662 research outputs found
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging to the whole infrastructure (e.g., cables and related
insulation, transformers, breakers and so on). In real-world smart grid
systems, usually, additional information that are related to the operational
status of the grid itself are collected such as meteorological information.
Designing a suitable recognition (discrimination) model of faults in a
real-world smart grid system is hence a challenging task. This follows from the
heterogeneity of the information that actually determine a typical fault
condition. The second point is that, for synthesizing a recognition model, in
practice only the conditions of observed faults are usually meaningful.
Therefore, a suitable recognition model should be synthesized by making use of
the observed fault conditions only. In this paper, we deal with the problem of
modeling and recognizing faults in a real-world smart grid system, which
supplies the entire city of Rome, Italy. Recognition of faults is addressed by
following a combined approach of multiple dissimilarity measures customization
and one-class classification techniques. We provide here an in-depth study
related to the available data and to the models synthesized by the proposed
one-class classifier. We offer also a comprehensive analysis of the fault
recognition results by exploiting a fuzzy set based reliability decision rule
Three-way Imbalanced Learning based on Fuzzy Twin SVM
Three-way decision (3WD) is a powerful tool for granular computing to deal
with uncertain data, commonly used in information systems, decision-making, and
medical care. Three-way decision gets much research in traditional rough set
models. However, three-way decision is rarely combined with the currently
popular field of machine learning to expand its research. In this paper,
three-way decision is connected with SVM, a standard binary classification
model in machine learning, for solving imbalanced classification problems that
SVM needs to improve. A new three-way fuzzy membership function and a new fuzzy
twin support vector machine with three-way membership (TWFTSVM) are proposed.
The new three-way fuzzy membership function is defined to increase the
certainty of uncertain data in both input space and feature space, which
assigns higher fuzzy membership to minority samples compared with majority
samples. To evaluate the effectiveness of the proposed model, comparative
experiments are designed for forty-seven different datasets with varying
imbalance ratios. In addition, datasets with different imbalance ratios are
derived from the same dataset to further assess the proposed model's
performance. The results show that the proposed model significantly outperforms
other traditional SVM-based methods
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Texture spectrum coupled with entropy and homogeneity image features for myocardium muscle characterization
People in middle/later age often suffer from heart muscle damage due to coronary artery disease associated to myocardial infarction. In young people, the genetic forms of cardiomyopathies (heart muscle disease) are the utmost protuberant cause of myocardial disease. Accurate early detected information regarding the myocardial tissue structure is a key answer for tracking the progress of several myocardial diseases. The present work proposes a new method for myocardium muscle texture classification based on entropy, homogeneity and on the texture unit-based texture spectrum approaches. Entropy and homogeneity are generated in moving windows of size 3x3 and 5x5 to enhance the texture features and to create the premise of differentiation of the myocardium structures. Texture is then statistically analyzed using the texture spectrum approach. Texture classification is achieved based on a fuzzy c–means descriptive classifier. The noise sensitivity of the fuzzy c–means classifier is overcome by using the image features. The proposed method is tested on a dataset of 80 echocardiographic ultrasound images in both short-axis and long-axis in apical two chamber view representations, for normal and infarct pathologies. The results established that the entropy-based features provided superior clustering results compared to homogeneity
Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values
This work is motivated by the needs of predictive analytics on healthcare
data as represented by Electronic Medical Records. Such data is invariably
problematic: noisy, with missing entries, with imbalance in classes of
interests, leading to serious bias in predictive modeling. Since standard data
mining methods often produce poor performance measures, we argue for
development of specialized techniques of data-preprocessing and classification.
In this paper, we propose a new method to simultaneously classify large
datasets and reduce the effects of missing values. It is based on a multilevel
framework of the cost-sensitive SVM and the expected maximization imputation
method for missing values, which relies on iterated regression analyses. We
compare classification results of multilevel SVM-based algorithms on public
benchmark datasets with imbalanced classes and missing values as well as real
data in health applications, and show that our multilevel SVM-based method
produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625
On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks.
Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residuals—to decrease the likelihood of false alarms—is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach
A Survey of Neural Trees
Neural networks (NNs) and decision trees (DTs) are both popular models of
machine learning, yet coming with mutually exclusive advantages and
limitations. To bring the best of the two worlds, a variety of approaches are
proposed to integrate NNs and DTs explicitly or implicitly. In this survey,
these approaches are organized in a school which we term as neural trees (NTs).
This survey aims to present a comprehensive review of NTs and attempts to
identify how they enhance the model interpretability. We first propose a
thorough taxonomy of NTs that expresses the gradual integration and
co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their
interpretability and performance, and suggest possible solutions to the
remaining challenges. Finally, this survey concludes with a discussion about
other considerations like conditional computation and promising directions
towards this field. A list of papers reviewed in this survey, along with their
corresponding codes, is available at:
https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl
3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition
The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives.
As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications.
This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new “CoDe4D” features that incorporate both color and depth information, a new “SOD” descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis
Much like the classical Fisher linear discriminant analysis, Wasserstein
discriminant analysis (WDA) is a supervised linear dimensionality reduction
method that seeks a projection matrix to maximize the dispersion of different
data classes and minimize the dispersion of same data classes. However, in
contrast, WDA can account for both global and local inter-connections between
data classes using a regularized Wasserstein distance. WDA is formulated as a
bi-level nonlinear trace ratio optimization. In this paper, we present a
bi-level nonlinear eigenvector (NEPv) algorithm, called WDA-nepv. The inner
kernel of WDA-nepv for computing the optimal transport matrix of the
regularized Wasserstein distance is formulated as an NEPv, and meanwhile the
outer kernel for the trace ratio optimization is also formulated as another
NEPv. Consequently, both kernels can be computed efficiently via
self-consistent-field iterations and modern solvers for linear eigenvalue
problems. Comparing with the existing algorithms for WDA, WDA-nepv is
derivative-free and surrogate-model-free. The computational efficiency and
applications in classification accuracy of WDA-nepv are demonstrated using
synthetic and real-life datasets
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