PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Abstract

This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine-(SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multiple sensing devices when concurrently detecting different patterns. Therefore, to resolve this issue, this study proposes a novel framework for establishing indicators and corresponding thresholds to identify patterns in the system; it employs a feature-based scheme that integrates principal component analysis (PCA) with an SVM for effectively detecting patterns in the system. Experiments were conducted using a tactile and optical measurement system. The experimental results demonstrated that the proposed method can effectively identify patterns in multisensor systems by using a feature-based algorithm that combines PCA and SVM classification for detecting various patterns. Moreover, the proposed framework established alarm indicators and corresponding thresholds that can be used for pattern detection.

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry

“…The combined application of PCA and SVM was reported in previous studies for the classification of groups with high dimensionality. 23,24 The prepared SVM model was evaluated for its ability to classify the spectra from tumor and normal tissues based on several parameters, including a ROC curve. Table S2 provides a summary of the evaluation results.…”

Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

“…The algorithm can engage in the dynamic selection of suitable feature extraction schemes for hybrid object classification and detection. A previously executed study also proposed a PCA/SVMbased approach for identifying multivariate patterns from tactile and optical measurements in a system with multiple sensors [16]. The approach uses a PCA-based method and an algorithm based on edge feature description (EFD) to detect patterns from tactile and optical measurements, respectively.…”

“…The approach uses a PCA-based method and an algorithm based on edge feature description (EFD) to detect patterns from tactile and optical measurements, respectively. Other studies have detected objects using an image segmenting technique [14], a feature scheme selection algorithm [15], and a tactile and optical measurement scheme [16]. In contrast to the aforementioned studies, the current study proposes a PCA-integrated algorithm for identifying suitable image features for effectively detecting objects.…”

“…Features. The PCA and feature extraction-based algorithm modifies a previously derived PCA-based method [16] to decorrelate image extraction data for identifying suitable features. The previously developed PCA-based method analyzes only vibration-sensed data for object detection.…”

“…The SVM model has been used for classifying the segmented image data [14], feature selection data [15], and vibration/image sensed data [16]. The present study employed the SVM model to classify the suitable features.…”