Interpretation of Raman Spectra of Nitro-Containing Explosive Materials. Part II: The Implementation of Neural, Fuzzy, and Statistical Models for Unsupervised Pattern Recognition

1997 ◽  
Vol 51 (12) ◽  
pp. 1868-1879 ◽  
Author(s):  
Nelson W. Daniel ◽  
Ian R. Lewis ◽  
Peter R. Griffiths
Keyword(s):  
Pattern Recognition ◽  
Raman Spectra ◽  
Fuzzy Clustering ◽  
Statistical Models ◽  
Structural Information ◽  
A Priori ◽  
Data Set ◽  
Pattern Recognition Techniques ◽  
Explosive Materials ◽  
Neural Fuzzy

The implementation of neural, fuzzy, and statistical models for the unsupervised pattern recognition and clustering of Fourier transform (FT)-Raman spectra of explosive materials is reported. In this work a statistical pattern recognition technique based on the concept of nearest-neighbors classification is described. Also the first application of both fuzzy clustering and a fuzzified Kohonen clustering network for the analysis of vibrational spectra is presented. Fuzzified Kohonen networks were found to perform as well as or better than the traditional fuzzy clustering technique. The unsupervised pattern recognition techniques, without the need for a priori structural information, yielded results which were comparable with those obtained by using a combination of a priori structural information and manual group-frequency analysis. This work demonstrates, via the use of a nitro-containing explosive data set, the utility of unsupervised pattern recognition techniques for the clustering, novelty detection, prototyping, and feature mapping of Raman spectra. The results of this work are directly applicable to the characterization of Raman spectra of explosives recorded with fiber-optic sampling.

scholarly journals Implementasi Algoritma Apriori Terhadap Data Penjualan Oli Mobil Berbasis Sistem Pencarian Aturan Asosiasi

2019 ◽  
Vol 3 (2) ◽  
pp. 316
Author(s):  
Jorza Rulianto ◽  
Wida Prima Mustika
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Software Testing ◽  
A Priori ◽  
Marketing Strategies ◽  
Motor Oil ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Pattern Recognition Techniques ◽  
Mathematical Techniques

Data mining techniques are used to design effective sales or marketing strategies by utilizing sales transaction data that is already available in the company. The problem in the company is that there are many data transactions that occur unknown, causing an accumulation of data unknown sales most in each month & year, unknown brands of car oil are often sold or demanded by customers. So this association search uses a priori algorithm as a place to store data using pattern recognition techniques such as static and mathematical techniques from a set of relationships (associations) between items obtained, it is expected that can help developers in designing marketing strategies for goods in the company. Software testing results that have been made have found the most sold oil brand products if you buy Shell Hx7, it will buy Toyota Motor Oil with 50% support and 66.7% confidence. If you buy Toyota Motor Oil, you will buy Shell Hx 7 with 50% support and 85.7% confidence.

Comparison of Pattern Recognition Techniques for Sample Classification Using Elemental Composition: Applications for ICP-AES

1995 ◽  
Vol 49 (7) ◽  
pp. 964-970 ◽  
Author(s):  
Wayne Branagh ◽  
Huinan Yu ◽  
Eric D. Salin
Keyword(s):  
Pattern Recognition ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Nearest Neighbors ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Pattern Recognition Techniques ◽  
Sample Classification ◽  
Relative Standard

Pattern recognition is very important for many aspects of data analysis and robotic control. Three pattern recognition techniques were examined— k-Nearest Neighbors, Bayesian analysis, and the C4.5 inductive learning algorithm. Their abilities to classify 71 different reference materials were compared. Each training and test example consisted of 79 different elemental concentrations. Different data sets were generated with relative standard deviations of 1, 3, 5, 10, 30, 100, and 500%. Each data set consisted of 2000 examples. These sets were used in both the training stages and in the test stages. It was found that C4.5's inductive learning algorithm had a higher classification accuracy than either Bayesian or k-Nearest Neighbors techniques, especially when large amounts of noise were present in the systems.

Pattern-recognition image processing of 3D atom-probe data

1994 ◽  
Vol 52 ◽  
pp. 832-833
Author(s):  
Patrick P. Camus ◽  
David J. Larson ◽  
Thomas F. Kelly
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Lattice Structure ◽  
A Priori ◽  
Three Dimensional ◽  
Empirical Work ◽  
Atom Probe ◽  
Theoretical Description ◽  
Pattern Recognition Techniques ◽  
Near Future

The ultimate three-dimensional atom probe (3DAP) system would have sufficient spatial resolution so that the crystal structure of a material could be determined directly from the atomic positions. Aberrations in the trajectories of ions evaporated from the specimen are the primary limitation on the lateral resolution of AP analysis. In the near future, it does not seem likely that these aberrationsmay be corrected physically because there is no theoretical description and there has been very little empirical work. If the lattice is known a priori, a suggestion was proposed to force the atoms to their nearest lattice sites. This work reports progress that has been made using Fourier transform (FT) and pattern recognition techniques to reconstruct an original lattice structure from simulated 3DAPdata and subsequently to force atoms to pick their nearest lattice point. Usually FT techniques areused in image processing to reduce the image noise, not actually to shift features in the image.

Identification of Infrasound Regimes at Mount Etna using Pattern Recognition Techniques

2021 ◽  
Author(s):  
Felix Eckel ◽  
Horst Langer ◽  
Mariangela Sciotto
Keyword(s):  
Pattern Recognition ◽  
Seismic Waves ◽  
Expert Knowledge ◽  
Amplitude Spectrum ◽  
Active Volcano ◽  
Mount Etna ◽  
Sound Waves ◽  
Color Code ◽  
Data Set ◽  
Pattern Recognition Techniques

<p>Mount Etna, Europe’s largest and most active volcano is situated close to the Metropolitan area of Catania with about 1 Million inhabitants. Continuous monitoring has therefore been carried out for decades. Among the various disciplines infrasound recordings play an important role in this context. Explosive activity near or above ground as well as shallow tremor processes are easier to identify with airborne sound waves than with seismic waves that are significantly scattered and refracted in the volcanic edifice. However, infrasound signals are often affected by noise, especially by wind noise in the summit area.</p><p>At Mount Etna five summit craters are currently known with fluctuating levels of activity. This leads to a wide variety of infrasound signal patterns interfered by changing noise levels. Manual distinction of noisy data from real volcanogenic signals brings along a considerable effort and requires expert knowledge. We therefore apply unsupervised pattern recognition techniques for this task. Extracting features from the amplitude spectrum we are able to distinguish different infrasound regimes with Self-Organizing maps (SOMs). SOMs allow to color-code the results for an intuitive interpretation and evidence the presence of transitional activity regimes. We define a reference data set from multiple months of infrasound waveforms to include as many activity regimes as possible to train the SOM. This enables a straight forward interpretation of new data.</p>

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

EEG-Based Emotion Recognition Using Different Neural Network and Pattern Recognition Techniques A Review

2019 ◽  
Vol 7 (1) ◽  
pp. 615-618
Author(s):  
Y. M. Rajput ◽  
S. Abdul Hannan ◽  
M. Eid Alzahrani ◽  
Ramesh R. Manza ◽  
Dnyaneshwari D. Patil
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Emotion Recognition ◽  
Pattern Recognition Techniques

Engagement Through the Diaspora Channel

2017 ◽  
Author(s):  
Michael S. Danielson
Keyword(s):  
Civil Society ◽  
Statistical Models ◽  
State Level ◽  
The State ◽  
Multivariate Statistical ◽  
Political Factors ◽  
Data Set ◽  
Level Data ◽  
Municipal Level

The first empirical task is to identify the characteristics of municipalities which US-based migrants have come together to support financially. Using a nationwide, municipal-level data set compiled by the author, the chapter estimates several multivariate statistical models to compare municipalities that did not benefit from the 3x1 Program for Migrants with those that did, and seeks to explain variation in the number and value of 3x1 projects. The analysis shows that migrants are more likely to contribute where migrant civil society has become more deeply institutionalized at the state level and in places with longer histories as migrant-sending places. Furthermore, the results suggest that political factors are at play, as projects have disproportionately benefited states and municipalities where the PAN had a stronger presence, with fewer occurring elsewhere.

scholarly journals Intra-Storm Pattern Recognition through Fuzzy Clustering

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 57
Author(s):  
Konstantinos Vantas ◽  
Epaminondas Sidiropoulos
Keyword(s):  
Fuzzy Clustering ◽  
A Priori ◽  
Temporal Distribution ◽  
Distribution Patterns ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Domain Specific ◽  
Runoff Modeling ◽  
National Bank ◽  
Rainfall Patterns

The identification and recognition of temporal rainfall patterns is important and useful not only for climatological studies, but mainly for supporting rainfall–runoff modeling and water resources management. Clustering techniques applied to rainfall data provide meaningful ways for producing concise and inclusive pattern classifications. In this paper, a timeseries of rainfall data coming from the Greek National Bank of Hydrological and Meteorological Information are delineated to independent rainstorms and subjected to cluster analysis, in order to identify and extract representative patterns. The computational process is a custom-developed, domain-specific algorithm that produces temporal rainfall patterns using common characteristics from the data via fuzzy clustering in which (a) every storm may belong to more than one cluster, allowing for some equivocation in the data, (b) the number of the clusters is not assumed known a priori but is determined solely from the data and, finally, (c) intra-storm and seasonal temporal distribution patterns are produced. Traditional classification methods include prior empirical knowledge, while the proposed method is fully unsupervised, not presupposing any external elements and giving results superior to the former.

Sign in / Sign up

Export Citation Format

Share Document