Irene Yu-Hua Gu
Bio: Irene Yu-Hua Gu is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Video tracking & Image segmentation. The author has an hindex of 33, co-authored 209 publications receiving 5789 citations. Previous affiliations of Irene Yu-Hua Gu include Eindhoven University of Technology & Lund University.
Papers published on a yearly basis
TL;DR: Quantitative evaluation and comparison show that the proposed Bayesian framework for foreground object detection in complex environments provides much improved results.
Abstract: This paper addresses the problem of background modeling for foreground object detection in complex environments. A Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance is proposed. Under this framework, the background is represented by the most significant and frequent features, i.e., the principal features , at each pixel. A Bayes decision rule is derived for background and foreground classification based on the statistics of principal features. Principal feature representation for both the static and dynamic background pixels is investigated. A novel learning method is proposed to adapt to both gradual and sudden "once-off" background changes. The convergence of the learning process is analyzed and a formula to select a proper learning rate is derived. Under the proposed framework, a novel algorithm for detecting foreground objects from complex environments is then established. It consists of change detection, change classification, foreground segmentation, and background maintenance. Experiments were conducted on image sequences containing targets of interest in a variety of environments, e.g., offices, public buildings, subway stations, campuses, parking lots, airports, and sidewalks. Good results of foreground detection were obtained. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results.
01 Jan 2006
TL;DR: In this article, the authors present an overview of machine learning methods for event classification of power system events and their application in the context of power quality measurement and power quality metrics, such as voltage variation, frequency domain analysis and signal transformation.
Abstract: PREFACE. ACKNOWLEDGMENTS. 1 INTRODUCTION. 1.1 Modern View of Power Systems. 1.2 Power Quality. 1.3 Signal Processing and Power Quality. 1.4 Electromagnetic Compatibility Standards. 1.5 Overview of Power Quality Standards. 1.6 Compatibility Between Equipment and Supply. 1.7 Distributed Generation. 1.8 Conclusions. 1.9 About This Book. 2 ORIGIN OF POWER QUALITY VARIATIONS. 2.1 Voltage Frequency Variations. 2.2 Voltage Magnitude Variations. 2.3 Voltage Unbalance. 2.4 Voltage Fluctuations and Light Flicker. 2.5 Waveform Distortion. 2.6 Summary and Conclusions. 3 PROCESSING OF STATIONARY SIGNALS. 3.1 Overview of Methods. 3.2 Parameters That Characterize Variations. 3.3 Power Quality Indices. 3.4 Frequency-Domain Analysis and Signal Transformation. 3.5 Estimation of Harmonics and Interharmonics. 3.6 Estimation of Broadband Spectrum. 3.7 Summary and Conclusions. 3.8 Further Reading. 4 PROCESSING OF NONSTATIONARY SIGNALS. 4.1 Overview of Some Nonstationary Power Quality Data Analysis Methods. 4.2 Discrete STFT for Analyzing Time-Evolving Signal Components. 4.3 Discrete Wavelet Transforms for Time-Scale Analysis of Disturbances. 4.4 Block-Based Modeling. 4.5 Models Directly Applicable to Nonstationary Data. 4.6 Summary and Conclusion. 4.7 Further Reading. 5 STATISTICS OF VARIATIONS. 5.1 From Features to System Indices. 5.2 Time Aggregation. 5.3 Characteristics Versus Time. 5.4 Site Indices. 5.5 System Indices. 5.6 Power Quality Objectives. 5.7 Summary and Conclusions. 6 ORIGIN OF POWER QUALITY EVENTS. 6.1 Interruptions. 6.2 Voltage Dips. 6.3 Transients. 6.4 Summary and Conclusions. 7 TRIGGERING AND SEGMENTATION. 7.1 Overview of Existing Methods. 7.2 Basic Concepts of Triggering and Segmentation. 7.3 Triggering Methods. 7.4 Segmentation. 7.5 Summary and Conclusions. 8 CHARACTERIZATION OF POWER QUALITY EVENTS. 8.1 Voltage Magnitude Versus Time. 8.2 Phase Angle Versus Time. 8.3 Three-Phase Characteristics Versus Time. 8.4 Distortion During Event. 8.5 Single-Event Indices: Interruptions. 8.6 Single-Event Indices: Voltage Dips. 8.7 Single-Event Indices: Voltage Swells. 8.8 Single-Event Indices Based on Three-Phase Characteristics. 8.9 Additional Information from Dips and Interruptions. 8.10 Transients. 8.11 Summary and Conclusions. 9 EVENT CLASSIFICATION. 9.1 Overview of Machine Data Learning Methods for Event Classification. 9.2 Typical Steps Used in Classification System. 9.3 Learning Machines Using Linear Discriminants. 9.4 Learning and Classification Using Probability Distributions. 9.5 Learning and Classification Using Artificial Neural Networks. 9.6 Learning and Classification Using Support Vector Machines. 9.7 Rule-Based Expert Systems for Classification of Power System Events. 9.8 Summary and Conclusions. 10 EVENT STATISTICS. 10.1 Interruptions. 10.2 Voltage Dips: Site Indices. 10.3 Voltage Dips: Time Aggregation. 10.4 Voltage Dips: System Indices. 10.5 Summary and Conclusions. 11 CONCLUSIONS. 11.1 Events and Variations. 11.2 Power Quality Variations. 11.3 Power Quality Events. 11.4 Itemization of Power Quality. 11.5 Signal-Processing Needs. APPENDIX A IEC STANDARDS ON POWER QUALITY. APPENDIX B IEEE STANDARDS ON POWER QUALITY. BIBLIOGRAPHY. INDEX.
••02 Nov 2003
TL;DR: A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated and the convergence of the learning process is proved and a formula to select a proper learning rate is also derived.
Abstract: This paper proposes a novel method for detection and segmentation of foreground objects from a video which contains both stationary and moving background objects and undergoes both gradual and sudden "once-off" changes. A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated. Under this rule, different types of background objects will be classified from foreground objects by choosing a proper feature vector. The stationary background object is described by the color feature, and the moving background object is represented by the color co-occurrence feature. Foreground objects are extracted by fusing the classification results from both stationary and moving pixels. Learning strategies for the gradual and sudden "once-off" background changes are proposed to adapt to various changes in background through the video. The convergence of the learning process is proved and a formula to select a proper learning rate is also derived. Experiments have shown promising results in extracting foreground objects from many complex backgrounds including wavering tree branches, flickering screens and water surfaces, moving escalators, opening and closing doors, switching lights and shadows of moving objects.
TL;DR: An expert system is presented that is able to classify different types of power system events to the underlying causes and offer useful information in terms of power quality and enables fast and accurate analysis of data from power quality monitors.
Abstract: This paper presents an expert system that is able to classify different types of power system events to the underlying causes (i.e., events) and offer useful information in terms of power quality. The expert system uses the voltage waveforms and distinguishes the different types of voltage dips (fault-induced, transformer saturation, induction motor starting), as well as interruptions (nonfault, fault induced). A method for event-based classification is used, where a segmentation algorithm is first applied to divide waveforms into several possible events. The expert system is tested using real measurements and the results show that the system enables fast and accurate analysis of data from power quality monitors.
TL;DR: A novel SVM classification system for voltage disturbances with high accuracy in classification with training data from one power network and unseen testing data from another and lower accuracy when the SVM classifier was trained on synthetic data and test data originated from the power network.
Abstract: The support vector machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pretrained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. This paper proposes a novel SVM classification system for voltage disturbances. The performance of the proposed SVM classifier is investigated when the voltage disturbance data used for training and testing originated from different sources. The data used in the experiments were obtained from both real disturbances recorded in two different power networks and from synthetic data. The experimental results shown high accuracy in classification with training data from one power network and unseen testing data from another. High accuracy was also achieved when the SVM classifier was trained on data from a real power network and test data originated from synthetic data. A lower accuracy resulted when the SVM classifier was trained on synthetic data and test data originated from the power network.
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
01 Jan 2015
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
TL;DR: In this paper, the authors prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm.
Abstract: This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individuallyq We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.