Stochastic models are important in global navigation satellite systems (GNSS) estimation problems. One can achieve reliable ambiguity resolution and precise positioning only by use of a suitable ...stochastic model. The BeiDou system has received increased research focus, but based only on empirical stochastic models from the knowledge of GPS. In this paper, we will systematically study the estimation, assessment and impacts of a triple-frequency BeiDou stochastic model. In our estimation problem, a single-difference, geometry-free functional model is used to extract pure random noise. A very sophisticated structure of unknown variance matrix is designed to allow the estimation of satellite-specific variances, cross correlations between two arbitrary frequencies, as well as the time correlations for phase and code observations per frequency. In assessing the stochastic models, six data sets with four brands of BeiDou receivers on short and zero-length baselines are processed, and the results are compared. In impact analysis of stochastic model, the performance of integer ambiguity resolution and positioning are numerically demonstrated using a realistic stochastic model. The results from ultrashort (shorter than 10 m) and zero-length baselines indicate that BeiDou stochastic models are affected by both observation and receiver brands. The observation variances have been modeled by an elevation-dependent function, but the modeling errors for geostationary earth orbit (GEO) satellites are larger than for inclined geosynchronous satellite orbit (IGSO) and medium earth orbit (MEO) satellites. The stochastic model is governed by both the internal errors of the receiver and external errors at the site. Different receivers have different capabilities for resisting external errors. A realistic stochastic model is very important for achieving ambiguity resolution with a high success rate and small false alarm and for determining realistic variances for position estimates. To the best of our knowledge, this paper is the first comprehensive study on such stochastic models used specifically with BeiDou data.
In this paper, a sampling-based stochastic model predictive control (SMPC) algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive disturbances. ...One of the main drivers for the development of the proposed control strategy is the need for reliable and robust guidance and control strategies for automated rendezvous and proximity operations between spacecraft. To this end, the proposed control algorithm is validated on a floating spacecraft experimental testbed, proving that this solution is effectively implementable in real time. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space environment are simultaneously considered. The approach enables to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided. Moreover, the offline sampling approach in the control design phase shifts all the intensive computations to the offline phase, thus greatly reducing the online computational cost, which usually constitutes the main limitation for the adoption of SMPC schemes, especially for low-cost on-board hardware. Numerical simulations and experiments show that the approach provides probabilistic guarantees on the success of the mission, even in rather uncertain and noisy situations, while improving the spacecraft performance in terms of fuel consumption.
•Propose a scenario-driven dynamic stochastic PRA model for interdependent CIs.•Construct a holistic PRA scenario for CIs to characterize the inherent features.•Quantify risk factor multiplicity, CI ...interdependency, and dynamic stochasticity.•Use multisource data from objective factual records and subjective expert judgment.•Demonstrate the effectiveness of the proposed model through a case study in China.
Critical infrastructures (CIs) are becoming increasingly important in social public services; however, various CI risk events emerge constantly with potential damages. Probabilistic risk assessment (PRA) is a quantitative measurement of risk occurrence probability that is used to support risk profile judgment and weakness identification. However, the inherent features of risk factor multiplicity, CI interdependency, and dynamic stochasticity render PRA for CIs more challenging. The purpose of this study is to investigate a PRA model for CIs with a comprehensive consideration of the inherent features and an integrated utilization of multisource data. A multidimensional PRA scenario analysis is first conducted from the perspectives of scenario elements, scenario evolution, and scenario effect. Subsequently, to support PRA for interdependent CIs, a scenario-driven dynamic stochastic model is developed based on a three-stage solution with accurate feature quantification, and effective utilization of objective factual records and subjective expert judgment. Furthermore, the applicability and effectiveness of the proposed model are demonstrated through a case study. It is indicated that the PRA results obtained using the proposed model are beneficial for decision makers to clarify the overall risk profile and determine the risk-alert periods, high-frequency risk factors, and high-risk CIs to support CI risk prevention.
•To develop an adaptive economic DR framework using price elasticity model to imitate customer’s behaviour.•A dynamic elasticity using deterministic approach is designed to interrelate peak hour’s ...elasticity to valley/off peak hours for the load recovery.•A stochastic approach using a geometric Brownian motion is proposed to mimic the customer’s behaviour with adaption of intertemporal constraint of load flexibility in DR.•A disaggregated load modeling approach is considered to assess the DR impact on class wise customers.
Price elasticity model (PEM) is an appealing and modest model for assessing the potential of flexible demand in demand response (DR). It measures the customer’s demand sensitivity through elasticity in relation to price variation. However, application of PEM is partially apprehensible on attributing the adaptability and adjustability along with intertemporal constraints in DR. Thus, this article presents an adaptive economic DR framework with its attributes via a dynamic elasticity approach to model customer’s demand sensitivity. This dynamic elasticity is modeled through the deterministic and stochastic approaches. Both approaches envision the notion of load recovery for shiftable/flexible loads to make the proposed framework adaptive and adjustable relative to price variation. In stochastic approach, a geometric Brownian motion is employed to emulate load recovery in addition to intertemporal constraint of load flexibility. The proposed mathematical model shows what should be the customers elasticity value to achieve the factual DR. The numerical study is carried out on standard IEEE 33 distribution system bus load data to assess its technical and socio-economic impact on customers and is also compared with the existing model.
We present a simple model for the axial dipole moment (ADM) of the geomagnetic field based on a stochastic differential equation for two coupled particles in a biquadratic potential, subjected to ...Gaussian random perturbations. This model generates aperiodic reversals and excursions separated by stable polarity periods. The model reproduces the temporal asymmetry of geomagnetic reversals, with slower decaying rates before the reversal and faster growing rates after it. This temporal asymmetry is possible because our model is out of equilibrium. The existence of a thermal imbalance between the two particles sets a preferential sense for the energy flux and renders the process irreversible.
In this paper, we present a comprehensive approach for investigating JPEG compressed test images, suspected of being tampered either by splicing or copy-move forgery (cmf). In JPEG compression, the ...image plane is divided into non-overlapping blocks of size 8 × 8 pixels. A unified approach based on block-processing of JPEG image is proposed to identify whether the image is authentic/forged and subsequently localize the tampered region in forged images. In the initial step, doubly stochastic model (dsm) of block-wise quantized discrete cosine transform (DCT) coefficients is exploited to segregate authentic and forged JPEG images from a standard dataset (CASIA). The scheme is capable of identifying both the types of forged images, spliced as well as copy-moved. Once the presence of tampering is detected, the next step is to localize the forged region according to the type of forgery. In case of spliced JPEG images, the tampered region is localized using block-wise correlation maps of dequantized DCT coefficients and its recompressed version at different quality factors. The scheme is able to identify the spliced region in images tampered by pasting uncompressed or JPEG image patch on a JPEG image or vice versa, with all possible combinations of quality factors. Alternatively, in the case of copy-move forgery, the duplication regions are identified using highly localized phase congruency features of each block. Experimental results are presented to consolidate the theoretical concept of the proposed technique and the performance is compared with the already existing state of art methods.
•This paper presents an integrated technique for detection and localization of splicing as well as copy-move forgery in JPEG compressed images.•The idea is to exploit doubly stochastic model of quantized discrete cosine transform (DCT) coefficients to detect forgery.•Splicing localization is performed using block-wise correlation maps of dequantized DCT coefficients and its recompressed version at different quality factors.•Copy-move forgery localization is performed by utilizing the principle moments of phase congruency.•The proposed scheme is robust against various pre- and post-processing transformations.
With the popularity and increasing demand of new energy automotive, four-wheel independent drive electric vehicles (4WID-EVs) and its distributed control mode is promising to be a better chassis ...architecture for its flexibility and maneuverability. However, due to the uncertainty of the vehicle internal time-varying parameters and imprecise fault detection method, a novel robust fault estimator-based stochastic model predictive control (SMPC) is proposed in this article to ensure the vehicle lateral motion control and track the desired longitudinal velocity considering the estimation error of motor failure degree. Firstly, a robust H-infinity fault estimator with a linear parameter-varying (LPV) model is established to estimate the real-time fault signal considering the external disturbance and parameter perturbation (i.g. longitudinal and lateral vehicle speed). Then, a SMPC controller framework is developed to alleviate the effect of inevitable evaluated estimation error while the vehicle lateral safety and vehicle speed tracking can be ensured. Finally, the hardware-in-loop (HIL) tests are conducted to prove the feasibility of SMPC controller. The experiment results indicate that the proposed controller framework can effectively preserve vehicle stability and keep vehicle in a healthy state with faster response compared with the traditional fault-tolerant controller.
Ba0.4Sr0.6TiO3 (BST) ceramics with Al2O3 additives are synthesized by spark plasma sintering (SPS) to enhance the energy storage density. Numerical simulations based on a stochastic model are carried ...out to further understand the breakdown behaviors in BST/Al2O3 ceramics. Greatly enhanced dielectric breakdown strength from 210 kV/cm to 300 kV/cm is realized in BST-1 %Al2O3 ceramics due to the optimized microstructure and the high insulation of Al2O3 additives. The maximum energy storage density of 1.69 J/cm3 is obtained, which is 1.4 times larger than pure BST ceramics. Based on the simulations, it is found that the discharge channels try to bypass the particles due the high critical breakdown field of Al2O3, which will extend the breakdown path in ceramics and contribute to the improved breakdown strength. However, after exceeding a certain amount of Al2O3 addition, the interface effect will be predominant and lead to the degradation of breakdown strength.
•Fully dense BST/Al2O3 ceramics were fabricated by spark plasma sintering (SPS).•Greatly enhanced energy storage density was realized in BST-1 %Al2O3 ceramics due to the high insulation of Al2O3 additives.•Numerical simulations based on a stochastic model were carried out to further understand the breakdown behaviors.
In this paper, we first propose a 3-dimensional (3-D) non-stationary geometry-based stochastic model (GBSM) for maritime massive multiple-input multiple-output (MIMO) communication systems with the ...uniform circular array (UCA) configuration. To reduce the model complexity and improve the mathematical tractability, a novel beam domain channel model (BDCM) is then proposed based on the transformation of the corresponding GBSM from the array domain to the beam domain for maritime communications. In the proposed BDCM, the beamforming matrices suitable for UCA structures are constructed and their invertibility is demonstrated to ensure the practicability of the BDCM. Two methods are used to characterize the array non-stationarity in maritime massive MIMO channels. First, the evolution of clusters over the large UCA is modeled by the visibility regions (VRs) attached to individual multipath components (MPCs). Second, the sphere wavefront (SWF) effect is captured by dividing the UCAs into several sub-arrays. Based on the proposed GBSM and BDCM, some important channel statistical properties are studied and compared, including channel power, power leakage, space-time-frequency correlation function (STF-CF), and root-mean-square (RMS) Doppler/beam spreads. Also, the importance of considering the array non-stationarity in maritime communication channels is revealed.
We present a stochastic model predictive control (MPC) method for linear discrete-time systems subject to possibly unbounded and correlated additive stochastic disturbance sequences. Chance ...constraints are treated in analogy to robust MPC using the concept of probabilistic reachable sets for constraint tightening. We introduce an initialization of each MPC iteration which is always recursively feasible and guarantees chance constraint satisfaction for the closed-loop system, which is typically challenging for systems under unbounded disturbances. Under an i.i.d. zero-mean assumption, we provide an average asymptotic performance bound. A building control example illustrates the approach in an application with time-varying, correlated disturbances.
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