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Graphical Models in Meshed Distribution Grids: Topology Estimation, change detection & Limitations

IEEE TRANSACTIONS ON SMART GRID 2020年第5期11卷 4299-4310页

作者： Deka, Deepjyoti Talukdar, Saurav Chertkov, Michael Salapaka, Murti V. Los Alamos Natl Lab Los Alamos NM 87545 USA Univ Minnesota Coll Sci & Engn Minneapolis MN 55455 USA Univ Arizona Tucson AZ 85721 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

Graphical models is a succinct way to represent the structure of a probability distributions. This article analyzes the graphical model of nodal voltages in non-radial power distribution grids. Using algebraic and structural properties of graphical models, algorithms exactly determining topology and detecting line changes for distribution grids are presented along with their theoretical limitations. We show that if distribution grids have cycles/loops of size greater than three, then nodal voltages are sufficient for efficient topology estimation without additional assumptions on system parameters. In contrast, line failure or change detection using nodal voltages does not require any structural assumption. Under noisy measurements, we provide the first non-trivial bounds on the maximum noise that the system can tolerate for asymptotically correct topology recovery. The performance of the designed algorithms is validated with non-linear AC power flow samples generated by Matpower on test grids, including scenarios with injection correlations and system noise.

关键词： Topology Graphical models Voltage measurement Estimation change detection algorithms Noise measurement Mathematical model Concentration matrix conditional independence distribution grids graphical lasso graphical models power flows line outage measurement noise

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Automatic change detection in Sparse Repeat CT Scanning

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第1期39卷 48-61页

作者： Shamul, N. Joskowicz, L. Hebrew Univ Jerusalem Sch Comp Sci & Engn CASMIP Lab IL-9190401 Jerusalem Israel

We describe a new method for the automatic detection of changes in repeat CT scanning with a reduced X-ray radiation dose. We present a theoretical formulation of the automatic change detection problem based on the on-line sparse-view repeat CT scanning dose optimization framework. We prove that the change detection problem is NP-hard and therefore cannot be efficiently solved exactly. We describe a new greedy change detection algorithm that is simple and robust and relies on only two key parameters. We demonstrate that the greedy algorithm accurately detects small, low contrast changes with only 12 scan angles. Our experimental results show that the new algorithm yields a mean changed region recall rate > 89% and a mean precision rate > 76%. It outperforms both our previous heuristic approach and a thresholding method using a low-dose prior image-constrained compressed sensing (PICCS) reconstruction of the repeat scan. The resulting changed region map may obviate the need for a high-quality repeat scan image when no major changes are detected and may streamline the radiologist's workflow by highlighting the regions of interest.

关键词： Computed tomography Image reconstruction Optimization Greedy algorithms change detection algorithms Compressed sensing Task analysis change detection repeat scanning sparse CT scanning

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Kernel-Based Edge-Preserving Methods for Abrupt change detection

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 86-90页

作者： Xiang, Shiming Tang, Bo Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

Abrupt change detection is critical to monitor the occurrence of abnormal events from sensor data for situational awareness of complex systems. However, various disturbances and noises applied to the data observations may pose significant challenges to the robustness of many abrupt change detection methods. Recent researches have shown that bilateral filter can acquire outstanding performance on removing noises from images while preserving edge information. In this letter, we propose two improved edge-preserving memory-based cumulative sum (MB-CUSUM) methods that are able to make the abrupt change detection method more robust against noises. Our experimental studies show that the proposed methods can achieve superior performance over state-of-the-art methods to detect abrupt changes, which demonstrates the effectiveness and feasibility of their practical use.

关键词： Kernel Microsoft Windows Image edge detection Euclidean distance Signal processing algorithms Probability distribution change detection algorithms Abrupt change detection edge preserving bilateral filter event detection

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Statistical change detection with moments under time-varying illumination

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1998年第9期7卷 1258-1268页

作者： Liu, SC Fu, CW Chang, SY Nankai Coll Technol & Commerce Nantou Taiwan Chunghwa Telcom Co Ltd Telecommun Labs Taoyuan Taiwan Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan

In this paper, an illumination-independent statistical change detection method is proposed. The proposed method consists of two parts. First, based on our defined circular shift moments, structural changes can be distinguished from those due to time-varying illumination in the noise-free case, Moreover, the amount of computation is less than that of the shading model method, Second, in the light of the characteristics of the defined moments, a statistical decision rule is also proposed to cope with the effects of noise. The change detection problem can be treated as one of hypothesis testing. Critical values can be chosen according to the desired level of significance. Experimental results indicate that the proposed method detects changes accurately in the time-varying illumination case.

关键词： Lighting Layout Samarium Robustness detection algorithms Algorithm design and analysis Testing Machine vision Image segmentation change detection algorithms

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A Novel Cluster Kernel RX Algorithm for Anomaly and change detection Using Hyperspectral Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2016年第11期54卷 6497-6504页

作者： Zhou, Jin Kwan, Chiman Ayhan, Bulent Eismann, Michael T. Google Inc Mountain View CA USA Signal Proc Inc Rockville MD 20850 USA Air Force Res Lab Dayton OH 45433 USA Air Force Inst Technol Wright Patterson AFB OH 45433 USA

The Reed-Xiaoli (RX) algorithm has been widely used as an anomaly detector for hyperspectral images. Recently, kernel RX (KRX) has been proven to yield high performance in anomaly detection and change detection. In this paper, we present a generalization of the KRX algorithm. The novel algorithm is called cluster KRX (CKRX), which becomes KRX under certain conditions. The key idea is to group background pixels into clusters and then apply a fast eigendecomposition algorithm to generate the anomaly detection index. Both global and local versions of CKRX have been implemented. Application to anomaly detection using actual hyperspectral images is included. In addition to anomaly detection, the CKRX algorithm has been integrated with other prediction algorithms for change detection. Spatially registered visible and near-infrared hyperspectral images collected from a tower-based geometry have been used in the anomaly and change detection studies. Receiver operating characteristics curves and actual computation times were used to compare different algorithms. It was demonstrated that CKRX has comparable detection performance as KRX, but with much lower computational requirements.

关键词： Anomaly detection change detection algorithms cluster kernel RX (CKRX) hyperspectral imaging kernel RX (KRX) receiver operating characteristics (ROC) Reed-Xiaoli (RX)

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A Bayesian Approach to Sequential change detection and Isolation Problems

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第3期67卷 1796-1803页

作者： Chen, Jie Zhang, Wenyi Poor, H. Vincent Univ Sci & Technol China CAS Key Lab Wireless Opt Commun Hefei 230027 Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

The problem of sequential change detection and isolation under the Bayesian setting is investigated, where the change point is a random variable with a known distribution. A recursive algorithm is proposed, which utilizes the prior distribution of the change point. We show that the proposed decision procedure is guaranteed to control the false alarm probability and the false isolation probability separately under certain regularity conditions, and it is asymptotically optimal with respect to a Bayesian criterion.

关键词： Bayes methods Delays Upper bound change detection algorithms Random variables Stochastic systems Probability distribution Asymptotic behavior average detection delay Bayesian change detection change detection and isolation decision procedures

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3M-CDNet-V2: An Efficient Medium-Weight Neural Network for Remote Sensing Image change detection

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IEEE ACCESS 2022年 10卷 89581-89597页

作者： Zhao, Bo Tang, Panpan Luo, Xiaoyan Liu, Dongsheng Wang, Hui Res Ctr Big Data Technol Nanhu Lab Jiaxing 314000 Peoples R China PIESAT Int Informat Technol Ltd Beijing 100195 Peoples R China

Remote sensing-based change detection (CD) is a critical technique of detecting land surface changes in earth observation. Inspired by recent success of a lightweight CD network- 3M-CDNet, we implemented 9 meaningful modifications to it, for example, the incorporation of MHSA (Multi-Head Self-Attention). This elaborately designed model is termed as 3M-CDNet-V2. Its effectiveness and advantages were demonstrated on three engineering CD datasets, and experimental results indicated that: (1) relative to other state-of-the-art algorithms, the proposed model obtained very competitive or slight better performance on both visual comparison and quantitative metrics evaluation. (2) By applying a novel transfer learning strategy, 3M-CDNet-V2 could perform well on the small dataset. (3) The incorporation of MHSA brings a substantial accuracy improvement while moderately increasing the computational complexity. (4) The late fusion framework and deep supervision contribute most to the performance gain of 3M-CDNet-V2, while the introduction of low-level features to the classifier by skip connection could guide the model to focus on detailed spatial information such as small changes, narrow shaped objects, or accurate boundaries. We hope that our 3M-CDNet-V2 model helps in improving the understanding of network architecture design for CD.

关键词： Feature extraction Semantics Transformers Remote sensing Computer architecture Computational modeling Neural networks change detection algorithms Neural network remote sensing medium weight change detection

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Asymptotic Performance Analysis of Distributed Non-Bayesian Quickest change detection With Energy Harvesting Sensors

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第4期58卷 3697-3707页

作者： Biswas, Sinchan Dey, Subhrakanti Lund Univ Dept Elect & Informat Technol S-22100 Lund Sweden Uppsala Univ Div Signals & Syst S-75105 Uppsala Sweden NUI Maynooth Hamilton Inst Maynooth W23 F2H6 Kildare Ireland

This article focuses on the distributed non-Bayesian quickest change detection based on the cumulative sum (CUSUM) algorithm in an energy harvesting wireless sensor network, where the distributions before and after the change point are assumed to be known. Each sensor is powered by randomly available harvested energy from the surroundings. It samples the observation signal and computes the log-likelihood ratios (LLRs) of the aforementioned two distributions if enough energy is available in its battery for sensing and processing the sample (E-s). Otherwise, the sensor decides to abstain from the sensing process during that time slot and waits until it accumulates enough energy to perform the sensing and processing of a sample. This LLR is used for performing the CUSUM test to arrive at local decisions about the change point, which are then combined at the fusion center (FC) by a predecided fusion rule to arrive at a global decision. In this article, we derive the asymptotic expressions (as the average time to a false alarm goes to infinity) for the expected detection delay and the expected time to a false alarm at the FC for three common fusion rules, namely, OR, AND, and r out of N majority rule, respectively, by considering the scenario, where the average harvested energy at each sensor is greater than the energy required for sensing and processing a sample E-s. To this end, we use the theory of order statistics and the asymptotic distribution of the first passage times of the local decisions. Numerical results are also provided to support the theoretical claims.

关键词： Sensors Wireless sensor networks Energy harvesting Delays Sensor fusion detection algorithms change detection algorithms

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A Novel Video Dataset for change detection Benchmarking

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2014年第11期23卷 4663-4679页

作者： Goyette, Nil Jodoin, Pierre-Marc Porikli, Fatih Konrad, Janusz Ishwar, Prakash Univ Sherbrooke Fac Sci Dept Informat Sherbrooke PQ J1K 2R1 Canada Mitsubishi Elect Res Labs Cambridge MA 02139 USA Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA

change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90 000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website(1) and will be updated with feedback from academia and industry in the future.

关键词： change detection algorithms benchmark testing video surveillance

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Unsupervised change detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 1796-1809页

作者： Xue, Dinghua Lei, Tao Jia, Xiaohong Wang, Xingwu Chen, Tao Nandi, Asoke K. Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Peoples R China Shaanxi Univ Sci & Technol Shaanxi Joint Lab Artificial Intelligence Xian 710021 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artif Intelligence Xian 710021 Peoples R China China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Peoples R China Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH Middx England Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Popular unsupervised change detection algorithms suffer from two problems: first, the difference image generated by bitemporal images usually includes a large number of falsely changed regions due to noise corruption and illumination change;second, fuzzy clustering algorithms are sensitive to noise and they miss the relationship among feature components. To address these issues, we propose a multiscale and multiresolution Gaussian-mixture-model guided by saliency-enhancement (SE-MGMM) for change detection in bitemporal remote sensing images. The proposed SE-MGMM makes two contributions. The first is a novel salient strategy that can enhance saliency objects while suppressing the image background. The strategy uses the saliency weight information to enhance changed regions leading to the improvement of grayscale contrast between changed regions and unchanged regions. The second is that we present a Gaussian-mixture-model based on spatial multiscale and frequency multiresolution information fusion, which can effectively utilize features of difference images and improve detection results of changed regions. Experiments show that the proposed SE-MGMM is robust for both very high-resolution remote sensing images and synthetic aperture radar images. Moreover, the SE-MGMM achieves better change detection and provides better performance metrics than state-of-the-art approaches.

关键词： Task analysis Feature extraction Lighting Level set Gray-scale change detection algorithms Satellites change detection feature fusion Gaussian-mixture-model (GMM) saliency enhancement

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