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The incorporation of Information and Communication Technologies (ICT) into traditional power grids has transformed them into smart grids, revolutionizing energy management systems. At the core of this transformation are Intelligent Electronic Devices (IEDs), which provide essential data for key Energy Management System (EMS) applications, such as state estimation and optimal power flow. IEDs are critical for ensuring the stability and security of smart grid operations, but they are also vulnerable to various anomalies, including infrastructure faults, equipment malfunctions, energy theft, and cyberattacks. Detecting these anomalies is vital to maintaining the reliability of smart grid systems and preventing potential threats to national security. This study introduces a statistical data-driven framework designed to detect and explain anomalies in IED-based smart grid systems. The framework includes a preprocessing module for ensuring high-quality input data and an anomaly detection module that prioritizes interpretability and explainability. Using methods like the Gaussian Mixture Model (GMM), Kalman Filter (KF), and ExtraTree Classifier, the framework achieves 99% accuracy in anomaly detection and 88% accuracy in classifying events as either natural occurrences or cyberattacks.

Visual-inertial odometry (VIO) has been found to have great value in robot positioning and navigation. However, the existing VIO algorithms rely heavily on excellent lighting environments and the accuracy of robot positioning and navigation is degraded largely in illumination-challenging scenes. A robust visual-inertial navigation method is developed in this paper. We construct an effective low-light image enhancement model using a deep curve estimation network (DCE) and a lightweight convolutional neural network to recover the texture information of dark images. Meanwhile, a brightness consistency inference method based on the Kalman filter is proposed to cope with illumination variations in image sequences. Multiple sequences obtained from UrbanNav and M2DRG datasets are used to test the proposed algorithm. Furthermore, we also conduct a real-world experiment for the proposed algorithm. Both experimental results demonstrate that our algorithm outperforms other state-of-art algorithms. Compared to the baseline algorithm VINS-mono, the tracking time is improved from 22.0% to 68.2% and the localization accuracy is improved from 0.489m to 0.258m on the darkest sequences.

Recent work presented a framework for space-time-resolved neurophysiological process imaging that augments existing electromagnetic source imaging techniques. In particular, a nonlinear Analytic Kalman filter (AKF) has been developed to efficiently infer the states and parameters of neural mass models believed to underlie the generation of electromagnetic source currents. Unfortunately, as the initialization determines the performance of the Kalman filter, and the ground truth is typically unavailable for initialization, this framework might produce suboptimal results unless significant effort is spent on tuning the initialization. Notably, the relation between the initialization and overall filter performance is only given implicitly and is expensive to evaluate; implying that conventional optimization techniques, e.g. gradient or sampling based, are inapplicable. To address this problem, a novel efficient framework based on blackbox optimization has been developed to find the optimal initialization by reducing the signal prediction error. Multiple state-of-the-art optimization methods were compared and distinctively, Gaussian process optimization decreased the objective function by 82.1% and parameter estimation error by 62.5% on average with the simulation data compared to no optimization applied. The framework took only 1.6h and reduced the objective function by an average of 13.2% on 3.75min 4714-source channel magnetoencephalography data. This yields an improved method of neurophysiological process imaging that can be used to uncover complex underpinnings of brain dynamics.

As one of the most densely populated countries in the world, Bangladesh has been trying to contain the impact of a pandemic like coronavirus disease 2019 (COVID-19) since March, 2020. Although government announced an array of restricted measures to slow down the diffusion in the beginning of the pandemic, the lockdown has been lifted gradually by reopening all the industries, markets and offices with a notable exception of educational institutes. As the physical geography of Bangladesh is highly variable across the largest delta, the population of different regions and their lifestyle also differ in the country. Thus, to get the real scenario of the current pandemic and a possible second wave of COVID-19 transmission across Bangladesh, it is essential to analyze the transmission dynamics over the individual districts. In this paper, we propose to integrate the Unscented Kalman Filter (UKF) with classic SIRD model to explain the epidemic evolution of individual districts in the country. We show that UKF-SIRD model results in a robust prediction of the transmission dynamics for 1–4 months. Then we apply the robust UKF-SIRD model over different regions in Bangladesh to estimates the course of the epidemic. Our analysis demonstrates that in addition to the densely populated areas, industrial areas and popular tourist spots will be in the risk of higher COVID-19 transmission if a second wave of COVID-19 occurs in the country. In the light of these outcomes, we also provide a set of suggestions to contain the future pandemic in Bangladesh.

After a review of widely used pattern recognition methods we present the Kalman filter and the associated smoother as a recursive variant of conventional least squares estimators. We first discuss its application to the reconstruction of charged tracks, including simultaneous track finding and track fitting and a robustification of the filter. This section is concluded by a case study of track reconstruction strategy in the DELPHI experiment. The second part deals with vertex reconstruction, including the detection of outlying tracks. It is shown that the detection of secondary vertices can be further improved by a robustification of the vertex fit via the M-estimator.

In this paper, we propose a localization simulator based on the random walk/waypoint mobility model and a hybrid-type location–compensation algorithm using the Mean kShift/Kalman filter (MSKF) to enhance the precision of the estimated location value of mobile modules. From an analysis of our experimental results, the proposed algorithm using the MSKF can better compensate for the error rates, the average error rate per estimated distance moved by the mobile node (Err_ Rate_DV) and the error rate per estimated trace value of the mobile node (Err_Rate_TV) than the Mean shift or Kalman filter up to a maximum of 29% in a random mobility environment for the three scenarios.

This paper presents an attitude solution algorithm based on the Micro-Electro-Mechanical System and quaternion method. We completed the numerical calculation and engineering practice by adopting fourth-order Runge–Kutta algorithm in the digital signal processor. The state space mathematical model of initial alignment in static base was established, and the initial alignment method based on Kalman filter was proposed. Based on the hardware in the loop simulation platform, the short-range flight simulation test and the actual flight test were carried out. The results show that the error of pitch, yaw and roll angle is fast convergent, and the fitting rate between flight simulation and flight test is more than 85%.

A novel method is proposed to achieve robust and real-time ball tracking in broadcast soccer videos. In sports video, the soccer ball is small, often occluded, and with high motion speed. Thus, it is difficult to detect the sole ball in a single frame. To solve this problem, rather than locate the ball in one of several frames through detection or tracking, we find the ball through optimizing its motion trajectory in successive frames. The proposed method includes three level processes: object level, intra-trajectory level, and inter-trajectory level processing. In object level, multiple objects instead of a single ball are detected and all of them are taken as ball candidates through shape and color features identification. Then at intra-trajectory level, each ball candidate is tracked by a Kalman filter and verified by detection in successive frames, which results in lots of initial short trajectories in a video shot. These trajectories are thereafter scored and filtered according to their length and spatial-temporal relationship in a time-line model. With these trajectories, we construct a distance graph, in which a node represents a trajectory, and an edge means distance between two trajectories. We then get the optimal path using the Dijkstra algorithm in the graph at the inter-trajectory level. The optimal path is composed by a sequence of initial trajectories which make the whole route smooth and long in duration. To get a complete and reasonable path, we finally apply cubic spline interpolation to bridge the gap between adjacent trajectories (the duration corresponding to when the ball is occluded). We select three representative real FIFA2006 soccer video clips (containing a total of 16,500 frames) and manually elaborately labeling each frame in it, and take it as ground-truth to evaluate the algorithm. The average F-score is 80.59%. The algorithm was used in our soccer analysis system and tested on a wide range of real soccer videos, and all the results are satisfied. The algorithm is effective and its whole speed far exceeds real-time, 35.6 fps on mpeg2 data on the Intel Conroe platform.

Face tracking in surveillance videos is one of the important issues in the field of computer vision and has realistic significance. In this paper, a new face tracking framework in videos based on convolutional neural networks (CNNs) and Kalman filter algorithm is proposed. The framework uses a rough-to-fine CNN to detect faces in each frame of the video. The rough-to-fine CNN method has a higher accuracy in complex scenes such as face rotation, light change and occlusion. When face tracking fails due to severe occlusion or significant rotation, the framework uses Kalman filter to predict face position. The experimental results show that the proposed method has high precision and fast processing speed.

A large number of image and spatial information processing problems involves the estimation of the intrinsic image information from observed images, for instance, image restoration, image registration, image partition, depth estimation, shape reconstruction and motion estimation. These are inverse problems and generally ill-posed. Such estimation problems can be readily formulated by Bayesian models which infer the desired image information from the measured data. Bayesian paradigms have played a very important role in spatial data analysis for over three decades and have found many successful applications. In this paper, we discuss several aspects of Bayesian paradigms: uncertainty present in the observed image, prior distribution modeling, Bayesian-based estimation techniques in image processing, particularly, the maximum a posteriori estimator and the Kalman filtering theory, robustness, and Markov random fields and applications.

Magnetic resonance imaging (MRI) has become a widely used research and clinical tool in the study of the human brain. The ability to robustly and accurately quantify repeatable morphological measures from such data is aided by the ability to accurately segment the MRI data set into homogeneous regions such as gray matter, white matter, and cerebro spinal fluid. The large amount of data associated with typical MRI scans makes completely manual segmentation prohibitive on a large scale. In this paper an efficient approach to the segmentation of such MR imagery is presented. The approach uses an estimation-theoretic interpretation of the segmentation problem to develop a computationally efficient, statistically-based recursive technique for its solution. Being statistically based, the method also provides associated measures of uncertainty of the resulting estimates, which are extremely important both for evaluation of the estimates as well as their combination with other sources of information.

In this paper, a state estimation problem is considered, where sensor data are transmitted with finite communication capacity constraint. To reduce the sensor data transmission, only one sensor data is transmitted at each period among multiple sensors. We propose a dynamic scheduling algorithm, which is based on the difference between the current sensor data and the last transmitted sensor value. The proposed algorithm chooses sensor data, which have the largest contribution to the estimation performance. With the proposed algorithm, when a sensor data is chosen and transmitted, some information can be derived from not transmitted sensor data. This information is also used for better estimation performance.

The Road extraction from the remotely sensed imagery is highly realistic for the quick road updating in the Geographic Information System (GIS) data collection. The particle filter (PF) was earlier employed to track the road maps in satellite images. In our previous work, we have introduced an efficient Gauss–Hermite Kalman Filter with Locally Excitatory Globally Inhibitory Oscillator Networks (GHKF–LEGION)-based road extraction, even though it does not properly extract the road from the complex region. In order to recover the track of the road beyond obstacles, in this work, we proposed a novel hybrid multi-kernel partial least squares (PLS) with PF approach. Here, at first, we estimate the initial leader point of the road employing the K-means clustering technique. Subsequently, the PF traces a road till a stopping benchmark is satisfied. Thereafter, without finishing the process, the outcomes are furnished to the hybrid kernel PLS technique which attempts to locate the continuance of the road after several potential road blocks or to locate the entire feasible road branches which are on the other side of the road junction. The outcomes are offered for five satellite images. The experimental results show our proposed road tracking method is better compared to other existing works.

Due to the complication control structure and heavy burden in computation of the Permanent Magnet Synchronous Motor (PMSM) control strategy, a new cascade control strategy is proposed, which is combined with advanced control algorithm of Active Disturbance Rejection Control (ADRC), Finite Control Set Model Predictive Control (FCS-MPC) and Kalman filtering. ADRC is designed in detail for speed control, in order to reduce the burden of Extended State Observer (ESO) in ADRC, a Kalman filter is designed to simultaneously observe load disturbance and back Electromotive Force (EMF), which can also reduce influence of the noise. While the estimated EMF is used for the current loop FCS-MPC control, it is improved with a method of best vector selection and duty cycle optimization. The proposed combined control scheme is verified in MATLAB simulation environment, through detailed analysis, it shows both good performance in steady state and dynamic response.

Kalman filter algorithm, an effective data processing algorithm, has been widely used in space monitoring, wireless communications, tracking systems, financial industry, big data and so on. On Sunway TaihuLight platform, we present an optimized Kalman filter parallel algorithm which is according to new architecture of the SW26010 many-core processors (260 cores) and new programming mode (master and slave heterogeneous collaboration mode). Furthermore, we propose a pipelined parallel mode for Kalman filter algorithm based on seven-level pipeline of SW26010 processor. The vector optimization strategy and double buffering mechanisms are provided to improve parallel efficiency of Kalman filter parallel algorithm on SW26010 processors. The vector optimization strategy can improve data concurrency in parallel computing. In addition, the communication time can be hidden by double buffering mechanisms of SW26010 processors. The experimental results show that the performance and scalability of the parallel Kalman filter algorithm based on SW26010 are greatly improved compared with the CPU algorithm for five data sets, and is also improved compared to the algorithm on GPU.

This paper proposes a fast and robust dynamic state estimation technique based on model transformation method using the proposed hybrid technique. The proposed hybrid method is the combination of Unscented Kalman Filter (UKF) and Gradient Boosting Decision Tree (GBDT), hence commonly referred to as the UKF–GBDT technique. The proposed model transformation approach is accomplished by taking the active power generator measured as input variable and derived frequency as rate of change of frequency measurements of phasor measurement units (PMU) as dynamic generator output variable model. The proposed hybrid technique is also formulated to deal with data quality issues, and the rate of change of frequency and frequency measurements may be skewed in the presence of rigorous disruption or communication problems. This permits to obtain discrete-time linear dynamic equations in state space based on the linear Kalman filter (LKF). With this proper control, this model alleviates filter divergence problems, which can be a severe issue if the nonlinear model is utilized in greatly strained operating system conditions, and gives quick estimate of rotor speeds together with angles through transient modes if only the transient stability with control is concerned. In the case of long-term dynamics, the outcome of governor’s response in long-term system dynamics is offset together with mechanical power at rotor speed and the state vector angles for joint evaluation. At last, the performance of the proposed method is simulated in MATLAB/Simulink and the performance is compared to the existing methods like UKF, GBDT and ANN. The proposed technique is simulated under three case studies like IEEE 14-, 30- and 118-bus systems.

We suggest incorporating dynamical information such as locations of unstable fixed points into parameter estimation algorithms in order to improve the method of reconstructing dynamics from time series data. We show how the process of reconstruction using nonlinear filters such as the extended Kalman filter can be easily modified to take advantage of the additional information. We demonstrate the methods using data from two systems exhibiting chaotic dynamics — the Chua circuit and Chen's equations. In both cases we find the models reconstructed using constraints that better approximate the unstable fixed point structure of the underlying systems.

In this paper, a method to account for model error due to unresolved scales in sequential data assimilation, is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. This approach is tested in the context of a low order chaotic dynamical system. The results show that the filter skill is significantly improved by implementing the proposed scheme for the treatment of the unresolved scales.

Online 3D games require fast and efficient user interaction support over the network environments, and the networking support is usually implemented by the use of a network game engine. The network game engine should minimize the network delay and mitigate the network traffic congestion. To minimize the network traffic between game users, a client-based prediction (dead reckoning (DR) algorithm) is used. Each game entity uses the algorithm to estimate its own movement as well as the others’. In case the estimation error exceeds the threshold, the entity sends an UPDATE packet which includes velocity, position and the like to other entities. As the estimation accuracy is increased, each entity can minimize the transmission of the UPDATE packet. In this paper, a Kalman filter-based approach is proposed in order to improve the prediction accuracy and an adaptive Kalman gain control in order to minimize the number of UPDATE packets to distant devices. The BZFlag game was used in the experiment in order to verify the proposed approach and the results have shown that it is possible to increase prediction accuracy and reduce the network traffic by 12%.

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.