Prior Statistical Information Research Articles

Bayesian inversion for in-situ thermal characterisation of walls in the presence of thermal anomalies

This paper develops a Bayesian inverse modelling framework for the in-situ characterisation of walls' thermal performance in the presence of thermal anomalies subject to uncertainty. For any given wall, the proposed framework uses in-situ contact measurements of temperature and heat flux in order to approximate the (posterior) distribution of a set of parameters which are inputs of a 3D heat transfer model of the wall that accounts for the presence of unknown anomalies. The set of inputs that we infer include (i) the geometry of the thermal anomaly as well was its material properties, (ii) the as-built thermophysical properties of the clear part (i.e. without anomaly) of the wall (iii) surface resistances and (iv) modelling errors that arise from unaccounted sources of uncertainty in the boundary conditions. The geometry of the unknown thermal anomaly is parameterised with a level-set function that is inferred within the proposed Bayesian approach. In order to incorporate prior statistical information of the thermal anomaly, a Gaussian process conditioned on measurements from thermal images is employed to build a prior on the level-set function. The posterior distribution of inferred inputs is approximated via the implementation of an Ensemble Kalman Inversion algorithm that provides stable and accurate approximations of the posterior. This paper also proposes a methodology that uses the inferred 3D model to derive a thermally equivalent 1D model of the wall that is suitable for its integration with existing building energy performance software. The proposed inverse modelling technique is experimentally validated by characterising the thermal performance of an insulation panel in the presence of a thermal anomaly. Our computations show that the proposed approach produces a 3D model that accurately describes thermal performance, and a derived equivalent 1D model that effectively reproduces the dynamic thermal performance of the insulation panel in the presence of the thermal anomaly.

Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas.

A fully automated artificial pancreas requires a meal estimator and predictions of blood glucose levels (BGL) to handle disturbances during meal times, all without relying on manual meal announcements and user interventions. This study introduces a technique for estimating the glucose appearance rate (GAR) and predicting BGL in people with type 1 diabetes and insulin and glucagon administration. It is demonstrated for intraperitoneal insulin and glucagon delivery but may be adapted to other delivery sites. The estimator is designed based on the moving horizon estimation (MHE) approach, where the underlying cost function incorporates prior statistical information on the GAR in subjects over the course of a day. The proposed prediction scheme is developed to predict GAR using estimated states and an intestinal model, which is then used to predict BGL with the help of an animal glucose metabolic model. The intraperitoneal dual-hormone estimator was evaluated on three anesthetized animals, achieving a 21.8% mean absolute percentage error (MAPE) for GAR estimation and a 10.0% MAPE for BGL prediction when the future GAR is known. For a 120-minute prediction horizon, the proposed predictor achieved an 18.0% MAPE for GAR and a 28.4% MAPE for BGL. The findings demonstrate the effectiveness and reliability of the proposed estimator and its potential for use in a fully automated artificial pancreas and reducing user interventions. This study represents advancements toward the development of a fully automated artificial pancreas, ultimately enhancing the quality of life for people with type 1 diabetes.

An adaptive algorithm for voice quality based on big data voiceprint identification

Abstract This paper improves the speech amplitude in Bayesian speech enhancement estimation by introducing a super-Gaussian cardinality distribution probability density function in the filter's construction. The derivation is combined with the perceptual error function, the new probability density function and the perceptual error cost function to better exploit and utilize the prior statistical information of the speech. The results show that the proposed method can improve the signal-to-noise ratio up to 0.7 dB under different noises and different signal-to-noise ratios, and the processed speech has better feasibility, which provides good speech enhancement for the processing of noisy speech quality in vocal identification practice without significantly increasing the computational complexity and can be better adapted to the application.

Drone-based Volume Estimation in Indoor Environments

Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous systems using visual features for indoor localization and surface reconstruction from 2D-LiDAR measurements. A Gaussian Process-based model incorporates information collected from measurements given statistical prior information about the terrain, from which the volume estimate is computed. Our algorithm finds feasible trajectories which minimize the uncertainty of the volume estimate. We show results in simulation for the surface reconstruction and volume estimate of topographic data.

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution (SR) methods rely on the assumption that the degradation process is predefined (e.g., bicubic downsampling). Thus, their performance would suffer from deterioration if the real degradation is not consistent with the assumption. To deal with real-world scenarios, existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme. However, degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors. In this paper, we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples, respectively. Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space. Furthermore, instead of estimating the degradation, we extract global statistical prior information to capture the character of the distortion. Considering the coupling between the degradation and the low-resolution image, we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions. We term our distortion-specific network with contrastive regularization as CRDNet. The extensive experiments on synthetic and real-world scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.

Linear time-varying matched filter for known and unknown SOI generalized cyclostationary signal with multiple cyclic frequencies

Chirp cyclic matched filter has been designed for chirp cyclostationary (CCS) signals, a kind of generalized cyclostationary signals, recently. However, it is designed based on statistical prior information and waveform of the signal-of-interest (SOI) CCS signal, which works only for CCS signal with a single chirp cyclic frequency. As known, the prior information of SOI signal may not be exactly known or the measured waveform of SOI signal is noised in practical applications, which decreases performance of the algorithm. In this study, by decomposing the imprecisely known SOI signal as the summation of SOI signal and an unknown additive disturbance, three kinds of chirp cyclic matched filters are designed according to the value of signal-to-noise ratio (SNR). Furthermore, noticing the CCS signal usually contains multiple chirp cyclic frequencies, three kinds of combined chirp cyclic matched filter are designed for known/unknown SOI CCS signals by linearly combining the chirp cyclic matched filter at every chirp cyclic frequency. Moreover, ramifications of the truncation process and complexity of the proposed algorithms are analyzed. Finally, simulation results validate the improvement of the chirp cyclic matched filter based on multiple chirp cyclic frequencies than on a single chirp cyclic frequency in different SNR situations.

DPSF: a Novel Dual-Parametric Sigmoid Function for Optical Coherence Tomography Image Enhancement.

Speckle noise reduces the image contrast significantly making the highly scattering structures boundaries difficult to distinguish. This has limited the usage of optical coherence tomography (OCT) images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision making. To overcome this limitation, we propose a fast and robust OCT image enhancement framework using non-linear statistical parametric technique. In the proposed framework, we utilize prior statistical information to model the image to follow Gaussian distribution. After which, a newly designed dual-parametric sigmoid function (DPSF) is utilized to control the dynamic range and contrast level of the image. To balance the intensity range and contrast level, both linear and non-linear normalization operations are performed, then followed by a mapping operation to obtain the enhanced image. Experimentation results on the three OCT vendors show that the proposed method obtained high values in EME, PSNR, SSIM, ρ, and low value in MSE of 36.72, 38.87, 0.87, 0.98, and 25.12 for Cirrus; 40.77, 41.84, 0.89, 0.98, and 22.15 for Spectralis; and 30.81, 32.10, 0.81, 0.96, and 28.55 for Topcon OCT devices, respectively. The proposed DPSF framework performs better than the state-of-the-art methods and improves the interpretability and perception of the OCT images, which can provide clinicians and computer vision program with good quantitative and qualitative information.

An Intelligent Anti-Jamming Mechanism against Rule-based Jammer in Cognitive Radio Network

Cognitive Radio Network (CRN) has become a promising technology to overcome the problem of insufficient spectrum utilization. However, the CRN is susceptible to the well-known jamming attack, which reduces its spectrum utilization efficiency. Existing jamming identification schemes and their countermeasure typically require prior statistical information about the communication channel and jamming pattern. This is quite an impractical assumption in the real context. The prime research problem is that the existing schemes are mainly associated with higher computational costs and communication overhead. Hence, the proposed manuscript presents a non-device-centric and efficient anti-jamming mechanism in the form of higher spectrum utilization driven by reinforcement learning techniques to address this above-stated problem. The proposed anti-jamming mechanism is modeled in two phases of implementation. First, the design of the customized environment is introduced as a single wideband cognitive-communication channel where a jammer signal sweeps transversely in the entire band of interest. Secondly, an intelligent agent is designed based on a model-free off-policy algorithm that operates over the same spectrum band. The agent uses its frequency-band knowledge discovery capability to learn frequency band selection and preference strategies to detect and avoid jamming signals, maximizing its successful transmission rate. The simulation results show that the proposed anti-jamming mechanism can effectively eliminate interference and is efficient in power usage and Signal to Noise Ratio (SNR) compared to other existing advanced algorithms.

An adaptive cubature Kalman filter for nonlinear systems against randomly occurring injection attacks

The problem of state estimation for nonlinear dynamic systems in the presence of randomly occurring injection attacks (ROIAs) is investigated. This paper requires no prior statistical information of the attacks, which relaxes the assumption of the existing result that the attack probability and the probability density function of attack signals need to be known. With the distribution of the attack probability and attack signals modeled as Beta distribution and Gaussian mixture distribution, a variational Bayesian based adaptive cubature Kalman filter is proposed to approximate the joint posterior distribution of the system state vector and unknown parameters. In addition, the update rules of the state and the statistical parameters of attacks are analytically derived by employing the fixed-point iteration approach. Finally, the effectiveness of the proposed filter is validated through numerical results.

Probabilistic Linear Inversion of Satellite Gravity Gradient Data Applied to the Northeast Atlantic

AbstractWe explore the mantle density structure of the northeast Atlantic region using constrained linear inversion of the satellite gravity gradient data based on statistical prior information and assuming a Gaussian model. The uncertainty of the residual gravity gradient signal is characterized by a covariance matrix obtained using geostatistical analysis of controlled‐source seismic data. The forward modeling of the gravity gradients in the 3D reference crustal model is performed using a global spherical harmonics analysis. We estimate the model covariance function in the radial and angular directions using a variogram method. We compute volumetric gravity gradient kernels for a spherical shell covering the northeast Atlantic region down to the mantle transition zone (410 km depth). The solution of the linear inverse problem in the form of the mean density model and the posterior covariance matrix follows a least squares approach. The results indicate that on average the seismic velocity variation is proportional to the density variation in the northeast Atlantic region. However, a noticeable mismatch or anti‐correlation exists in some areas, such as the Greenland‐Iceland‐Faroe ridge and southwestern Norway. The predicted low‐density anomalies at depths of 100–150 km underneath the northeast Atlantic Ocean are correlated with the distribution of Cenozoic submarine volcanoes and seamount‐like features of the seafloor.

Agricultural machinery GNSS/IMU-integrated navigation based on fuzzy adaptive finite impulse response Kalman filtering algorithm

This study uses global navigation satellite system (GNSS) positioning equipment and inertial measurement unit integrated with accelerometer and gyroscope to improve the accuracy and stability of the current agricultural machinery automatic navigation technology. Considering the actual motion state of agricultural machinery in operation, a fuzzy adaptive finite impulse response Kalman filter (FA-FIR-KF) algorithm was proposed to integrate position information and attitude information, and some necessary auxiliary optimization algorithms were introduced to make innovative improvements. The introduction of quaternion method can suppress the actual nonlinear problem of the agricultural machinery coordinate caused by the attitude angle. A fuzzy inference system was adopted to improve the adaptive adjustment ability to abnormal noise. A forgetting factor was adopted to reduce the system’s excessive dependence on prior statistical information, so that the system can quickly track the abrupt signal. The algorithm simulation program was written on MATLAB, and the performance and effect of the proposed algorithm were verified through simulation and farm experiments. Simulation results of artificially added noise simulation data show that the localization precision in the Xn, Yn, and Zn directions increases by 38.95%, 38.88%, and 32.99%, respectively. This finding indicates that the FA-FIR-KF algorithm can effectively suppress the Gaussian white noise of the GNSS received signal and improve the positioning accuracy of agricultural machinery. The reliability of this algorithm applied to the automatic navigation system was verified through a tractor straight-line navigation experiment. The tractor conducts an automatic navigation test at a speed of 0.8 m/s. Under the GNSS differential state, the average error and root mean square error (RMSE) are 1.074 and 1.396 cm in filtering case and 1.17 and 1.551 cm in nonfiltering case, respectively. Under the GNSS nondifferential state, the average error and RMSE are 2.097 and 2.72 cm in filtering case and 3.663 and 4.633 cm in nonfiltering case, respectively. Compared with the nonfiltering case, the average error and RMSE reduce by 8.21% and 9.99% in the differential state and 42.75% and 41.32% in the nondifferential state, respectively. Test results show that the proposed algorithm can make the agricultural machinery track the desired path more smoothly, stably, and accurately than in the nonfiltered case, and the tracking accuracy is at the centimeter level.

Probabilistic and deterministic wind speed forecasting based on non-parametric approaches and wind characteristics information

As the proportion of the total installed wind capacity continues to increase, precise probabilistic and deterministic wind speed forecasting becoming increasingly significant for wind turbine stable function and operation management. It is worth noting that except for the intermittent nature of wind speed itself, the site dependence of wind energy and the heterogeneous nature of the distribution at different locations can also affect the results of wind speed forecasting. Considering this has often been overlooked in previous studies, this study attempts to introduce characteristic information of wind from wind farms into wind speed probabilistic forecasting. For the sake of acquiring a more rational descriptive statistical interpretation of wind speed, a variety of probability density functions for actual wind speed data are evaluated in this study. And then, Generalized Gaussian Process models are constructed via observation likelihood functions which are determined by obtained probability density functions. Beyond that, to enhance the robustness against model misspecification, a pseudo-likelihood for the Leave-One-Out cross-validation methodology is proposed for approximating the posterior in the Bayesian inference stage. Wind speed forecasts on 6-hour-ahead horizons from two wind farms in China illustrated that the appropriate mining of wind prior statistical characteristic information can contribute to improving the precision of probabilistic wind speed forecasting.

Pilot and data power allocation for spatially correlated massive MIMO systems with imperfect CSI

This paper investigates pilot and data power allocation for a multi-cell spatially correlated massive multiple-input multiple-output (MIMO) system with imperfect channel state information (CSI). By exploiting the availability of statistical CSI, the uplink closed-form spectral efficiency (SE) expressions are derived for both full covariance knowledge case and no prior statistical information case. In order to enhance the accuracy of channel estimation, novel pilot power allocation schemes are firstly designed by minimizing the sum normalized mean square error (NMSE) of all users. Then, based on the optimal pilot power, data power allocation schemes are proposed to maximize the sum SE of system. For solving these non-convex problems, an iterative optimization algorithm is developed to obtain the local optimal solution by exploiting second-order channel statistics of users. Compared with the traditional method, numerical results show that the proposed pilot and data power allocation schemes can achieve higher SE for spatially correlated massive MIMO system.

Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems.

Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.

Probabilistic back analysis for improved reliability of geotechnical predictions considering parameters uncertainty, model bias, and observation error

The predicted performance of a geotechnical system may deviate from the in situ observation due to the uncertainties in the input geotechnical parameters, solution model, and observation error. A precise characterization of these uncertainties is a significant challenge primarily because of limited data availability. The Bayesian theory provides a means for updating these uncertainties by incorporating prior statistical information and observations. However, conventional Bayesian inference focuses on limited sources of uncertainties. This paper presents a probabilistic back analysis method for improved reliability of subsequent predictions that considers all the uncertainties. Three distinct features of this new method include: (1) multiple observations are incorporated into the Bayesian updating, (2) the statistical information of the uncertain variables is updated in a stage-by-stage manner, and (3) the posterior distributions of uncertain variables are derived with Markov Chain Monte Carlo (MCMC) simulation that is based on the Hamiltonian Monte Carlo (HMC) algorithm. Two case histories, including a braced excavation problem and a tunnel excavation problem, are analyzed to demonstrate the effectiveness of the new method. The advantages of this new back analysis method over the conventional Bayesian updating analyses are documented.

Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast

The Bayesian vector autoregressive (BVAR) model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive (VAR) model, which can overcome the problem of too little freedom. The BVAR model established in this paper can overcome the problem of short time series data by using prior statistical information. In theory, it should have a good effect in China’s regional economic forecasting. Most regional forecasting model literature lacks out-of-sample forecasting error evaluation research in the real sense, but our early forecasts of major economic indicators provide an excellent opportunity for this paper to evaluate the actual forecast errors of the BVAR model in detail. The analysis in this paper shows that the prediction error of the BVAR model is very small and the prediction ability is very satisfactory. At the same time, this article also analyzes and points out the direction of efforts to further improve the prediction accuracy of the BVAR model.

A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing

In this study, a novel deep convolutional neural network-bootstrap-based integrated prognostic approach for the remaining useful life (RUL) prediction of rolling bearing is developed. The proposed architecture includes two main parts: 1) a deep convolutional neural network–multilayer perceptron (i.e., DCNN–MLP) dual network is utilized to simultaneously extract informative representations hidden in both time series-based and image-based features and to predict the RUL of bearings, and 2) the proposed dual network is embedded into the bootstrap-based implementation framework to quantify the RUL prediction interval. Unlike other deep-learning-based prognostic approaches, the proposed DCNN-bootstrap integrated method has two innovative features: 1) both 1D time series-based and 2D image-based features of bearings, which can multi-dimensionally characterize the degradation of bearings, are comprehensively leveraged by the proposed dual network, and 2) the RUL prediction interval can be effectively quantified without relying on the bearing’s physical or statistical prior information based on bootstrap implementation paradigm. The proposed approach is experimentally validated with two case studies on rolling element bearings, and comparisons with other state-of-the-art techniques are also presented. Subsequently, our code will be open sourced.

A high dynamics algorithm based on steepest ascent method for GNSS receiver

High dynamic conditions impose critical challenges on Global Navigation Satellite System (GNSS) receivers, leading to large tracking errors or even loss of tracking. Current methods that intend to improve receivers’ adaptability for high dynamics require either complicated structures or prior statistical information of noises. This paper proposes a high dynamics algorithm based on steepest ascent method that can circumvent the deficiencies of existing methods. First, the relationship between the error of carrier tracking and the maximum of Fast Fourier Transform (FFT) outputs is established, and a performance function based on the steepest ascent method is designed. It can keep stable in high dynamics. Second, a new carrier-tracking loop is constructed by deploying the performance function. When the variation of GPS receiver acceleration ranges from 10 g to 100 g, compared with the PLL that either loses lock or keeps tracking accuracy less than 33.89 Hz, the experimental results show that the proposed method can not only keep tracking, but also achieve tracking accuracy more than 2.77 Hz.

Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography

Image based diagnostics are interpreted in the context of spatial resolution. The same is true for tomographic image reconstruction. Current empirically driven approaches to quantify spatial resolution in chemical species tomography rely on a deterministic formulation based on point-spread functions which neglect the statistical prior information, that is integral to rank-deficient tomography. We propose a statistical spatial resolution measure based on the covariance of the reconstruction (point estimate). By demonstrating the resolution measure on a chemical species tomography test case, we show that the prior information acts as a lower limit for the spatial resolution. Furthermore, the spatial resolution measure can be employed for designing tomographic systems under consideration of spatial inhomogeneity of spatial resolution.

Quantifying the spatial resolution of the maximum a posteriori estimate in linear, rank-deficient, Bayesian hard field tomography

Image based diagnostics are interpreted in the context of spatial resolution. The same is true for tomographic image reconstruction. Current empirically driven approaches to quantify spatial resolution in chemical species tomography rely on a deterministic formulation based on point-spread functions which neglect the statistical prior information, that is integral to rank-deficient tomography. We propose a statistical spatial resolution measure based on the covariance of the reconstruction (point estimate). By demonstrating the resolution measure on a chemical species tomography test case, we show that the prior information acts as a lower limit for the spatial resolution. Furthermore, the spatial resolution measure can be employed for designing tomographic systems under consideration of spatial inhomogeneity of spatial resolution.