scholarly journals Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3527
Author(s):  
Melanija Vezočnik ◽  
Roman Kamnik ◽  
Matjaz B. Juric
Keyword(s):  
Principal Component Analysis ◽  
Human Body ◽  
Inertial Sensor ◽  
Principal Component ◽  
Step Length ◽  
Component Analysis ◽  
Indoor Positioning ◽  
Estimation Model ◽  
Length Estimation ◽  
Proposed Model

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.

scholarly journals Facial Landmarks Detection on Faulty Datasets with Regression Trees and Principal Component Analysis Parametrization

2018 ◽  
Author(s):  
Abner S. Nascimento ◽  
Danilo A. Oliveira ◽  
Maria Raquel L. De Couto ◽  
Iális C. De Paula Júnior
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Regression Trees ◽  
Component Analysis ◽  
Parametric Models ◽  
Essential Step ◽  
Proposed Model ◽  
Combined Use ◽  
Regression Algorithms ◽  
Missing Labels

Tracking landmarks points of the human face is an essential step for the construction of interfaces capable of taking advantage of the communicative potential of facial expressions. Many strategies based on parametric models and regression algorithms with boosting can be applied to this problem. This paper proposes a solution based on the combined use of principal component analysis and regression trees. The main purpose of the presented method is to reduce the sensibility of the system to the presence of missing labels when trained with faulty datasets, by the adoption of corrective heuristics. On such cases, the proposed model achieves performance similar to the reference results, obtained by training on fault free datasets.

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

2013 ◽  
Vol 5 (4) ◽  
pp. 82-94 ◽  
Author(s):  
A. F. M. Saifuddin Saif ◽  
Anton Satria Prabuwono ◽  
Zainal Rasyid Mahayuddin ◽  
Teddy Mantoro
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Personal Identification ◽  
Human Face ◽  
Analysis Model ◽  
Human Face Recognition ◽  
Proposed Model ◽  
The Face

Face recognition has been used in various applications where personal identification is required. Other methods of person's identification and verification such as iris scan and finger print scan require high quality and costly equipment. The objective of this research is to present an extended principal component analysis model to recognize a person by comparing the characteristics of the face to those of new individuals for different dimension of face image. The main focus of this research is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background is constant. This research requires a normal camera giving a 2-D frontal image of the person that will be used for the process of the human face recognition. An Extended Principal Component Analysis (EPCA) technique has been used in the proposed model of face recognition. Based on the experimental results it is expected that proposed the EPCA performs well for different face images when a huge number of training images increases computation complexity in the database.

scholarly journals Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Haifan Liu ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Independent Component Analysis ◽  
Stock Market ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Chinese Stock Market ◽  
Proposed Model ◽  
Bp Model

We investigate the statistical behaviors of Chinese stock market fluctuations by independent component analysis. The independent component analysis (ICA) method is integrated into the neural network model. The proposed approach uses ICA method to analyze the input data of neural network and can obtain the latent independent components (ICs). After analyzing and removing the IC that represents noise, the rest of ICs are used as the input of neural network. In order to forect the fluctuations of Chinese stock market, the data of Shanghai Composite Index is selected and analyzed, and we compare the forecasting performance of the proposed model with those of common BP model integrating principal component analysis (PCA) and single BP model. Experimental results show that the proposed model outperforms the other two models no matter in relatively small or relatively large sample, and the performance of BP model integrating PCA is closer to that of the proposed model in relatively large sample. Further, the prediction results on the points where the prices fluctuate violently by the above three models relatively deviate from the corresponding real market data.

A Model of Aircraft Support Concept Evaluation Based on DEA and PCA

2018 ◽  
Vol 6 (6) ◽  
pp. 563-576 ◽  
Author(s):  
Bin Lin ◽  
Dong Song ◽  
Zhiyue Liu
Keyword(s):  
Principal Component Analysis ◽  
Data Envelopment Analysis ◽  
Manufacturing Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Data Envelopment ◽  
Proposed Model ◽  
Concept Evaluation ◽  
Equipment Manufacturing ◽  
Complex Equipment

Abstract With the vigorous development of equipment manufacturing industry in China, higher requirements to the equipment supportability are put forward. How to evaluate the supportability of equipments (especially the aviation equipment-aircraft) objectively and correctly is the problem to be solved in the development of aviation equipments construction, demonstration and battle application. Aimed at the needs of the supportability analysis of complex equipment systems-aircraft, a model of aircraft support concept evaluation based on DEA (data envelopment analysis) and PCA (principal component analysis) is proposed. The model is used to evaluate a certain aircraft support concept. The process and the results of evaluation show that proposed model is feasible and effective. The model is suitable for advanced aircraft support concept evaluation. The feasibility and effectiveness of the proposed model is verified by the analysis of the evaluation results. This method is applicable to the evaluation of aircraft support concepts.

scholarly journals Assessment of coal pillar stability using principal component analysis and stepwise selection and elimination

2021 ◽  
Vol 69 (3) ◽  
pp. 81
Author(s):  
Brijesh Kumar ◽  
Punit Paurush ◽  
Sanjay K. Sharma ◽  
Gauri S. Prasad Singh
Keyword(s):  
Principal Component Analysis ◽  
Underground Mining ◽  
Coal Pillar ◽  
Principal Component ◽  
Component Analysis ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Pillar Stability ◽  
Stepwise Selection ◽  
Proposed Model

Prediction of pillar stability is one of the most critical tasks in underground mining industries. This pillar stability analysis requires many input parameters and some of them are difficult to be determined. Various statistical based analysis is presented in literature for assessing pillar stability successfully. In the present work, the data from three mines had been to determine the factor of safety. A total of 63 pillar cases had been collected from the mines. Principal component analysis (PCA) and Stepwise selection and elimination (SSE) models were developed by using multi variate linear regression (MLR) on 45 data sets and subsequently the proposed models were validated on 18 different data sets. The value of coefficient of determination (R2) is 0.86 and 0.84 for PCA and SSE respectively. The root mean square error for PCA and SSE are found to be 0.112 and 0.123 respectively. On validation of the proposed model developed by PCA and SSE, the PCA model provided a better validation results. Hence, PCA is recommended for modelling pillar stability.

scholarly journals Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 43 ◽  
Author(s):  
Zhong Li ◽  
Xiaorong Guan ◽  
Kaifan Zou ◽  
Cheng Xu
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Sample Size ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Surface Emg ◽  
Joint Movement ◽  
Estimation Model ◽  
Knee Movement

To study the relationship between surface electromyography (sEMG) and joint movement, and to provide reliable reference information for the exoskeleton control, the sEMG and the corresponding movement of the knee during the normal walking of adults have been measured. After processing the experimental data, the estimation model for knee movement from sEMG was established using the novel method of random forest with principal component analysis (RFPCA). The influence of the sample size and the previous sEMG data on the prediction efficiency was analyzed. The estimation model was not sensitive to the sample size when samples increased to a certain value, and the results of different previous sEMG showed that the prediction accuracy of the estimation models did not always improve with the increasing features of input. By comparing the estimation model of back propagation neural network with principal component analysis (BPPCA), it was found that RFPCA was suitable for all participants in the experiment with less execution time, and the root mean square error was around 5° which was lower than BPPCA with errors varying from 7° to 25°. Therefore, it was concluded that the RFPCA method for the estimation of knee movement from sEMG is feasible and could be used for motion analysis and the control of exoskeleton.

scholarly journals An Efficient Method to Improve the Clustering Performance using Hybrid Robust Principal Component Analysis-Spectral biclustering in Rainfall Patterns Identification

2019 ◽  
Vol 8 (3) ◽  
pp. 237
Author(s):  
Shazlyn Milleana Shaharudin ◽  
Shuhaida Ismail ◽  
Siti Mariana Che Mat Nor ◽  
Norhaiza Ahmad
Keyword(s):  
Principal Component Analysis ◽  
Data Center ◽  
Principal Component ◽  
Small Variation ◽  
Component Analysis ◽  
Peninsular Malaysia ◽  
Robust Pca ◽  
Robust Principal Component Analysis ◽  
Proposed Model ◽  
Rainfall Patterns

<p>In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and bi-clustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhibit a high correlation compared to one-way cluster analysis. The experimental results showed that the best cumulative percentage of variation in between 65% - 70% for both Robust and classical PCA. Meanwhile, the number of clusters has improved from six disjointed cluster in Robust PCA-kMeans to eight disjointed cluster for the proposed model. Further analysis shows that the proposed model has smaller variation with the values of 0.0034 compared to 0.030 in Robust PCA-kMeans model. Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation of the clustering result.</p>

scholarly journals A New Early Stage Diabetic Retinopathy Diagnosis Model Using Deep Convolutional Neural Networks and Principal Component Analysis

2020 ◽  
Vol 37 (5) ◽  
pp. 711-722
Author(s):  
Mali Mohammedhasan ◽  
Harun Uğuz
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
World Health ◽  
Diabetic Patients ◽  
Proposed Model ◽  
Diagnosis Accuracy

Diabetic retinopathy (DR) is a disease of the retina, which leads over time to vision problems such retinal detachment, vitreous hemorrhage, glaucoma, and in worse cases leads to blindness, which can initially be controlled by periodic DR-screening. Early diagnosis will lead to greater control of the disease, whereas performing retinal examinations on all diabetic patients is an unattainable need, as diabetes is a chronic disease and its global prevalence has been steadily increasing over the past few decades. According to recent World Health Organization statistics, about 422 million people worldwide have diabetes, the majority living in low-and middle-income countries. This paper proposes a new strategy that brings the strength of convolutional neural networks (CNNs) to the diagnosis of DR. Coupled with using principal component analysis (PCA) that performs dimension reduction to improve the diagnostic accuracy, the proposed model exploiting edge-preserving guided image filtering (E-GIF) that performs as a contrast enhancement mechanism, and in addition to smoothing low gradient areas, it also accentuates strong edges. Diabetic retinopathy causes progressive damage to the blood vessels in the retina to the extent that it leaves traces and lesions in the tissues of the retina. These lesions appear in the form of edges and when processing retinal images, we seek to accentuate these edges to enable better diagnosis of diabetic retinopathy symptoms. A new CNN architecture with residual connections is used, which performs very well in diagnosing DR. The proposed model is named with RUnet-PCA: Residual U-net Deep CNN with Principal Component Analysis. The well-known AlexNet, VggNet-s, VggNet-16, VggNet-19, GoogleNet, and ResNet models were adopted for comparison with the proposed model. Publicly available Kaggle dataset was employed for training exploring the DR diagnosis accuracy. Experimental results show that the proposed RUnet-PCA model achieved a diagnosis accuracy of 98.44% and it was extremely robust and promising in comparison to other diagnosis methods.

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