State Estimation Model Research Articles

Righteous Shoot or Racial Injustice? What Crowdsourced Data Can(not) Tell Us About Police-Caused Homicide

Social scientists have developed various theoretical perspectives to explain the disproportionate incidence of police-caused homicide involving Black citizens in the United States. A common approach focuses on structural characteristics (e.g., percent Black) of cities. Such research relies primarily on Uniform Crime Reports’ Supplemental Homicide Reports, which poses two problems for researchers. Undercounting raises concerns about the reliability of findings, and the data are not amenable to testing influential alternative hypotheses. Recently, efforts to more accurately count police-caused homicides have been undertaken, with these new crowdsourced databases increasingly being used in research. When merged with structural-level data, they may allow the estimation of multilevel statistical models that include city-level and event-level predictors of police-caused homicide. These databases also pose methodological challenges, but they hold out the promise of providing a more reliable answer to a fundamental question that has yet to be adequately addressed. Is the racial disparity in police-caused homicide primarily attributable to the objective threats posed by Black citizens or the subjective biases of police officers? This article evaluates the degree to which the new crowdsourced databases can help resolve this conundrum.

A Hybrid State Estimation Approach for Integrated Heat and Electricity Networks Considering Time-scale Characteristics

State estimation (SE) usually serves as the basic function of the energy management system (EMS). In this paper, the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established. Then, a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline. Meanwhile, to accommodate the measuring resolutions of the integrated network, a hybrid SE approach is developed based on the two-stage iterative algorithm. Results show that, in both steady and dynamic processes, the two-stage estimator has good accuracy and convergence. The hybrid estimator has good performance on tracking the variation of the states in the heating network, even when the available measurements are limited.

Semi-autonomous control of excavation using model of human operation and state estimation via Extended Kalman Filter

For robotic teleoperation, semi-autonomous control, i.e. autonomous control allowing human interposition will be an effective way overcoming delay of electrical communication and emergency operation. We have proposed semi-autonomous control method focusing on excavation in teleoperation environments. In the method, a model of human operation is designed and human internal state is estimated by Extended Kalman Filter. In this paper, experimental evaluations are conducted and reported about ”Digging” process. A human skill of excavation is modeled by an attractor-based dynamics embedding shallow and deep digging trajectories for soft and hard soil, respectively. An index parameter is changed based on the load of the bucket, and a seamless motion transition is realized satisfying the restriction by setting a threshold. Moreover, by adding a human operation to the autonomy, the proposed method will cope with the emergency, which is experimentally evaluated.

Interval State Estimation With Uncertainty of Distributed Generation and Line Parameters in Unbalanced Distribution Systems

Distribution system state estimation (DSSE), which provides critical information for system monitoring and control, is being challenged by multiple sources of uncertainties such as random meter errors, stochastic power output of distributed generation (DG), and imprecise network parameters. This paper originally proposes a general interval state estimation (ISE) model to simultaneously formulate these uncertainties in unbalanced distribution systems by interval arithmetic. Moreover, this model can accommodate partially available measurements of DG outputs and inaccurate line parameters. Further, a modified Krawczyk-operator (MKO) algorithm is proposed to solve the general ISE model efficiently, and effectively provides the upper and lower bounds of state variables under coordinated impacts of these uncertainties. The proposed algorithm is tested on unbalanced IEEE 13-bus and 123-bus systems. Comparison with various methods including Monte Carlo simulation indicates that the proposed algorithm is many orders of magnitude faster and encloses tighter boundaries of state variables.

Dynamic state estimation for integrated electricity-gas systems based on Kalman filter

In recent years, the integrated electricity-gas systems (IEGS) have attracted widespread attention. The unified scheduling and control of the IEGS depend on high-precision operating data. To this end, it is necessary to establish an appropriate state estimation (SE) model for IEGS to filter the raw measured data. Considering that power systems and natural gas systems have different time scales and sampling periods, this paper proposes a dynamic state estimation (DSE) method based on Kalman filter that can consider the dynamic characteristics of natural gas pipelines. Firstly, the standardized state transition equations for the gas system are developed by applying the finite difference method to the partial differential equations (PDEs) of the gas system; then the DSE model for IEGS is formulated based on Kalman filter; also, the measurements from electricity system and the gas system with different sampling periods are fused to ensure the observability of DSE by using the interpolation method. The IEEE 39-bus electricity system and the 18-nodes Belgium gas system are integrated as the test system. Simulation results verify the proposed method's accuracy and calculation efficiency.

Gearbox Fault Prediction of Wind Turbine Based on Improved NEST Model

This paper studies a fault prediction method for wind turbine gearbox. It uses grey relation analysis to get modeling variables, and makes sample data getting good integrity and redundancy by similarity analysis. Thus it gets the reduced process memory matrix, and trains the improved nonlinear state estimation (NEST) model. When the gearbox fails, the model residual will exceed the threshold value, and the model will give an early warning. Combined with the actual operation data of a wind turbine, the effectiveness and accuracy of the improved model are verified.

Navnet: AUV Navigation Through Deep Sequential Learning

Achieving accurate navigation and localization is crucial for Autonomous Underwater Vehicle (AUV). Traditional navigation algorithms, such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), require the system model and measurement model for state estimation to obtain the AUV position. However, this may introduce modeling errors and state estimation errors which will affect the final precision of AUV navigation system to a certain extent. To avoid these problems, in this paper, we proposed a deep framework - NavNet - by taking AUV navigation as a deep sequential learning problem. Firstly, the proposed NavNet can take raw sensor data at different frequencies as input, which benefits from the sequential learning capability of Recurrent Neural Network (RNN). Secondly, NavNet takes advantage of a simplified attention mechanism and Fully Connected (FC) layers to output AUV displacements per unit time, which accomplishes low-frequency AUV navigation by accumulation of it. More importantly, there is no need for the model building and state estimation with NavNet, which avoids the import of relevant errors. We compare the performance of NavNet to EKF and UKF using collected data by running Sailfish in the sea. Experimental results show that NavNet has an excellent performance in terms of both the navigation accuracy and fault tolerance. In addition, a reliable fusion strategy of NavNet and conventional method is applied to achieve high-frequency AUV navigation. The experimental results show that the proposed architecture can be a reliable supplement to limit the error growth of conventional algorithms.

Optimized SVM-Driven Multi-Class Approach by Improved ABC to Estimating Ship Systems State

In the intelligent ship field, with the upgrading of ship maintenance mode, the human-centered system maintenance will be gradually replaced by the artificial intelligence decision methods. To improve the training speed and testing accuracy of the state estimation model, an optimized Support Vector Machine (SVM) driven approach by Improved Artificial Bee Colony (IABC) was proposed to solve the global parameters optimization problem. First, the IABC method was achieved from three aspects: nectar source initializing, employed bee global neighborhood searching, and scouts mutation neighborhood searching. Second, the multi-class SVM with one-against-one classifiers was selected, and the best global parameters were achieved by the IABC. Third, the optimized SVM model was adopted in the testing to verify the effectiveness of state estimation. Finally, the elaborated methodology was applied to two actual ship systems to get the analysis results. The effectiveness was verified by using two examples. The results show the following: the IABC optimized SVM can obtain the global optimal parameters at a faster speed than the traditional ABC optimized method; the IABC optimized method can help the training start with better initial parameters, and get a higher classification accuracy rate than the traditional ABC optimized method. Based on the comparative analysis results, the IABC optimized SVM shows an obvious advantage of parameter optimization in the training process, and it can also significantly improve the model training efficiency and achieve a higher state estimation accuracy. The optimized SVM by IABC is an effective state estimation method in ship systems.

Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

Details about a fault’s progression, including the remaining-useful-lifetime (RUL), are key features in monitoring, industrial operation and maintenance (O&M) planning. In order to avoid increases in O&M costs through subjective human involvement and over-conservative control strategies, this work presents models to estimate the RUL for wind turbine main bearing failures. The prediction of the RUL is estimated from a likelihood function based on concepts from prognostics and health management, and survival analysis. The RUL is estimated by training the model on run-to-failure wind turbines, extracting a parametrization of a probability density function. In order to ensure analytical moments, a Weibull distribution is assumed. Alongside the RUL model, the fault’s progression is abstracted as discrete states following the bearing stages from damage detection, through overtemperature warnings, to over overtemperature alarms and failure, and are integrated in a separate assessment model. Assuming a naïve O&M plan (wind turbines are run as close to failure as possible without regards for infrastructure or supply chain constrains), 67 non run-to-failure wind turbines are assessed with respect to their early stopping, revealing the potential RUL lost. These are turbines that have been stopped by the operator prior to their failure. On average it was found that wind turbines are stopped 13 days prior to their failure, accumulating 786 days of potentially lost operations across the 67 wind turbines.

Computer vision–based damage and stress state estimation for reinforced concrete and steel fiber–reinforced concrete panels

This article presents a computer vision damage assessment approach that relates surface crack patterns to damage levels and stress state characteristics in conventionally reinforced concrete and steel fiber–reinforced concrete panels. Previous studies have focused on crack patterns for specific structural element types such as beams and columns, but this study considers stress states in a more general framework. In particular, image data from previously published panel test specimens subjected to nominally constant stress have been collected to develop image-based estimation models capable of quantifying damage levels and stress components for full-panel crack patterns, and to investigate subimage sampling strategies to approximate full-panel results using partial-panel images. The objective here is to show that the analog of representative volume elements can be extended to image-based analysis contexts. The image datasets used in this article have been obtained from five different published studies, which provided 189 crack pattern images captured from concrete and steel fiber–reinforced concrete shear panel specimens. Given the limited size of the dataset, a feature-based computer vision approach has been used, with various geometric attributes of surface crack patterns used to train the estimation models. Within the limits of the data available, the preliminary results presented here indicate that quantifiable correlations exist such that stress state and damage level estimation models are valid across a range of loadings (i.e. reverse cyclic and monotonic) and materials (reinforced concrete and steel fiber–reinforced concrete), and that with appropriate sampling techniques, it is possible for subsampled images to yield estimations similar to full-panel results. These localized correlations between crack patterns and stress states potentially could be used in broader contexts for damage assessment of more general reinforced concrete and steel fiber–reinforced concrete members.

First order multi-lane traffic flow model – an incentive based macroscopic model to represent lane change dynamics

ABSTRACTUnbalanced lane usage on motorways might lead to the reduction in capacity of the motorway. Lane-level traffic management present new opportunities to balance the lane-flow distribution and help reduce congestion. In order to come up with efficient traffic management strategies on a lane-level, there is a need for accurate lane-specific traffic state estimation models. This paper presents a first-order lane-level traffic flow model. The proposed model differs from the existing models in the following areas: (i) incentive-based motivation for lane changes and consideration of downstream conditions (ii) transfer of lateral flows among cells. The model is tested against real-world data. It is observed that the model is able to capture the lane-level dynamics in terms of the lane flow distribution. The model results are compared to a linear regression model and results show that the developed model performs better than the regression model on the test sections.

Security distributed state estimation for nonlinear networked systems against DoS attacks

SummaryThis paper is concerned with security distributed state estimation for nonlinear networked systems against denial‐of‐service attacks. By taking the effects of resource constraints into consideration, an event‐triggered scheme and a quantization mechanism are employed to alleviate the burden of network. A mathematical model of distributed state estimation is constructed for nonlinear networked systems against denial‐of‐service attacks. Sufficient conditions ensuring the exponential stability of the estimation error systems are obtained by utilizing the Lyapunov stability theory. The explicit expressions of the designed state estimators are acquired in terms of the linear matrix inequalities. Finally, a numerical example is used to testify the feasibility of the proposed method.

Statistical determination of synergy based on Bliss definition of drugs independence.

Although synergy is a pillar of modern pharmacology, toxicology, and medicine, there is no consensus on its definition despite its nearly one hundred-year history. Moreover, methods for statistical determination of synergy that account for variation of response to treatment are underdeveloped and if exist are reduced to the traditional t-test, but do not comply with the normal distribution assumption. We offer statistical models for estimation of synergy using an established definition of Bliss drugs’ independence. Although Bliss definition is well-known, it remains a theoretical concept and has never been applied for statistical determination of synergy with various forms of treatment outcome. We rigorously and consistently extend the Bliss definition to detect statistically significant synergy under various designs: (1) in vitro, when the outcome of a cell culture experiment with replicates is the proportion of surviving cells for a single dose or multiple doses, (2) dose-response methodology, (3) in vivo studies in organisms, when the outcome is a longitudinal measurement such as tumor volume, and (4) clinical studies, when the outcome of treatment is measured by survival. For each design, we developed a specific statistical model and demonstrated how to test for independence, synergy, and antagonism, and compute the associated p-value.

Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems With Unknown Control Inputs

This paper proposes a correlation-aided robust adaptive unscented Kalman filter (UKF) for power system decentralized dynamic state estimation with unknown inputs, termed as robust AUKF-UI. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive (VAR) model in an adaptive manner. This VAR model is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. This allows taking into account the implicit cross-correlations between the states and the unknown inputs. As a result, the rank requirement for unknown input vector estimation is relaxed and the local generator frequency measurement is not required. The temporal correlations of time series innovation vectors, predicted state and input vectors are also leveraged by the robust AUKF-UI to detect, identify and process bad data. Without these correlations, it is very challenging to address bad data with unknown inputs. Simulation results carried out on the IEEE 39-bus system demonstrate that, thanks to the use of hidden system temporal and spatial correlations, the proposed robust AUKF-UI has lower requirement of the number of measurements for dynamic state estimation while achieving better robustness against bad data as compared to the existing UKF methods with unknown inputs.

A Multi-Scan Labeled Random Finite Set Model for Multi-Object State Estimation

State-space models in which the system state is a finite set–called the multi-object state–have generated considerable interest in recent years. Smoothing for state-space models provides better estimation performance than filtering. In multi-object state estimation, the multi-object filtering density can be efficiently propagated forward in time using an analytic recursion known as the generalized labeled multi-Bernoulli (GLMB) recursion. In this paper, we introduce a multi-scan version of the GLMB model to accommodate the multi-object posterior recursion, and develop efficient numerical algorithms for computing this so-called multi-scan GLMB posterior.

A New Hybrid State Estimation Considering Different Accuracy Levels of PMU and SCADA Measurements

This paper proposes a new hybrid state estimation (SE) model where the supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements are decoupled. The proposed hybrid SE can effectively handle different accuracy levels of PMU and SCADA measurements which is a problem for conventional hybrid SEs due to increasing the condition number of SE gain matrix. In addition, the proposed hybrid SE model is fully compatible with PMU measurements, particularly current measurements, which increase the complexity of conventional hybrid SE formulation. The proposed hybrid SE model is tested on the IEEE 118-bus test system as well as on the real-world Iran’s power system. Moreover, the proposed hybrid SE is compared with conventional hybrid SE using a large number of scenarios modeling uncertain telemetry noises. The comparative results illustrate better performance of the proposed hybrid SE than conventional hybrid SE in terms of the gain matrix condition number, SE run time, and SE accuracy.

Interval State Estimation for Low-Voltage Distribution Systems Based on Smart Meter Data

The real-time monitoring and control of distribution systems are an essential step toward the future smart grid. This paper presents an interval state estimation model for low-voltage (LV) systems based on smart meter data provided by electrical customers. The formulation is based on a two-level approach, constituted of a nodal processor and a system state estimator. The nodal processor filters the smart meter data and provides a smaller amount of clean processed data to the upper level system state estimator. The system state estimator, based on the extreme value theory, provides the maximum and minimum values of the system states. To obtain narrow interval images, a scaling parameter is used to consider the fact that the errors of downstream smart meters are rarely at the maximum values simultaneously. The presented approach allows removing the assumption on the particular probability distribution of the smart meter measurement errors. Simulations are carried out on a sample LV network to show the characteristics and performance of the proposed approach.

Robust visual tracking based on global-and-local search with confidence reliability estimation

Visual object tracking is an open and challenging problem, an online tracker must be able to keep track of the target object for a long time period even in complex scenarios, such as target drift and background occlusion. Discriminative correlation filters (DCF) have shown excellent performance in short-term target tracking problems thanks to their circular dense sampling mechanism and fast computation with a discrete Fourier transform. However, they tend to drift from the target when the target encounters drastic deformation, fast motion, or background occlusion. This can result in a bad model update since the tracker searches the target in a local region centered at the position where target was located in the previous frame. There is no recovery mechanism for target re-identification and re-location. To handle this issue, this paper proposes a global-and-local-search technique that applies a DCF-based tracking model with a novel target-aware detector in a collaborative way. Our tracking model performs the local search process with high tracking confidence, and the target-aware detector is executed to re-identify and locate the target via global search from the entire frame when the model instability and confidence fluctuation are detected by proposed tracking system. Additionally, we designed an enhanced peak-to-sidelobe ratio (EPSR) for confidence estimation, which indicates system instability and fluctuation degree. Thus, the local tracking model and target-aware detector are collaboratively applied for both final target state estimation and online model updates. This not only avoids model corruption from bad updates, but also prevents our tracker from drifting problems for long-term tracking. Experiments on OTB-100 and VOT2016 benchmarks demonstrate that the proposed tracking method achieves state-of-the-art tracking performance in terms of accuracy and robustness, with 22 fps tracking speed (close to realtime) run on a single GPU.

State of charge monitoring of Li-ion batteries for electric vehicles using GP filtering

Abstract Electric vehicles are dependent on onboard battery management systems that protect the battery from functioning outside its safe operating limits by monitoring its state of charge (SOC). Advanced online monitoring techniques are required so that the performance of the energy management is not lowered severely. However, the behavior of batteries is difficult to be predicted online because of its nonlinearity, intrinsic variability and fluctuating environmental conditions. Gaussian Process (GP)-Bayesian filters are based on probabilistic non-parametric Gaussian models of hidden states using available measurements. As a result, model response variability can be explicitly incorporated into the prediction and measurement steps, which is usually not the case for more traditional filtering strategies that resort to parametric models for state estimation. In this work, GP models were incorporated into nonparametric filtering techniques to monitor the battery SOC online. Results show that Bayes’ filtering techniques increase the predictability of the SOC under uncertainty about the effect of environmental conditions on the SOC.

Enhanced Performance of Developed Two-Step (Hybrid/PMU) Linear State Estimator Model

Enhanced Performance of Developed Two-Step (Hybrid/PMU) Linear State Estimator Model