Stochastic Conditions Research Articles

Control-Aware Transmission Scheduling for Industrial Network Systems Over a Shared Communication Medium

In this article, we consider the design of dynamic transmission scheduling policies for the industrial network systems sharing scarce communication resources. Only a few subsystems can obtain channel access for information updates to close their control loops at each time step, which highlights the necessity of designing optimal transmission scheduling schemes to achieve a minimum average linear quadratic cost of the industrial network systems. We first propose a greedy state-error-dependent scheduling (SES) policy based on the one-step expected profit and discuss its stability employing the Lyapunov function method. After formulating the scheduling optimization as a Markov decision process problem and relaxing with a soft constraint, we develop a heuristic near-optimal solution that guarantees the optimality of certainty equivalent controllers, namely, Whittle’s index-inspired error-dependent scheduling (WIES). A stochastic stability condition of WIES is further given based on f-ergodicity. Due to low computational complexity and ease of implementation, the proposed schemes are suitable for large-scale heterogeneous industrial network systems. Finally, simulation results show that the proposed policies outperform the existing round-robin, holding-time-prioritized, and error-aware scheduling schemes.

Source Term Estimation Using Deep Reinforcement Learning With Gaussian Mixture Model Feature Extraction for Mobile Sensors

This paper proposes a deep reinforcement learning method for mobile sensors to estimate the properties of the source of the hazardous gas release. The problem of estimating the properties of the released gas is generally termed as the source term estimation (STE) problem. Since the sensor measurements from atmospheric gas dispersion are sparse, intermittent, and time-varying due to the turbulence and the sensor noise, STE is considered to be a challenging problem. The particle filter is adopted to estimate the source term under such stochastic noise conditions. The deep deterministic policy gradient (DDPG) is also employed to find the best source search policy in terms of successful estimation and traveled distance. Through ablation studies, we demonstrate that the use of the Gaussian mixture model, which clusters the potential source positions from the particle filter, as an input to the DDPG and the gated recurrent unit functioning as a memory in DDPG help to improve the STE performance. Besides, simulation results in randomized source term conditions and previously-unseen environments show the superior STE performance of the proposed algorithm compared with the existing information-theoretic STE algorithm.

Cash management and risk-taking incentives with performance-sensitive debt under stochastic financing conditions

We build a model of cash management for financially constrained firm with performance-sensitivity debt (PSD) in a stochastic financing conditions framework. In this model, stochastic financing conditions lead shareholders to have incentives for risk-taking. This paper highlights the implications of PSD on cash management and risk-taking incentives for shareholders. We find that firms with PSD issue equity earlier, and delay payout to shareholders relative to firms with straight debt. In addition, we also discover that larger performance-sensitivity leads shareholders to have stronger risk-taking incentives.

An evolutive probability transformation method for the dynamic stochastic analysis of structures

The problem of the stochastic response determination of dynamic systems, once the random properties of the actions are assigned, is studied. In the present work, an extension of the Probability Transformation Method (PTM) to the dynamic systems has been proposed. In particular, an evolutive PTM (EPTM) is introduced. Based on the properties of the mean square random calculus with the principle of conservation of probability, the EPTM applies the PTM contextually to the mean square Riemann integral of the system solution process. In this way, the EPTM gives the expression of the single-time evolutive characteristic function (CF) of the system’s output. The application to a linear structural systems subject to assigned stochastic process excitations and random initial conditions has revealed the efficiency of the stochastic procedure.

Green energy extraction for sustainable development: A novel MPPT technique for hybrid PV-TEG system

The Photovoltaic (PV) module converts only a small portion of irradiance into electrical energy. Most of the solar energy is wasted as heat, resulting in a rise in PV cell temperature and a decrease in solar cell efficiency. One way to harvest this freely available solar thermal energy and improve PV cell efficiency is by integrating PV systems with thermoelectric generators (TEG). This cogeneration approach of the hybrid PV-TEG system uses waste heat as an energy production source, resulting in higher output power density. Furthermore, as hybrid PV-TEG systems are frequently used in dynamic environments with temperature and irradiance variations, it is critical to modify the electrical operating points effectively and precisely to maximize the collected power, a process known as maximum power point tracking (MPPT). Under stochastic environmental conditions, a properly chosen MPPT technique can improve the generation efficiency from 10 to 15%. Therefore, in this study a novel implementation of atomic orbital search optimization algorithm is presented for MPPT tracking. Several case studies are designed to test AOS based MPPT algorithm performance under stochastic operating conditions. The effectiveness of the proposed algorithm is validated by comparing it with the conventional Perturb and Observe (P&O) algorithm and other highly-efficient metaheuristic algorithms such as Grasshopper Optimization (GHO), Particle swarm optimization (PSO), and Grey Wolf Optimization (GWO). AOS based controller extracts 6% more energy and tracks optimal power at an efficiency of 99.984% under various test scenarios. The tracking time is improved by 616.48% as compared to PSO. Experimental, quantitative, comparative, and statistical results demonstrate the AOS-based proposed technique's superior performance under various practical conditions.

Composite event-triggered disturbance-observer-based control and passive control for semi-Markovian jump nonlinear systems with actuator saturation

In this paper, the composite control problem of continuous time semi-Markovian jump system with actuator saturation, multiple disturbances and unknown nonlinearity is investigated. A stochastic stability condition and a lower bound of the time between two consecutive events are derived to avoid Zeno behaviour, based on the combination of disturbance-observer-based control and event-triggered control. In addition, the passive control of the system based on the disturbance observer is studied. Finally, numerical simulation proves that the designed composite controller can effectively save communication resources, and can maintain the system to meet the passive performance indicators of the obtained closed-loop system under the influence of multiple disturbances and actuator saturation.

An Experimental Analysis of Hierarchical Rail Traffic and Train Control in a Stochastic Environment

The hierarchical connection of Rail Traffic Management System (TMS) and Automatic Train Operation (ATO) for mainline railways has been proposed for a while; however, few have investigated this hierarchical connection with the real field. This paper studies in detail the benefits and limitations of an integrated framework of TMS and ATO in stochastic and dynamic conditions in terms of punctuality, energy efficiency, and conflict-resolving. A simulation is built by interfacing a rescheduling tool and a stand-alone ATO tool with the realistic traffic simulation environment OpenTrack. The investigation refers to different disturbed traffic scenarios obtained by sampling train entrance delays and dwell times within a typical Monte Carlo scheme. Results obtained for the Dutch railway corridor Utrecht–Den Bosch prove the value of the approach. In case of no disruptions, the implementation of ATO systems is beneficial for maintaining timetables and saving energy costs. In case of delay disruptions, the TMS rescheduling has its full effect only if trains are able to follow TMS rescheduled timetables, while the energy-saving by using ATO can only be achieved with conflict-free schedules. A bidirectional communication between ATO and TMS is therefore beneficial for conflict-resolving and energy-saving.

Stochastic boundary condition effects on supersonic leading edge blowing

The present manuscript studies the robustness of leading edge blowing for active control of the upstream bow shock and overall aerothermal characteristics of a supersonic airfoil subject to variability in the injection conditions. The injection boundary conditions were considered stochastic and their randomness was propagated to the flow topology and aerothermal quantities of interest by means of non-intrusive Polynomial Chaos Expansions. The evaluations needed to build these expansions were carried out via steady two-dimensional Reynolds-Averaged Navier Stokes simulations. Two different injection port sizes were considered, with the smallest promoting a Coanda effect that gives rise to non-symmetric flow topologies. The uncertain boundary conditions lead to significant variations in the flow topology, particularly at the injection exit and the bow shock position, with variations of up to 75% of the nominal value. This study identifies the key operating conditions to ensure the optimal application of leading-edge injection for active control of supersonic airfoils.

Multiagent Reinforcement Learning for Task Offloading of Space/Aerial-Assisted Edge Computing

The task offloading in space-aerial-ground integrated network (SAGIN) has been envisioned as a challenging issue. In this paper, we investigate a space/aerial-assisted edge computing network architecture considering whether to take advantage of edge server mounted on the unmanned aerial vehicle and satellite for task offloading or not. By optimizing the energy consumption and completion delay, we formulate a NP-hard and non-convex optimization problem to minimize the computation cost, limited by the computation capacity and energy availability constraints. By formulating the problem as a Markov decision process (MDP), we propose a multiagent deep reinforcement learning (MADRL)-based scheme to obtain the optimal task offloading policies considering dynamic computation request and stochastic time-varying channel conditions, while ensuring the quality-of-service requirements. Finally, simulation results demonstrate the task offloading scheme learned from our proposed algorithm that can substantially reduce the average cost as compared to the other three single agent deep reinforcement learning schemes.

Cost-Efficient Beam Management and Resource Allocation in Millimeter Wave Backhaul HetNets With Hybrid Energy Supply

Energy consumption accounts for a significant portion of OPEX in 5G networks, which will be further increased when millimeter-wave (mmWave) base stations are deployed. To reduce the cost, renewable energy has been introduced to provide complementary power supply. Nevertheless, how to cost-efficiently utilize radio resources and renewable energy remains a challenge. In this paper, we jointly investigate the beamwidth management and resource allocation in mmWave backhaul HetNets with hybrid energy supply aiming to maximize long-term cost efficiency. This requires solving a mixed stochastic combinatorial optimization problem, characterizing the causal property of renewable energy harvesting process, stochastic channel conditions, and imperfect antenna alignment. Considering the complexity and dynamic environment, a learning system is established instead of using traditional optimization methods which typically experience exponential worst-case complexity and require complete information. Specifically, we propose a Learning-based Cost-efficient Resource Allocation (LCRA) algorithm that employs deep neural network to learn policies from experiences to ensure system performance while achieving cost-efficiency. To enhance the sampling efficiency and stability of the conventional deep reinforcement learning methods for our problem, an improved proximal policy optimization method is proposed to reuse the history samples. Specifically, a modified clip function is designed to deal with the hard restrictions between current and old policies. Furthermore, random network distillation is introduced to enhance the exploration capability. Numerical results reveal the convergence performance of the LCRA and the superiority in improving the cost efficiency in a hybrid energy powered mmWave backhaul HetNet compared with state-of-the-arts

Assessment of Current Downtime Analysis

Abstract: The approach used to calculate downtime for complicated projects is consistent with the literature: simulation. Researchers evaluate current downtime study on Shipping Corporation in this report. As a result, the existing approach for determining downtime for complex projects is untrustworthy; in fact, due to the significant simulation uncertainty, it is highly unsafe to utilise current methodology to make predictions on downtime distribution. We advocate utilising a stochastic metocean condition generator in the simulation to reduce simulation uncertainty. These uncertainties, however, may be managed by the model used, & they can also be quantified. Finally, a comparison1should be drawn between simulation uncertainty in HADDOCK & combined model, parametric, & simulation uncertainty in a stochastic model simulation. Keywords: Marine operation, downtime analysis, Assessment advantages and limitations

Development of intelligent and expert system for automation of processes of mining and transport works on the basis of satellite navigation

The object of research relates to the field of control systems for mining and transport machines in the development of deposits of solid minerals in an open way. The problem of reducing the cost of transporting 1 ton of rock mass and increasing the efficiency of these machines is being solved. The article develops an expert system for dispatching mining vehicles with a subsystem for selecting their innovative appearance with the ability to control their operational parameters, taking into account the stochastic conditions of the developed sections of the rock mass. The mathematical model for constructing the prospective appearance of a mining and transport machine, based on its functional and economic assessment, is reduced to solving the problem of optimizing the generalized criterion of the required efficiency. As an example of private indicators of their effectiveness in operation, there is an expert analysis of the evaluation of solution options, for example, structural-kinematic and operational parameters of these machines, etc. Innovative designs of a skip body of any size of its carrying capacity of single-rope and multi-rope steeply inclined skip hoists for highly profitable mining in quarries are substantiated. unlimited values of their depth and annual productivity. In the proposed study, the values of the forces of resistance to the destruction of a section of a rock mass, obtained by analytical and experimental methods, are refined by finding the optimal Kalman coefficient, which increases the efficiency of using mining and transport machines. The proposed methods provide the creation of innovative mining and transport machines with the ability to control their operational parameters, taking into account the stochastic conditions of the developed section of the rock mass

Life history strategies of stream fishes linked to predictors of hydrologic stability.

Life history theory provides a framework to understand environmental change based on species strategies for survival and reproduction under stable, cyclical, or stochastic environmental conditions. We evaluated environmental predictors of fish life history strategies in 20 streams intersecting a national park within the Potomac River basin in eastern North America. We sampled stream sites during 2018–2019 and collected 3801 individuals representing 51 species within 10 taxonomic families. We quantified life history strategies for species from their coordinates in an ordination space defined by trade‐offs in spawning season duration, fecundity, and parental care characteristic of opportunistic, periodic, and equilibrium strategies. Our analysis revealed important environmental predictors: Abundance of opportunistic strategists increased with low‐permeability soils that produce flashy runoff dynamics and decreased with karst terrain (carbonate bedrock) where groundwater inputs stabilize stream flow and temperature. Conversely, abundance of equilibrium strategists increased in karst terrain indicating a response to more stable environmental conditions. Our study indicated that fish community responses to groundwater and runoff processes may be explained by species traits for survival and reproduction. Our findings also suggest the utility of life history theory for understanding ecological responses to destabilized environmental conditions under global climate change.

Integrated Dynamics Response Analysis for IEA 10-MW Spar Floating Offshore Wind Turbine

Wind energy in the deep-sea area is more abundant and the capacity of wind turbines can be made larger. Therefore, the research on deep-sea floating offshore wind turbines will be the primary strategy for wind energy exploitation in the future. The spar-type platform depends on the characteristics of a small water plane, deep draft, and good stability, which has been applied to the commercial development of deep-sea wind energy. In the next ten years, the 10-MW wind turbine will become the mainstream class installed in the floating offshore wind turbine farm. Thus, it is very necessary to conduct a comprehensive and in-depth study on the 10-MW spar type floating offshore wind turbine. The direct-drive 10-MW offshore wind turbine was proposed by the International Energy Agency (IEA) in Wind Task 37 in 2019. In this paper, a spar-type platform is designed to support the IEA 10-MW reference wind turbines, and a nonlinear aero-hydro-servo-elastic numerical model is established using the FAST tool (which is developed by the National Renewable Energy Laboratory, NREL). Then, the accuracy of the wind turbine and the sensitivity of the controller are verified, and the natural periods of the floating offshore wind turbine are obtained by free-decay tests. The natural periods of the platform in six degrees-of-freedom are found to be within the range recommended by the design standard. The measured wind and wave data of the target site close to Fujian Province of China are used to evaluate the performance of the floating offshore wind turbine under the 100-, 50-, 5-, and 2-year-return stochastic weather conditions. The results indicate that the design of the spar platform is reasonable and has excellent hydrodynamic performance.

On Generalized Reflected BSDEs with Rcll Obstacle

Abstract This paper proves the existence and uniqueness theorem for generalized (reflected) backward stochastic differential equations under stochastic Lipschitz and monotone condition. The result is shown by using Picard’s iteration, the Snell envelope theory and the penalization method.

A periodic averaging method for impulsive stochastic age‐structured population model in a polluted environment

In this paper, we present a periodic averaging (PA) method for impulsive stochastic age‐structured population model (ISASPM) in a polluted environment. By using Young's inequality and Gronwall's lemma and under the stochastic Lipschitz condition, we reveal that both the standard ISASPM and the averaged ISASPM have a unique solution. We also study the mean‐square convergence criteria of the numerical solutions produced by the PA method. Finally, asimulation example is given to demonstrate that the PA method is efficient for our results.

Online computation offloading and trajectory scheduling for UAV-enabled wireless powered mobile edge computing

The unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) architecture is expected to be a powerful technique to facilitate 5G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer (WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things (IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energy-efficiency (EE) minimization problem is formulated. Due to non-convexity and the time domain coupling of the variables in the formulated problem, a low-complexity online computation offloading and trajectory scheduling algorithm (OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

Stochastic moisture-heat coupled processes of an embankment in permafrost regions

Moisture-heat processes are important for the service performance of embankments in permafrost regions. The external boundary conditions and soil-water parameters involved in the moisture-heat processes are spatially and temporally uncertain due to complex and variable geological processes, stochastic climate conditions, human activities, and construction disturbances. These uncertainties were often averaged over many measured data and ignored in existing engineering design. The exact mechanism of stochastic moisture-heat processes with uncertain factors effects thus remains unclear. Therefore, a numerical stochastic moisture-heat coupled model was established to explore the uncertain moisture-heat processes for permafrost embankments. The simulation results show that the stochastic moisture-heat processes of the embankment exhibit seasonal differences. The randomness of temperature on September 15 was found to be the highest during a one-year period, while the unfrozen water content and ice content both show the highest randomness on January 15. We also found that the standard deviations (SDs) of moisture-heat processes showed the largest fluctuation at the middle and bottom of the embankment, which implies the deviations of determined analyses. The findings of this study would serve as a reference to parameters selection for simulation and design, mechanism analyses of freezing-thawing damage and engineering reliability in cold regions.

A dual-process approach to cooperative decision-making under uncertainty.

Cooperative behaviors are typically investigated using social dilemmas inserted into scenarios with well-known characteristics. Nonetheless, in real life, group members may be uncertain about what others will decide (social uncertainty) and the characteristics of the dilemma itself (environmental uncertainty). Previous studies have shown that uncertainty reduces the willingness to cooperate. Dual-process approaches to cooperation have given rise to two different views. Some authors argue that deliberation is needed to overrule selfish motives, whereas others argue that intuition favors cooperation. In this work, our goal was to investigate the role of intuitive mental processing on cooperation in a prisoner’s dilemma game involving uncertainty. Our results showed that participants cooperated less with their counterparts as the number of rounds progressed, suggesting a learning process and that intuitive mental processing in the first 50 rounds appears to favor cooperation under both deterministic and stochastic conditions. These results may help clarify the literature’s mixed effects regarding cognitive processing manipulation on cooperation. Developing a better understanding of these effects may improve strategies in social problems involving cooperation under uncertainty and cognitive constraints.

Resilience assessment of a subsea pipeline using dynamic Bayesian network

Microbiologically influenced corrosion (MIC) is a serious concern and plays a significant role in the marine and subsea industry’s infrastructure failure. A probabilistic methodology is introduced in the present study to assess the subsea system’s resilience under MIC. Conventionally, the risk-based models are constructed using the system’s characteristic features. This helps decision-makers understand how a system operates and how the failed system can be recovered. The subsea system needs to be designed with sufficient resilience to maintain the performance under the time-varying interdependent stochastic conditions. This paper presents the dynamic Bayesian network-based approach to model the subsea system’s resilience as a function of time. An industry-based application study of the subsea pipeline is studied to demonstrate the efficiency and effectiveness of the proposed methodology for the resilience assessment. The proposed methodology will assist decision-makers in considering the resilience in the system design and operation.