Feature reduction using principal component analysis for agricultural data set

2011 ◽  
Author(s):  
Subhadra Mishra ◽  
Debahuti Mishra ◽  
Satyabrata Das ◽  
Amiya Kumar Rath
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Data Set

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

Design and Development of EMG Conditioning System and Hand Gesture Recognition Based on Principal Component Analysis Feature Reduction Technique

2014 ◽  
pp. 304-320
Author(s):  
P. Geethanjali
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Digital Signal ◽  
Component Analysis ◽  
Classification Performance ◽  
Feature Reduction ◽  
Design And Development ◽  
Emg Signal ◽  
Knn Classifier ◽  
Power Line Interference

This chapter discusses design and development of a surface Electromyogram (EMG) signal detection and conditioning system along with the issues of gratuitous spurious signals such as power line interference, artifacts, etc., which make signals plausible. In order to construe the recognition of hand gestures from EMG signals, Time Domain (TD) and well as Autoregressive (AR) coefficients features are extracted. The extracted features are diminished using the Principal Component Analysis (PCA) to alleviate the burden of the classifier. A four-channel continuous EMG signal conditioning system is developed and EMG signals are acquired from 10 able-bodied subjects to classify the 6 unique movements of hand and wrist. The reduced statistical TD and AR features are used to classify the signal patterns through k Nearest Neighbour (kNN) as well as Neural Network (NN) classifier. Further, EMG signals acquired from a transradial amputee using 8-channel systems for the 6 amenable motions are also classified. Statistical Analysis of Variance (ANOVA) results on classification performance of able-bodied subject divulge that the performance TD-PCA features are more significant than the AR-PCA features. Further, no significant difference in the performance of NN classifier and kNN classifier is construed with TD reduced features. Since the average classification error of kNN classifier with TD features is found to be less, kNN classifier is implemented in off-line using the TMS2407eZdsp digital signal controller to study the actuation of three low-power DC drives in the identification of intended motion with an able-bodied subject.

Comparisons of Environmental Ordinations with Principal Component Vegetational Ordinations for Sets of Data Having Different Degrees of Complexity

1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Two Dimensional ◽  
High Complexity ◽  
Water Conductivity ◽  
Data Set ◽  
Total Data

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).

Evaluation of Feature Reduction using Principal Component Analysis and Sequential Pattern Matching for Manet

2017 ◽  
Vol 7 (3) ◽  
pp. 1228
Author(s):  
M. Reji ◽  
P.C. Kishore Raja ◽  
Bhagyalakshmi M
Keyword(s):  
Principal Component Analysis ◽  
Ad Hoc ◽  
Pattern Mining ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Sequential Pattern ◽  
Basic Part ◽  
Information Set ◽  
Hoc Networks

In Mobile Ad hoc Networks (MANETs) there are some security problems because of portability, element topology changes, and absence of any framework. In MANETs, it is of extraordinary significance to identify inconsistency and malignant conduct. With a specific end goal to recognize malignant assaults by means of interruption identification frameworks and dissect the information set, we have to choose some components. Thus, highlight determination assumes basic part in recognizing different assaults. In the writing, there are a few recommendations to choose such elements. For the most part, Principal Component Analysis (PCA) breaks down the information set and the chose highlights. In this paper, we have gathered a list of capabilities from some cutting edge works in the writing. Really, our reproduction demonstrates this list of capabilities identify inconsistency conduct more precise. Likewise, interestingly, we utilize PCA for investigating the information set. In contrast to PCA, our results show Sequential pattern mining (SPM) cannot be affected by outlier data within the network. The  normal and attack states are simulated and the results are analyzed using NS2 simulator.

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