Uncertain State Research Articles

Computationally efficient robust design in MIMO broadcast channels with norm bounded error models

Using multiple antennas at the transmitter or receiver sides of the communications systems is a trend with no seeming end in forthcoming standards beyond 5G. In such networks, implementing a reasonably realistic beamformer means to challenge with uncertain Channel State Information (CSI). In many published works, the original design problem is a Second Order Cone Programming (SOCP) problem, and the robust counterpart, aiming to solve the design problem with data uncertainty, is a Semidefinite Programming (SDP) problem. In this paper, we study the robust beamforming design of a Broadcast Channel (BC) in a Multiple-user Multiple-Input Multiple-Output (MU-MIMO) scenario by formulating the Sum Mean Square Error (S-MSE) minimization design problem into a single SOCP robust counterpart problem. Each semi-infinite SOCP constraint is replaced with another SOCP constraint while introducing a new slack variable as well. The arithmetic computational complexity of our method is compared with the well-known and well-studied SDP design in closed form, and it is shown that the proposed method is exponentially faster in real-world design scenarios. To better verify our proposal, extensive numerical simulations are carried out, and the results show similar performances.

El bayesianismo: de lo casuístico a lo histórico

Este documento presenta fundamentalmente dos aspectos relacionados al asunto Bayesiano: i) dos casos de aplicación de la epistemología Bayesiana, y, ii) una exposición resumida e intuitiva de la formalidad de las técnicas Bayesianas y su núcleo, el teorema de Bayes, culminando con una breve reseña histórica y una concepción de la epistemología Bayesiana. Apoyándose en la metodología de revisión documental bibliográfica, una caracterización general de un contexto de análisis Bayesiano describe dos situaciones cognitivas comprensivas de ese contexto, la inferencia Bayesiana y la teoría de decisión Bayesiana; además, se comentan aplicaciones Bayesianas generales y luego se dan dos casos específicos de aplicación: en el ejercicio médico cotidiano, y en la “plani-evaluación” de proyectos sociales. Una breve sección expone de manera muy básica las formalidades metodológicas Bayesianas interpretando el teorema de Bayes y el concepto de verosimilitud. Finalmente, una sección de cierre incluye una reseña histórica y una definición integradora de los principales componentes de la epistemología Bayesiana. La consolidación de epistemología Bayesiana ha requerido el aporte de muchas autoridades en el asunto; dos de los más influyentes pioneros mencionados en este documento son Aykac y Brumat (1977) y Lindley (1977). Como conclusión, la epistemología Bayesiana se presenta como una teoría de aprendizaje en incertidumbre respecto a eventos o estados aleatorios e inciertos de las cosas, de la naturaleza o de la realidad, aprendizaje o nuevo conocimiento que es expresado en términos de probabilidades.

Social learning with coordination motives

We study observational learning among agents with coordination motives. On a discrete time line, communities of agents receive private information about an uncertain state, observe some predecessors' actions, and then take their own action. An agent's payoff is both state-dependent and increasing in the number of her peers taking the same action as hers. We find that connectivity between observations is the key determinant of the equilibrium pattern of information aggregation. When observations are connected, coordination motives inevitably lead to herding even when private beliefs are unbounded; when observations are separated, asymptotic learning becomes achievable. Herding can also be eliminated by making private information public within each community, and reduced by making observation endogenous and costly.

Nonlinear filtering of light-curve data

A particle filter with an expectation-maximization (EM) clustering algorithm to approximate the state’s distribution with Gaussian mixture models (GMMs) is proposed to simultaneously estimate the position, velocity, attitude, angular rates, and surface parameters of a space object (SO) in the near-Geostationary Earth Orbit (GEO). Recent work shows that the unscented Kalman filter applied to this problem is susceptible to divergence due to the presence of the many uncertain states estimated. The underlying reasons of the filter’s divergence have not yet been completely revealed and preliminary attributed to information dilution. Under the scenario considered in this paper, it is demonstrated through numerical simulation that the underlying reason for the filter’s divergence in SO tracking is due to the severe nonlinearities of light-curve measurement data coupled with weak observability; rather than information dilution. In addition to the novel particle filter, two alternative estimation techniques based on modifications of the extended Kalman filter (EKF) and unscented Kalman filter (UKF) are introduced to reduce the computational burden while still mitigating filter divergence.

BISTRO

The current trend toward urbanization and adoption of flexible and innovative mobility technologies will have complex and difficult-to-predict effects on urban transportation systems. Comprehensive methodological frameworks that account for the increasingly uncertain future state of the urban mobility landscape do not yet exist. Furthermore, few approaches have enabled the massive ingestion of urban data in planning tools capable of offering the flexibility of scenario-based design. This article introduces Berkeley Integrated System for Transportation Optimization (BISTRO), a new open source transportation planning decision support system that uses an agent-based simulation and optimization approach to anticipate and develop adaptive plans for possible technological disruptions and growth scenarios. The new framework was evaluated in the context of a machine learning competition hosted within Uber Technologies, Inc., in which over 400 engineers and data scientists participated. For the purposes of this competition, a benchmark model, based on the city of Sioux Falls, South Dakota, was adapted to the BISTRO framework. An important finding of this study was that in spite of rigorous analysis and testing done prior to the competition, the two top-scoring teams discovered an unbounded region of the search space, rendering the solutions largely uninterpretable for the purposes of decision-support. On the other hand, a follow-on study aimed to fix the objective function. It served to demonstrate BISTRO’s utility as a human-in-the-loop cyberphysical system: one that uses scenario-based optimization algorithms as a feedback mechanism to assist urban planners with iteratively refining objective function and constraints specification on intervention strategies. The portfolio of transportation intervention strategy alternatives eventually chosen achieves high-level regional planning goals developed through participatory stakeholder engagement practices.

A Healer's Uncertain State of Mind in COVID-19 Pandemic

<p>Current plight of COVID-19 in developing countries entails uncertain prognosis, impending severe shortages of resources for testing and treatment and inadequate safety measure for health care providers. The imposition of unfamiliar public health measures that may infringe on personal freedoms, large and growing financial losses, and conflicting messages from authorities are among the other major stressors that undoubtedly will contribute to widespread emotional distress and increased risk for mental fatigue associated with COVID-19.</p>

Impact of motion artefacts and motion-artefact correction on diagnostic accuracy of apical periodontitis in CBCT images: an ex vivo study in human cadavers.

To assess the impact of motion artefacts and motion-artefact correction on diagnostic accuracy of apical periodontitis (AP) in CBCT images. Based on clinical and radiographic inspection of 40 formalin-fixated human jaw specimens, 77 roots in 45 teeth (molars and premolars), with various disease and treatment state, were selected. The specimens were mounted on a robot simulating 3-mm movement types (nodding, lateral rotation and tremor). CBCT images with and without (controls) movements were acquired in four CBCT units: without motion-artefact correction in Cranex 3Dx, Orthophos SL 3D, and Promax 3D Mid, and with motion-artefact correction in Promax 3D Mid and X1. Three observers blindly assessed (i) whether the images were interpretable and (ii) if AP was present (5-step probability index). Histopathology provided the reference standard for presence of AP. Weighted Kappa statistics described inter-observer agreement. Estimates of diagnostic accuracy were assessed by means of receiver operator characteristic (ROC) curve analysis. Area under the curve (AUC) provided a measure of accuracy, and paired-sample AUC difference tests compared differences amongst the CBCT units and movement types. Observer agreement was substantial for control images, moderate for motion-artefact corrected images and fair for images without motion-artefact correction. When movement was present, motion-artefact correction reduced the percentage of images scored as noninterpretable or with uncertain disease state (score 3 in the 5-step probability index). Control images were not perfectly accurate (both false-positive and false-negative results were present; AUC 0.750-0.799). Images acquired with movement and without motion-artefact correction (AUC 0.541-0.709) were associated with significantly lower accuracy than control images (P<0.05). With motion-artefact correction, accuracy was comparable to that observed in control images (AUC 0.732-0.790). Diagnostic accuracy of apical periodontitis in CBCT images was dependent on the presence of motion artefacts (i.e. lower accuracy associated with the presence of movement). Motion-artefact correction systems positively influenced image interpretability and diagnostic accuracy.

Some issues of training specialists in the field of law enforcement activities

The article focuses on the professional requirements for persons who carry out law enforcement activities. The questions of higher education in the specialty «law enforcement activities» are considered. The functional responsibilities and personal qualities of investigators of pre-trial investigation bodies and employees of operational units are analyzed. Generalizations are made regarding the working conditions of law enforcement officers. Conclusions are drawn regarding the lack of significant differences in the training of investigators and operational unit employees. The need for a mutual understanding of the essence of the work of the investigator and the operative officer is expressed. The problematic issues are identified that relate to the uncertainty of employment of graduates of the specialty «law enforcement activities», the experience of the position of an investigator to legal professions, as well as the lack of using the calculation of the norms of time spent for investigators of pre-trial investigation bodies and employees of operational units. The article suggests two ways out of the uncertain state of graduates of the specialty «law en-forcement activities»: 1) the classification of the specialty «law enforcement activities» as a legal education; 2) amending the legislation regarding the requirements of higher education for investigators of pre-trial investigation bodies - to provide for the possibility of having a higher education in the specialty «law» and «law enforcement activities». Work experience as an investigator should be counted as work experience in the field of law, regardless of the specialty received.

Redundancy management strategy for electro-hydraulic actuators based on intelligent algorithms

This paper explores three methods for electro-hydraulic actuator redundancy management strategies based on fuzzy aggregation, Mamdani’s fuzzy logic rules, and fuzzy neural network theory. These intelligent management schemes and implementation strategies are studied for a designed hardware-independent redundancy loop. Fault identification and isolation and system reconstruction are performed by combining fuzzy clustering with Mamdani’s fuzzy control, a fuzzy neural network, and redundancy management. Simulation analyses of the three algorithms are performed and loop simulation models are established based on the analysis algorithm. The methods proposed in this paper can solve the problem of misjudgment encountered in traditional methods and can also avoid uncertain states in the system.

Along the Academe‐Policy Frontier: A Tribute to Aileen S.P. Baviera

Along the Academe‐Policy Frontier: A Tribute to Aileen S.P. Baviera

Evaluation of safety barriers deduced from the HAZOP study of fired heater F201-101, ADRAR refinery, Algeria using ETA method

This paper described the method of event tree analysis that is commonly used to identify, characterize and estimate the risk. Quantitative estimates for probability of breach or failure and the resulting consequences can be obtained using event trees. Qualitative depictions of potential failure modes and consequences can also be developed using event trees. An event tree analysis (ETA) is an inductive procedure that shows all possible outcomes resulting from an accidental (initiating) event. An event tree consists of a sequence of interconnected nodes and branches. Each node defines a random variable that represents an uncertain event or state of nature. Branches originating from a node represent each of branches to represent the likelihood for each event or condition. These probabilities are conditional on the occurrence of the preceding events to the left in the tree. Our work includes a HAZOP study to determine the necessary safety barriers as a first step. And as a second step is their evaluation using ETA method of fired heater F201-101, ADRAR refinery, Algeria. This particular method is well suited to estimating the risk stemming from process plant failure. Neither the initiators of industrial accidents nor the layers of protection that prevent them are typically complex, that is, they do not usually require analysis by redundant systems or the time for on-line repair. Thus, they can be accurately characterized with the probability multiplication methods that from the basis of event tree analysis.

Ellipsoidal reachable sets of linear time-varying continuous and discrete systems in control and estimation problems

The paper is devoted to reachable sets of linear time-varying continuous or discrete systems under uncertain initial states and disturbances with a bounded uncertainty measure. The uncertainty measure is the sum of a quadratic form of the initial state and the integral/sum over the finite-time interval from a quadratic form of the disturbance. It is shown that the reachable set of the system under this assumption is an evolving ellipsoid with a matrix being a solution to the linear matrix differential or difference equation. This result is used to synthesize optimal observers and estimators providing the minimal ellipsoidal sets as the estimates of the system state and unknown parameters, respectively, as well as optimal controllers steering the system state into a final target ellipsoidal set or keeping the entire system trajectory in a prescribed ellipsoidal tube under all admissible initial states and disturbances. The relationships between the optimal ellipsoidal observer and the Kalman filter as well as between the optimal ellipsoidal estimator and the recursive least weighted squares algorithm are established. Numerical modeling with the Mathieu equation for parametric vibrations of a linear oscillator illustrates the results.

Multi-machine preventive maintenance scheduling with imperfect interventions: A restless bandit approach

In this paper we address the problem of allocating the efforts of a collection of repairmen to a number of deteriorating machines in order to reduce operation costs and to mitigate the cost (and likelihood) of unexpected failures. Notwithstanding these preventive maintenance interventions are aimed at returning the machine to a so-called as-good-as-new state, unforeseeable factors may imply that maintenance interventions are not perfect and the machine is only returned to an earlier (uncertain) state of wear. The problem is modelled as a restless bandit problem and an index policy for the sequential allocation of maintenance tasks is proposed. A series of numerical experiments shows the strong performance of the proposed policy. Moreover, the methodology is of interest in the general context of dynamic resource allocation and restless bandit problems, as well as being useful in the particular imperfect maintenance model described.

Control strategy for networked control systems with time delay and packet dropout using linear matrix inequalities

A control strategy using linear matrix inequalities towards time delay and stochastic packet dropout of networked control system is proposed in this paper. First, a reconfigured target plant of networked control system is built based on a linear time-invariant target. Before discretization of target plant, a Pade approximation is employed towards the time delay segment of reconfigured target plant, while zero-order holder compensator and an estimator are employed for packet dropout of control input and measured output, respectively. Then the control target is reformed as a programming problem of an uncertain time-variant multi-parametric state space. In order to deal with such problem, herein the time-variant state space is rewritten as a linear parametric-varying prediction model by enumerating all possible occasions. Afterwards, a novel robust model predictive controller is proposed as a constrained minimax optimization of the predictive cost function. Finally, we present two simulations based on an aircraft model and a second-order numeric model, by which step responses and explicit solutions are derived, performed and analysed. Simulation results demonstrate both the stability and robustness of the proposed control strategy.

A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system

The Hamilton–Jacobi–Bellman (HJB) equation can be solved to obtain optimal closed-loop control policies for general nonlinear systems. As it is seldom possible to solve the HJB equation exactly for nonlinear systems, either analytically or numerically, methods to build approximate solutions through simulation based learning have been studied in various names like neurodynamic programming (NDP) and approximate dynamic programming (ADP). The aspect of learning connects these methods to reinforcement learning (RL), which also tries to learn optimal decision policies through trial-and-error based learning. This study develops a model-based RL method, which iteratively learns the solution to the HJB and its associated equations. We focus particularly on the control-affine system with a quadratic objective function and the finite horizon optimal control (FHOC) problem with time-varying reference trajectories. The HJB solutions for such systems involve time-varying value, costate, and policy functions subject to boundary conditions. To represent the time-varying HJB solution in high-dimensional state space in a general and efficient way, deep neural networks (DNNs) are employed. It is shown that the use of DNNs, compared to shallow neural networks (SNNs), can significantly improve the performance of a learned policy in the presence of uncertain initial state and state noise. Examples involving a batch chemical reactor and a one-dimensional diffusion-convection-reaction system are used to demonstrate this and other key aspects of the method.

Impact of model inconsistencies on reconstructions of past mantle flow obtained using the adjoint method

SUMMARYThe adjoint method is a powerful technique to compute sensitivities (Fréchet derivatives) with respect to model parameters, allowing one to solve inverse problems where analytical solutions are not available or the cost to determine many times the associated forward problem is prohibitive. In Geodynamics it has been applied to the restoration problem of mantle convection—that is, to reconstruct past mantle flow states with dynamic models by finding optimal flow histories relative to the current model state—so that poorly known mantle flow parameters can be tested against observations gleaned from the geological record. By enabling us to construct time dependent earth models the adjoint method has the potential to link observations from seismology, geology, mineral physics and palaeomagnetism in a dynamically consistent way, greatly enhancing our understanding of the solid Earth system. Synthetic experiments demonstrate for the ideal case of no model error and no data error that the adjoint method restores mantle flow over timescales on the order of a transit time (≈100 Myr). But in reality unavoidable limitations enter the inverse problem in the form of poorly known model parameters and uncertain state estimations, which may result in systematic errors of the reconstructed flow history. Here we use high-resolution, 3-D spherical mantle circulation models to perform a systematic study of synthetic adjoint inversions, where we insert on purpose a mismatch between the model used to generate synthetic data and the model used for carrying out the inversion. By considering a mismatch in rheology, final state and history of surface velocities we find that mismatched model parameters do not inhibit misfit reduction: the adjoint method still produces a flow history that fits the estimated final state. However, the recovered initial state can be a poor approximation of the true initial state, where reconstructed and true flow histories diverge exponentially back in time and where for the more divergent cases the reconstructed initial state includes physically implausible structures, especially in and near the thermal boundary layers. Consequently, a complete reduction of the cost function may not be desirable when the goal is a best fit to the initial condition. When the estimated final state is a noisy low-pass version of the true final state choosing an appropriate misfit function can reduce the generation of artefacts in the initial state. While none of the model mismatches considered in this study, taken singularly, results in a complete failure of the recovered flow history, additional work is needed to assess their combined effects.

Does Evidence-Based EU Law Survive the COVID-19 Pandemic? Considering the Status in EU Law of Lockdown Measures Which Affect Free Movement

When Member States restrict free movement on public health grounds they must show that their measures have a sound scientific basis. However, during the pandemic Member States have imposed a wide variety of restrictions, at the border, and internally. While Member State governments have invariably had local scientific advice, the variety of their measures suggests that their actions have also been driven, to some extent, by public opinion, contrary to what EU law generally allows. This situation could be seen as a defeat for EU law as traditionally conceived, and the triumph of local preferences over scientific standards. Perhaps we learn that in a crisis, local desires for symbolic security and closure trump both law and science. Alternatively, it can be argued that the Court of Justice's emphasis on exclusively objective justifications for measures is unrealistic and over-strict. The pandemic responses show that (i) science is often neither clear nor determinative, and (ii) policy is invariably a mix of science and values, even in apparently technical fields. In either case, the absence of legal challenges to Member State actions leaves free movement in an uncertain state. Have we entered a new phase, where national fears are a more legitimate justification for restricting movement, or will the pandemic be treated as so exceptional as to be beyond law, and thus not a precedent?

Критичні чинники та перспективи відновлення світової економіки в умовах глобальної рецесії COVID-19

The article focuses on identifying and systematizing the hallmarks of the COVID-19 recession as unprecedented in terms of the both spread and impact on the global economy in the phase of business activity downturn. The dynamics of the main indicators of economic growth are researched and it is substantiated that the negative linear forecast trends allow forecasting the long-term recovery period after the current global recession. It is shown that the global recession of 2020 negatively affects the prospects for the recovery of major national economies of the world, given the high probability of the introduction of repeated quarantine restrictions. Based on the analysis of the dynamics of the index of global economic uncertainty, the world economy is confirmed to be in the most uncertain state during the period of observations. According to the analysis of the values of the barometer of world trade in goods in 2020, negative changes in the structure and dynamics of foreign trade turnover and doubt for the rapid recovery of global supply chains were evidenced. Based on the generalization of the results of the study, key distinctive signs of the COVID-19 economic recession have been systematized justifying that, in addition to any recession inherent in the loss of growth rate of world production, the current economic crisis is accompanied by a drop reaching the critical deadlines of a number of indicators of the intensity of global development, which confirms its unprecedented spread and impact on the world economy. It is substantiated that the confirmation of the determined trends allows to predict the long-term negative consequences of the modern global recession, which leads to the need to substantiate the directions of recovery of the world economy and foreign trade cooperation of the countries under crisis conditions.

Multi-Agent Infinite Horizon Persistent Monitoring of Targets with Uncertain States in Multi-Dimensional Environments

Abstract This paper investigates the problem of persistent monitoring, where a finite set of mobile agents persistently visits a finite set of targets in a multi-dimensional environment. The agents must estimate the targets’ internal states and the goal is to minimize the mean squared estimation error over time. The internal states of the targets evolve with linear stochastic dynamics and thus the optimal estimator is a Kalman-Bucy Filter. We constrain the trajectories of the agents to be periodic and represented by a truncated Fourier series. Taking advantage of the periodic nature of this solution, we define the infinite horizon version of the problem and explore the property that the mean estimation squared error converges to a limit cycle. We present a technique to compute online the gradient of the steady state mean estimation error of the targets’ states with respect to the parameters defining the trajectories and use a gradient descent scheme to obtain locally optimal movement schedules. This scheme allows us to address the infinite horizon problem with only a small number of parameters to be optimized.

Improvement of Rule Generation Methods for Fuzzy Controller

This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to improvement the above challenge. A zero order Sugeno fuzzy model used as fuzzy system structure. At first by using clustering obtains the number of rules and input membership functions. A set of candidate amounts for consequence parts of fuzzy rules is considered. Considering each pair of training data, according which rules fires and what is the output in the pair, the amount of probability of consequences candidates are change. In the next step, eligibility probability of each consequence candidate for all rules is determined. Finally, using these obtained probability, two probable outputs is generate for each input. The experimental results show superiority of the proposed approach rather than some available well-known approaches that makes reduce the number of rule and reduce system complexity.