Data Sets ◽  
Dynamic Clustering ◽  
Data Set ◽  
Density Data ◽  
Clustering Center ◽  
And Performance

This article presents a querying algorithm of dynamic clustering based on grid in manufacturing system. The algorithm divides grids based on the location of nodes, and computes clustering center of grids, then queries based on clustering in the station, processing speed of this method are independent of size of data set, processing speed is quick, it can handle massive and multi-density data sets and performance is better in terms of accuracy and efficiency of querying.

scholarly journals Integrating Coral Restoration Data With a Novel Coral Sample Registry

2021 ◽  
Vol 8 ◽  
Author(s):  
Amelia Moura ◽  
Brian Beck ◽  
Renee Duffey ◽  
Lucas McEachron ◽  
Margaret Miller ◽  
...  
Keyword(s):  
Data Structures ◽  
Current Data ◽  
South Florida ◽  
Coral Tissue ◽  
Data Sets ◽  
Accession Number ◽  
Sample Collection ◽  
Data Set ◽  
Coral Sample ◽  
And Performance

In the past decade, the field of coral reef restoration has experienced a proliferation of data detailing the source, genetics, and performance of coral strains used in research and restoration. Resource managers track the multitude of permits, species, restoration locations, and performance across multiple stakeholders while researchers generate large data sets and data pipelines detailing the genetic, genomic, and phenotypic variants of corals. Restoration practitioners, in turn, maintain records on fragment collection, genet performance, outplanting location and survivorship. While each data set is important in its own right, collectively they can provide deeper insights into coral biology and better guide coral restoration endeavors – unfortunately, current data sets are siloed with limited ability to cross-mine information for deeper insights and hypothesis testing. Herein we present the Coral Sample Registry (CSR), an online resource that establishes the first step in integrating diverse coral restoration data sets. Developed in collaboration with academia, management agencies, and restoration practitioners in the South Florida area, the CSR centralizes information on sample collection events by issuing a unique accession number to each entry. Accession numbers can then be incorporated into existing and future data structures. Each accession number is unique and corresponds to a specific collection event of coral tissue, whether for research, archiving, or restoration purposes. As such the accession number can serve as the key to unlock the diversity of information related to that sample’s provenance and characteristics across any and all data structures that include the accession number field. The CSR is open-source and freely available to users, designed to be suitable for all coral species in all geographic regions. Our goal is that this resource will be adopted by researchers, restoration practitioners, and managers to efficiently track coral samples through all data structures and thus enable the unlocking of a broader array of insights.

scholarly journals Early results from the Whisper instrument on Cluster: an overview

2001 ◽  
Vol 19 (10/12) ◽  
pp. 1241-1258 ◽  
Author(s):  
P. M. E. Décréau ◽  
P. Fergeau ◽  
V. Krasnoselskikh ◽  
E. Le Guirriec ◽  
M. Lévêque ◽  
...  
Keyword(s):  
Electron Density ◽  
Small Scale ◽  
Data Sets ◽  
Total Density ◽  
Total Electron Density ◽  
Data Set ◽  
Total Electron ◽  
Density Data ◽  
Early Results ◽  
Natural Emissions

Abstract. The Whisper instrument yields two data sets: (i) the electron density determined via the relaxation sounder, and (ii) the spectrum of natural plasma emissions in the frequency band 2–80 kHz. Both data sets allow for the three-dimensional exploration of the magnetosphere by the Cluster mission. The total electron density can be derived unambiguously by the sounder in most magnetospheric regions, provided it is in the range of 0.25 to 80 cm-3 . The natural emissions already observed by earlier spacecraft are fairly well measured by the Whisper instrument, thanks to the digital technology which largely overcomes the limited telemetry allocation. The natural emissions are usually related to the plasma frequency, as identified by the sounder, and the combination of an active sounding operation and a passive survey operation provides a time resolution for the total density determination of 2.2 s in normal telemetry mode and 0.3 s in burst mode telemetry, respectively. Recorded on board the four spacecraft, the Whisper density data set forms a reference for other techniques measuring the electron population. We give examples of Whisper density data used to derive the vector gradient, and estimate the drift velocity of density structures. Wave observations are also of crucial interest for studying small-scale structures, as demonstrated in an example in the fore-shock region. Early results from the Whisper instrument are very encouraging, and demonstrate that the four-point Cluster measurements indeed bring a unique and completely novel view of the regions explored.Key words. Space plasma physics (instruments and techniques; discontinuities, general or miscellaneous)

scholarly journals Image Processing of SEM Image Nano Silver Using K-means MATLAB Technique

2019 ◽  
Vol 29 (3) ◽  
pp. 150 ◽  
Author(s):  
Elham Jasim Mohammad
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Sem Image ◽  
Huge Data ◽  
And Performance ◽  
Biometric Information

Nanotechnology is one of the non-exhaustive applications in which image processing is used. For optimal nanoparticle visualization and characterization, the high resolution Scanning Electron Microscope (SEM) and the Atomic Force Microscope (AFM) are used. Image segmentation is one of the critical steps in nanoscale processing. There are also different ways to reach retail, including statistical approximations.In this study; we used the K-means method to determine the optimal threshold using statistical approximation. This technique is thoroughly studied for the SEM nanostructure Silver image. Note that, the image obtained by SEM is good enough to analyze more recently images. The analysis is being used in the field of nanotechnology. The K-means algorithm classifies the data set given to k groups based on certain measurements of certain distances. K-means technology is the most widely used among all clustering algorithms. It is one of the common techniques used in statistical data analysis, image analysis, neural networks, classification analysis and biometric information. K-means is one of the fastest collection algorithms and can be easily used in image segmentation. The results showed that K-means is highly sensitive to small data sets and performance can degrade at any time. When exposed to a huge data set such as 100.000, the performance increases significantly. The algorithm also works well when the number of clusters is small. This technology has helped to provide a good performance algorithm for the state of the image being tested.

Accelerate proposal generation in R-CNN methods for fast pedestrian extraction

2019 ◽  
Vol 37 (3) ◽  
pp. 435-453
Author(s):  
Juncheng Wang ◽  
Guiying Li
Keyword(s):  
Object Detection ◽  
Pedestrian Detection ◽  
Data Preprocessing ◽  
Detection Task ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
Novel Approach ◽  
General Object ◽  
And Performance

Purpose The purpose of this study is to develop a novel region-based convolutional neural networks (R-CNN) approach that is more efficient while at least as accurate as existing R-CNN methods. In this way, the proposed method, namely R2-CNN, provides a more powerful tool for pedestrian extraction for person re-identification, which involve a huge number of images and pedestrian needs to be extracted efficiently to meet the real-time requirement. Design/methodology/approach The proposed R2-CNN is tested on two types of data sets. The first one the USC Pedestrian Detection data set, which consists of three sub-sets USC-A, UCS-B and USC-C, with respect to their characteristics. This data set is used to test the performance of R2-CNN in the pedestrian extraction task. The speed and performance of the investigated algorithms were collected. The second data set is the PASCAL VOC 2007 data set, which is a common benchmark data set for object detection. This data set was used to analyze characteristics of R2-CNN in the case of general object detection task. Findings This study proposes a novel R-CNN method that is both more efficient and more accurate than existing methods. The method, when used as an object detector, would facilitate the data preprocessing stage of person re-identification. Originality/value The study proposes a novel approach for object detection, which shows advantages in both efficiency and accuracy for pedestrian detection task. It contributes to both data preprocessing for person re-identification and the research on deep learning.

A manufacturing systems network model for the evaluation of complex manufacturing systems

2014 ◽  
Vol 63 (3) ◽  
pp. 324-340 ◽  
Author(s):  
Till Becker ◽  
Mirja Meyer ◽  
Katja Windt
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Network Theory ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Sets ◽  
Manufacturing Companies ◽  
Content Type ◽  
Complex Network Theory ◽  
And Performance

Purpose – The topology of manufacturing systems is specified during the design phase and can afterwards only be adjusted at high expense. The purpose of this paper is to exploit the availability of large-scale data sets in manufacturing by applying measures from complex network theory and from classical performance evaluation to investigate the relation between structure and performance. Design/methodology/approach – The paper develops a manufacturing system network model that is composed of measures from complex network theory. The analysis is based on six company data sets containing up to half a million operation records. The paper uses the network model as a straightforward approach to assess the manufacturing systems and to evaluate the impact of topological measures on fundamental performance figures, e.g., work in process or lateness. Findings – The paper able to show that the manufacturing systems network model is a low-effort approach to quickly assess a manufacturing system. Additionally, the paper demonstrates that manufacturing networks display distinct, non-random network characteristics on a network-wide scale and that the relations between topological and performance key figures are non-linear. Research limitations/implications – The sample consists of six data sets from Germany-based manufacturing companies. As the model is universal, it can easily be applied to further data sets from any industry. Practical implications – The model can be utilized to quickly analyze large data sets without employing classical methods (e.g. simulation studies) which require time-intensive modeling and execution. Originality/value – This paper explores for the first time the application of network figures in manufacturing systems in relation to performance figures by using real data from manufacturing companies.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

Sign in / Sign up

Export Citation Format

Share Document