Joint State Research Articles

Particle Filter based on Jaya optimisation for Bayesian updating of nonlinear models

Particle filter (PF) is a powerful and commonly used filtering technique based on Sequential Monte Carlo framework. The main challenge in using PF for nonlinear state and parameter estimation is the degeneracy of particles. Although resampling techniques can solve this to some extent, it would still result in particle impoverishment when a limited number of particles are used thereby affecting the accuracy. Hence, a hybrid metaheuristic optimisation algorithm that combines the PF with Jaya optimisation, (PF-JAYA) has been proposed and implemented for joint state and parameter estimation for geotechnical engineering problems. The performance of PF-JAYA has been compared against the traditional Particle Filter with Sampling Importance Resampling (PF-SIR) technique. The synthetic examples show that PF-JAYA outperforms PF-SIR in terms of accuracy, rate of convergence, parameter identification and particle diversity. Furthermore, the performance of PF-JAYA is independent of the choice of prior distribution and due to its superior convergence proves to be efficient when working with sparse monitoring information. The performance of PF-JAYA on Bayesian updating of state and parameters of an elastoplastic model for a synthetic embankment case has also been evaluated where, along with PF-SIR, the Ensemble Kalman Filter (EnKF) is also chosen for comparison. Finally a further evaluation using the Lorenz ‘63 model, shows the superior performance of PF-JAYA in terms of accuracy and precision over the classical Data Assimilation techniques.

A Joint State and Fault Estimation Scheme for State-Saturated System with Energy Harvesting Sensors.

In this article, the issue of joint state and fault estimation is ironed out for delayed state-saturated systems subject to energy harvesting sensors. Under the effect of energy harvesting, the sensors can harvest energy from the external environment and consume an amount of energy when transmitting measurements to the estimator. The occurrence probability of measurement loss is computed at each instant according to the probability distribution of the energy harvesting mechanism. The main objective of the addressed problem is to construct a joint state and fault estimator where the estimation error covariance is ensured in some certain sense and the estimator gain is determined to accommodate energy harvesting sensors, state saturation, as well as time delays. By virtue of a set of matrix difference equations, the derived upper bound is minimized by parameterizing the estimator gain. In addition, the performance evaluation of the designed joint estimator is conducted by analyzing the boundedness of the estimation error in the mean-squared sense. Finally, two experimental examples are employed to illustrate the feasibility of the proposed estimation scheme.

Classification and rating of disintegrated dolomite strata for slope stability analysis

Although disintegrated dolomite, widely distributed across the globe, has conventionally been a focus of research in underground engineering, the issue of slope stability issues in disintegrated dolomite strata is gaining increasing prominence. This is primarily due to their unique properties, including low strength and loose structure. Current methods for evaluating slope stability, such as basic quality (BQ) and slope stability probability classification (SSPC), do not adequately account for the poor integrity and structural fragmentation characteristic of disintegrated dolomite. To address this challenge, an analysis of the applicability of the limit equilibrium method (LEM), BQ, and SSPC methods was conducted on eight disintegrated dolomite slopes located in Baoshan, Southwest China. However, conflicting results were obtained. Therefore, this paper introduces a novel method, SMRDDS, to provide rapid and accurate assessment of disintegrated dolomite slope stability. This method incorporates parameters such as disintegrated grade, joint state, groundwater conditions, and excavation methods. The findings reveal that six slopes exhibit stability, while two are considered partially unstable. Notably, the proposed method demonstrates a closer match with the actual conditions and is more time-efficient compared with the BQ and SSPC methods. However, due to the limited research on disintegrated dolomite slopes, the results of the SMRDDS method tend to be conservative as a safety precaution. In conclusion, the SMRDDS method can quickly evaluate the current situation of disintegrated dolomite slopes in the field. This contributes significantly to disaster risk reduction for disintegrated dolomite slopes.

Image of the Soviet Authorities in the Perception of the Siberian Peasants during the Russian Civil War and the Period of New Economic Policy

The article explores the ideas of the Siberian peasantry regarding Soviet authorities during the Russian Civil War and the Period of Implementation of the New Economic Policy (NEP). The importance of this issue lies in the fact that the population’s perception of the governing bodies plays a significant role in the life of any society since it influences the political sentiments and the behavior of different social groups. However, this topic has not been extensively studied by Russian or international historiography. The research is based on a wide range of official documents, including newly discovered ones, produced by various Communist Party (Bolsheviks) bodies, the Joint State Political Directorate, and letters from ordinary people to authorities. The authors of the article analyze how different categories of Siberian peasants imagined the Soviet power vertical, what factors and how influenced formation and transformation of ideas about authorities during the Russian Civil War and NEP period. The article concludes by arguing that different social groups of the Siberian peasantry had different perceptions of Soviet authorities, which did not remain unchanged but varied over time. From the end of 1917 until the end of 1919, ideas about the Soviet authorities arose mainly independently, without significant external influence. The situation changed after the establishment of the dictatorship of the proletariat. The government’s policies and practices of implementation were the determining factors that shaped the image of the Soviet authorities throughout the 1920s

A Copula-based framework for joint encounter and state evolution analysis of urban short-duration rainstorm characteristics

In this study, in order to reveal the joint encounter and state evolution characteristics of urban short-duration rainstorms, a case study is conducted in Zhengzhou city, China. The joint probability distribution model of total rainfall (P) and peak rainfall (R) is constructed by using the Copula function, and their joint encounter and joint return period are analyzed. Additionally, the state evolution characteristics of P and R are explored by incorporating with the Markov chain. The results show that: (1) there is a good correlation between P and R under different rainfall durations. The Clayton Copula function is found suitable for 0.5 h and 1 h rainfall durations, while the Frank Copula function is selected for the 1.5 h rainfall duration. The P and R are more densely distributed in the joint probability interval of 0.2–0.7 and the joint return period of rainfall events (JRPRE) of 15-30freq, which are particularly sensitive to the changes in the corresponding intervals. (2) The probability of synchronous rich (F)-low (L) between P and R is greater than that of asynchronous F-L. The probability of synchronous F-L increases with longer rainfall durations, reaching the highest value of 74.92 % for the 1.5 h rainfall duration. (3) The transition probability and limit probability of the F/high (H) state and F-H combination state are relatively large, indicating that P and R exhibit strong self-conservation. These states are associated with the highest risk and tend to occur more frequently with shorter average return periods of rainfall events (ARPRE). The findings are valuable in uncovering the occurrence patterns of short-duration rainstorms and facilitating measures to mitigate waterlogging, thereby supporting urban sustainable development.

Distributed joint parameter and state estimation algorithm for large‐scale interconnected systems

SummaryThis paper proposes a distributed joint parameter and state variables estimation algorithm for large‐scale state‐space interconnected systems. In this distributed estimation scheme, each interconnected sub‐system is described by a linear discrete‐time state space mathematical model. Each sub‐system is supposed to be controlled by an intelligent controller that can communicate with its interconnected neighbors and exchange information, such as state variables. The proposed approach comprises two recursive estimation algorithms, a parameter estimation algorithm considering the state space model and a distributed Kalman filter for state variables estimation. It is a fully distributed cooperative approach that allows to reduce complexity and saves computational and communication resources. Theoretical analysis and numerical examples are provided to prove the feasibility and effectiveness of this joint estimation algorithm.

Evaluating the impact of the Medicaid expansion program on diabetes hospitalization.

Diabetes is the most expensive chronic disease in the United States, and hospital inpatient care accounts for 30% of the total medical expenditures. Medical costs for people withlimited resources are covered by Medicaid, a joint federal and state program, and its expansion that extent the coverage to those with incomes up to 138% of the federal poverty level. We investigated the impact of Medicaid expansion on diabetes hospitalizations by states and payer, among adults aged 19 to 64years old, 5years after the expansion. We found that Medicaid expansion decreased total diabetes hospitalization in most states and a diabetes hospitalization payer mix shifted from private insurance and uninsured to Medicaid. The percentage of diabetes hospitalizations paid by Medicaid increased by 11% (95% CI 7%, 16%), while the percentage paid by private insurance decreased by 6% (95% CI -8%, -3%) and the percentage of uninsured diabetes hospitalization decreased by 13% (95% CI -18%, -9%).

Joint condition monitoring framework of wind turbines based on multi-task learning with poor-quality data

Effective condition monitoring can improve the reliability of the turbine and reduce its downtime. However, due to the complexity of the operating conditions, the monitoring data is always mixed with poor-quality data. Poor-quality data mixed in monitoring tasks disrupts long-term dependency on data, which challenges traditional condition monitoring methods to work. To solve it, a joint reparameterization feature pyramid network (JRFPN) is proposed. Firstly, three different reparameterization tricks are designed to reform temporal information and exchange cross-temporal information, to alleviate the damage of long-term dependency. Secondly, a joint condition monitoring framework is designed, aiming to suppress feature confounding between poor-quality data and faulty data. The auxiliary task is trained to extract the degradation trend. The main task fights against feature confounding and dynamically delineates the failure threshold. The degradation trend and failure threshold decisions are corrected for each other to make the final joint state inference. Besides, considering the different quality of the monitoring variables, a channel weighting mechanism is designed to strengthen the ability of JRFPN. The measured data proved that JRFPN is more effective than other methods.

Joint State and Unknown Input Estimation for a Class of Artificial Neural Networks With Sensor Resolution: An Encoding-Decoding Mechanism.

This article is concerned with the joint state and unknown input (SUI) estimation for a class of artificial neural networks (ANNs) with sensor resolution (SR) under the encoding-decoding mechanisms. The consideration of SR, which is an important specification of sensors in the real world, caters to engineering practice. Furthermore, the implementation of the encoding-decoding mechanism in the communication network aims to accommodate the limited bandwidth. The objective of this study is to propose a set-membership estimation algorithm that accurately estimates the state of the ANN without being influenced by the unknown input while accounting for the SR and the encoding-decoding mechanism. First, a sufficient condition is derived to ensure an ellipsoidal constraint on the estimation error. Then, by addressing an optimization problem, the design of the estimator gains is accomplished, and the minimal ellipsoidal constraint on the state estimation error is obtained. Finally, an example is provided to confirm the validity of the proposed joint SUI estimation scheme.

Joint State of Charge and State of Health Estimation Using Bidirectional LSTM and Bayesian Hyperparameter Optimization

Joint State of Charge and State of Health Estimation Using Bidirectional LSTM and Bayesian Hyperparameter Optimization

Cultural elites as selfless ethnic entrepreneurs in the fall of Yugoslavia?

In socialist Yugoslavia, cultural elites were structurally related to the communist ruling elite/s, but the relationship was not simple. Cultural elites were always constituted at the national (republic) level, whereas the political elite was initially a single Yugoslav one, but later, it fractured along the same lines. National cultural elites soon ceased to be anything like transmissions of the ruling one, nor were they unconditionally subjugated to it/them. All cultural elites had a proclivity to view issues as unfavourable towards their own nationhood. The Croat, Serb, and Slovene viewed the Yugoslav arrangement as somehow suppressing, exploiting, or restricting their respective nationhood. This sprang up in literary works, in intellectual production, in the issue of whether Serbo-Croat was a single language. These issues were brought about disputes, which grew from intellectual ones into inter-ethnic ones. These disputes contributed significantly to the disintegration of firstly, the weak Yugoslav cultural nucleus, and secondly of trust among nationhoods and finally they brought about a comprehension of an impossibility of a joint state. Most of these actions could be designated as ethnic entrepreneurship.

Illegal Church Document from the Era of Persecution (1920–1930 of the 20th Century)

The research of the history of the Russian Orthodox Church of the 20th century is largely based on the study of documents of that era drawn up in the church milieu. Since all kinds of church publications were prohibited, the reproduction of documents in private — by copying or retyping — gained ground in Russia of the 1920s and 1930s. These documents played the role of the church press — they introduced the events of church life, expressed beliefs in the rightness of the author or a group of like-minded people, unmasked ideological opponents, and also served to communicate with foreign church figures. The reproduction of such documents, in case of their discovery by authorities, could endanger not only the authors thereof and the persons mentioned therein, but also ordinary copyists and put them all to punitive measures such as purge. Thanks to the ascetics who preserved them, these documents remain the most important information sources in the field of history of the Russian Orthodox Church in the era of persecution. The article lists the documents that were taken abroad — letters of Bishop Damaskin (Cedric), the collection of church documents “The Case of Metropolitan Sergius”. Moreover, it analyzes documents both originating from the clerical office of Metropolitan Sergius (Stragorodsky) and generated by church figures opposed to Deputy Patriarchal Locum Tenens. A large number of documents was drawn up by Mikhail Novosyolov himself and the circle of his close associates. Also, the most important documents of that era are the letters of Metropolitan Kirill (Smirnov) and replies of Metropolitan Sergius (Stragorodsky). The article pays special attention to the transfer abroad of the collection of church documents entitled “The Case of Metropolitan Sergius”, the role of the journalist Mikhail Brоndsted (pseudonym: Mikhail Artemyev), who left abroad in 1930, and his articles published abroad on underground literature in the Soviet Union. The problem of the authorship of anonymous sources, the authenticity of documents distributed in the church milieu is also raised here. The Joint State Political Directorate of the Soviet Union actively used church documents found during arrests to persecute believers. Fragments of these documents often became the basis for indictments, as evidence of the accused’s anti-Soviet activity. The article also mentions the role of collectors of church documents during the persecution against the Church — Archpresbyter Michael Polsky, Mikhail Gubonin, Metropolitan Manuel (Lemeshevsky).

Multi-Track Diplomacy of the Joint State Actor and Non-State Actor Proposed the Maros Pangkep Geopark to Become a UNESCO Global Geopark

The Maros area has been initiated to be developed into a national geopark in 2015, then to maintain,protect, preserve and be recognized globally. The Maros area is proposed to become a UNESCOGlobal Geopark in 2021. This research aims to understand the process of the government,stakeholders, and the community around the geopark proposed that the Maros Geopark become aUNESCO Global Geopark (UGG). This research used a qualitative method with a descriptivenarrative approach from various sources, articles, websites, and news. The findings from this researchthrough multi-track diplomacy, which focuses on track one (state) and track two (non-state actors),showed that the relevant actors succeeded in turning the Maros Pangkep Geopark into a UGG. Theannouncement was made at a council meeting in Thailand on September 4, 2022, followed by anofficial announcement on May 24, 2023, based on decisions taken at the 216th UNESCO ExecutiveBoard Session in Paris.

SoC Estimation in Lithium-Ion Batteries with Noisy Measurements and Absence of Excitation

Accurate State-of-Charge estimation is crucial for applications that utilise lithium-ion batteries. In real-time scenarios, battery models tend to present significant uncertainty, making it desirable to jointly estimate both the State of Charge and relevant unknown model parameters. However, parameter estimation typically necessitates that the battery input signals induce a persistence of excitation property, a need which is often not met in practical operations. This document introduces a joint state of charge/parameter estimator that relaxes this stringent requirement. This estimator is based on the Generalized Parameter Estimation-Based Observer framework. To the best of the authors’ knowledge, this is the first time it has been applied in the context of lithium-ion batteries. Its advantages are demonstrated through simulations.

The line-of-sight peak detection and tracking of underwater acoustic DSSS communications in the doubly spread channel

In the underwater acoustic (UWA) channel, the line-of-sight (LOS) path has the characteristics of stable magnitude and continuous phase, which is seldom affected by the surface/bottom reflection. Therefore, in the direct sequence spread spectrum (DSSS) communication systems, the receiver usually extracts the LOS path to recover the information bits and achieve the robust communication. However, the UWA channel is typically the doubly spread channel, i.e., multipath fast fading and non-uniform Doppler shifts, which seriously degrades the performance of extracting the LOS path by the conventional maximum energy method. Therefore, this paper proposes a LOS peak detection and tracking algorithm to extract the LOS path in the UWA DSSS communications. In the proposed algorithm, the candidate correlation peaks after de-spreading are firstly selected by the adaptive peak detection threshold. Then, according to the transmitter/receiver motion relationship, the Delay-Doppler joint state equation is established, and thus the nearest neighbor method adopting the Kalman filter is proposed to track the LOS path. Numerical simulation results demonstrate that the proposed algorithm has better processing performance in the doubly spread channels compared with the conventional method. Finally, the performance of the proposed algorithm is confirmed by the shallow water horizontal channel and deep sea vertical channel experiments.

Sequential Joint State Estimation and Track Extraction Algorithm Based on Improved Backward Smoothing

In order to further promote the accuracy of state estimation and track extraction capabilities for multi-target tracking, a sequential joint state estimation and track extraction algorithm is proposed in this article. This algorithm is based on backward smoothing under the framework of Labeled Random Finite Set and utilizes label iterative processing to perform outlier removal, invalid short-lived track removal, and track continuity processing in a sequential manner in order to achieve the goal of improving the multi-target tracking performance of the algorithm. Finally, this paper verifies through experimental simulation and measured data analysis that the proposed algorithm has improved the performance of radar multi-target tracking to a certain extent.

Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe

Abstract. Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.

Robust Bayesian state and parameter estimation framework for stochastic dynamical systems with combined time-varying and time-invariant parameters

We consider the joint state and parameter estimation for dynamical systems having both time-varying and time-invariant parameters. For systems with time-invariant parameters, it has previously been demonstrated that although nonlinear filters may be used for concurrent state and parameter estimation, employing the Markov Chain Monte Carlo (MCMC) algorithm for estimating time-invariant parameters with nested nonlinear filters for state estimation provides more reliable estimates. Following established methods for parameter estimation using filters, we augment the state vector to include the original system states in addition to the subset of the parameters that are known to vary in time. Conventionally, both time-varying and time-invariant parameters would be appended in the state vector, and thus for the purpose of estimation, both would free to vary in time. However, allowing parameters that are known to be time-invariant to change in time introduces non-physical dynamics into the original system. To avoid inducing artificial dynamics, the time-invariant parameters are estimated using MCMC. Furthermore, by estimating the time-invariant parameters by MCMC, the dimensionality of the augmented state is smaller and the nonlinearity in the ensuing state space model will tend to be weaker compared to the conventional approach. The time-varying parameters are perturbed by random noise processes, and the amplitude of the artificial noise is estimated alongside the time-invariant system parameters using MCMC. Estimating the noise strength in this procedure circumvents the need for manual tuning. We illustrate the above-described approach for a simple dynamical system in which some model parameters are time-varying, while the remaining parameters are time-invariant. We adopt the extended Kalman filter (EKF) for state estimation and the estimation of the time-varying system parameters, but reserve the task of estimating time-invariant parameters, including the model noise strength and the artificial noise strength associated with the time-varying parameter to the MCMC algorithm.

Joint State Sensing and Communication Systems With OFDM Transmission

Joint State Sensing and Communication Systems With OFDM Transmission

A suggestion-based hierarchical energy efficient control framework for connected and automated vehicles in mixed stream urban road networks

Connected and automated vehicles (CAVs) and their advanced control systems have the ability to improve energy efficiency and mobility of the transportation system, but hundred percent market penetration of CAVs will not happen in the near future. CAVs are thus expected to share the road with human-driven vehicles (HDVs), which can act in suboptimal manners. Since vehicles on road constitute an interactive system, where actions of one vehicle influence the actions of its surrounding vehicles, suboptimal behavior of HDVs affect all their surrounding vehicles including the CAVs. This paper thus focuses on the development of a suggestion-based control framework for CAVs in interactive mixed traffic environments. Our mixed traffic environment includes both CAVs and connected HDVs that have vehicle-to-vehicle (V2V) communication capabilities. In this suggestion-based control framework, a CAV is considered to provide velocity and lane-change suggestions to multiple surrounding HDVs to follow. These suggestions are the velocities and lane-change actions the CAV wants the HDVs to follow to improve its own and the group’s energy efficiency and mobility. To take into account the interactive nature of the environment, the CAV is considered to jointly plan its own trajectory, evaluate the suggestions to the surrounding HDVs, and predict the actions of its surrounding vehicles. The joint planning requires solving the problem in joint state- and action-space, and in this paper, we develop a Monte Carlo Tree Search (MCTS)-based trajectory planning approach for the CAV. Since the joint action- and state-space grows exponentially with the number of agents and can be computationally expensive, we propose an adaptive action-space by pruning the actions resulting in unsafe trajectories for each agent. The trajectory planning approach is followed by a low-level model predictive control (MPC)-based motion controller, which aims at tracking the reference trajectory in an optimal fashion. Simulation studies show the proposed control strategy’s efficacy compared with existing baseline methods. Simulation studies show improvements in fuel efficiency ranging from 4% to 50% for HDVs and from 1% to 30% for the CAV compared to the baseline.