Stochastic Behavior Research Articles

Protocol-based control for semi-Markov reaction-diffusion neural networks

This paper addresses the asynchronous control problem for semi-Markov reaction–diffusion neural networks (SMRDNNs) under probabilistic event-triggered protocol (PETP) scheduling. A semi-Markov process with a deterministic switching rule is introduced to characterize the stochastic behavior of these networks, effectively mitigating the impacts of arbitrary switching. Leveraging statistical data on communication-induced delays, a novel PETP is proposed that adjusts transmission frequencies through a probabilistic delay division method. The dynamic adjustment of event trigger conditions based on real-time neural network is realized, and the responsiveness of the system is enhanced, which is of great significance for improving the performance and reliability of the communication system. Additionally, a dynamic asynchronous model is introduced that more accurately captures the variations between system modes and controller modes in the network environment. Ultimately, the efficacy and superiority of the developed strategies are validated through a simulation example.

Learning minimal representations of stochastic processes with variational autoencoders.

Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertainty, they are, however, difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic process. Our method builds upon an extended β-variational autoencoder architecture. By means of simulated data sets corresponding to paradigmatic diffusion models, we showcase its effectiveness in extracting the minimal relevant parameters that accurately describe these dynamics. Furthermore, the method enables the generation of new trajectories that faithfully replicate the expected stochastic behavior. Overall, our approach enables the autonomous discovery of unknown parameters describing stochastic processes, hence enhancing our comprehension of complex phenomena across various fields.

DiffPLF: A conditional diffusion model for probabilistic forecasting of EV charging load

Due to the vast electric vehicle (EV) penetration to distribution grid, charging load forecasting is essential to promote charging station operation and demand-side management. However, the stochastic charging behaviors and associated exogenous factors render future charging load patterns quite volatile and hard to predict. Accordingly, we devise a novel Diffusion model termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can explicitly approximate the predictive load distribution conditioned on historical data and related covariates. Specifically, we leverage a denoising diffusion model, which can progressively convert the Gaussian prior to real time-series data by learning a reversal of the diffusion process. Besides, we couple such diffusion model with a cross-attention-based conditioning mechanism to execute conditional generation for possible charging demand profiles. We also propose a task-informed fine-tuning technique to better adapt DiffPLF to the probabilistic time-series forecasting task and acquire more accurate and reliable predicted intervals. Finally, we conduct multiple experiments to validate the superiority of DiffPLF to predict complex temporal patterns of erratic charging load and carry out controllable generation based on certain covariate. Results demonstrate that we can attain a notable rise of 39.58% and 49.87% on MAE and CRPS respectively compared to the conventional method.

Improved two-stage energy community optimization model considering stochastic behaviour of input data

Improved two-stage energy community optimization model considering stochastic behaviour of input data

Similarity analysis of randomness in instantaneous chloride diffusion coefficient of concrete under different environmental exposures

This study addresses the temporal variability and randomness of concrete's chloride diffusion coefficient by proposing an analysis using stochastic measures for the instantaneous chloride diffusion coefficient (Dins) alongside Kullback-Leibler (K-L) divergence and Hamming distance for similarity assessment. Examination of diverse concrete specimens subjected to both natural tidal environments (NE) and simulated environments (SE) indicated that the environmental factors, primarily exposure setting and duration, substantially influence the diffusion coefficient's temporal behavior. SE with elevated temperature and salinity accelerated chloride penetration, leading to higher diffusion rate means than those in NE. Concrete in NE exhibited a log-normal distribution for Dins, while under SE, a normal distribution was observed. Variability was mainly driven by the exposure environment, with minor contributions from material components. Similarity analysis suggested that the stochastic character of Dins in SE after 120 days resembled those in NE at 600 days, as per K-L divergence, whereas Hamming distance showed the closest resemblance at 240 days in SE. The Material make-up showed negligible impact on distribution similarity, and the extending simulation exposure time had a limited effect on enhancing similarity of stochastic diffusion behavior across environments.

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge–Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson’s correlation index (R) of 0.9374, and a Nash–Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

Stochastic dynamical behaviors of SOS/ERK signaling pathway perturbated by external noise

The SOS/ERK cascades are key signaling pathways that regulate cellular processes ranging from cellular proliferation, differentiation and apoptosis to tumor formation. However, the properties of these signaling pathways are not well understood. More importantly, how stochastic perturbations of internal and external cellular environment affect these pathways remains unanswered. To answer these questions, we, in this paper, propose a stochastic model according to the biochemical reaction processes of the SOS/ERK pathways, and, respectively, research the dynamical behaviors of this model under the four kinds of noises: Gaussian noise, colored noise, Lévy noise and fraction Brown noise. Some important results are found that Gaussian and colored noises have less effect on the stability of the system when the strength of the noise is small; Lévy and fractional Brownian noises significantly change the trajectories of the system. Power spectrum analysis shows that Lévy noise induces a system with quasi-periodic trajectories. Our results not only provide an understanding of the SOS/ERK pathway, but also show generalized rules for stochastic dynamical systems.

Laboratory Investigations of the Leica RTC360 Laser Scanner-Distance Measuring Performance.

A Leica RTC360 laser scanner was investigated using a linear horizontal comparator system with four targets of different reflectance. Several thousand panorama scans were conducted along the 30 m long comparator, basically in 40 mm steps. For a selected target, more detailed investigations were carried out with a 2 mm step width for a 2 m wide section. The absolute offset between the scanner and the relative interferometer measurements was determined with a calibrated total station. The investigations revealed several systematic effects like an offset in the distance measurement of about 1.3 mm. Furthermore, sections with stochastic behavior as well as sections with pseudo-cyclic parts were observed, depending on the reflectance of the target. The deterministic sections showed curved and striped patterns with some discontinuities of about 2 mm at 20 m, resulting in a saw-tooth like pattern along the distances. Within all the experiments, the distance deviations were below the manufacturer specifications of the 3D point accuracy. However, it was demonstrated that the distance measurements had clear systematic components. In using these new findings, the specification of the measurement "noise" in the data sheet has to be seen as critical.

Protocol-based state estimation of two-time-scale complex networks with nonhomogeneous sojourn probabilities

This paper is dedicated to researching the problem of the state estimation for two-time-scale complex networks (TTSCNs) subject to sojourn probabilities (SPs) under channel fadings and false data injection attacks (FDIAs). The network’s switching topology is governed by a novel switching law that incorporates time-varying SPs, with the variation of SPs being regulated through a persistent dwell-time (PDT) switching signal. Meanwhile, for the sake of achieving energy savings and mitigating data congestion, the dynamic probabilistic event-triggered weighted try-once-discard (DPEWTOD) policy is innovatively developed by integrating with the major merits of the dynamic probabilistic event-triggered mechanism (DPETM) and WTOD protocol (WTODP) based on hybrid compensation. Different from the traditional communication rules, this DPEWTOD concept not only captures stochastic behavior of the dynamic triggering threshold (DTT), but also enables a compensatory measurement that closely approximates the actual value. Subsequently, we construct time delay-, DTT-, and PDT-dependent Lyapunov functionals to analyze the stability of error dynamic systems in the presence of fading and malicious FDIAs. By employing a separation technique, our proposed solving algorithm transforms non-convex analysis conditions into tractable ones. Eventually, the rationality and effectiveness of the proposed method are demonstrated by an resistance–capacitance (RC) circuit example.

A Bayesian optimization approach for data‐driven mixed‐integer nonlinear programming problems

AbstractThe optimization of process systems within the field of Chemical Engineering often confronts uncertainties that can exert significant influences on the performance and dependability of the obtained solutions. This research endeavors to investigate the application of Bayesian optimization in the realm of constrained mixed integer nonlinear problems. A comparative analysis was conducted, exploring different surrogate models, and evaluating diverse kernel functions and acquisition functions. Furthermore, a sampling strategy was devised to assess the enhancement achieved by the acquisition function. The findings of this article reveal the superior performance of sparse Gaussian processes in conjunction with computationally inexpensive acquisition functions, thereby highlighting their suitability for addressing mixed integer nonlinear programming problems characterized by noisy functions and stochastic behavior. Consequently, this article presents a computationally efficient approach to effectively tackle the challenges associated with data‐driven mixed integer nonlinear programming problems within the domain of Process System Engineering.

ENERGY MANAGEMENT SYSTEM OF MICROGRID WITH RENEWABLE ENERGY SOURCES

The most recent development in the electrical system is the microgrid. Many scattered, networked energy storage, loads, and generation units make up a typical microgrid system. An energy system made up entirely of renewable resources is called a microgrid (MG), an energy storage device, and loads that can operate in grid-connected or islanded mode. The demand for the load and the power flow within MG should be managed by scheduling renewable resources. This paper presents a hybrid microgrid energy management system (MEMS) that combines grid-connected solar (PV), wind power from microturbines, and battery energy storage systems (BESS). Due to their increased energy efficiency, microgrid units are becoming more and more well-liked every day. Energy management for microgrids is required since renewable energy sources provide an imbalance in the Power System due to their stochastic behavior. Controlling the DC/DC bidirectional converter (DBC), which links the Ni-H battery to the DC bus of the grid-connected microgrid, is the primary goal of this study. The start-up of a wind/photovoltaic power generation system to the load is covered in this study. MATLAB/SIMULINK was used to model a microgrid system that is connected to the grid. Key Words: Microgrid, Photovoltaic (PV)

Multiple stochastic and inverse stochastic resonances with transition phenomena in complex corporate financial systems.

This study examines the role of periodic information, the mechanism of influence, stochastic resonance, and its controllable analysis in complex corporate financial systems. A stochastic predator-prey complex corporate financial system model driven by periodic information is proposed. Additionally, we introduce signal power amplification to quantify the stochastic resonance phenomenon and develop a method for analyzing stochastic resonance in financial predator-prey dynamics within complex corporate financial systems. We optimize a simplified integral calculation method to enhance the proposed model's performance, which demonstrates superiority over benchmark models based on empirical evidence. Based on stochastic simulations and numerical calculations, we can observe multiple stochastic and multiple inverse stochastic resonances. Furthermore, variations in initial financial information, periodic information frequency, and corporate growth capacity induced stochastic resonance and inverse stochastic resonance. These variations also led to state transitions between the two resonance behaviors, indicating transition phenomena. These findings suggest the potential for regulating and controlling stochastic and inverse stochastic resonance in complex corporate finance, enabling controllable stochastic resonance behaviors.

Performance Analysis of Multi-Tote Storage and Retrieval Autonomous Mobile Robot Systems

Multi-tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retrieve them from different shelf rack tiers, and transport them to a workstation where the products are picked to fulfill customer orders. In each robot trip, totes retrieved during the previous trip must be stored. This leads to a mixed storage and retrieval route. We analyze this mixed storage and retrieval route problem and derive the optimal travel route for a multiblock warehouse by a layered graph algorithm, based on storage first-retrieval second and mixed storage and retrieval policies. We also propose an effective heuristic routing policy, the closest retrieval (CR) sequence policy, based on a local shortest path. Numerical results show that the CR policy leads to shorter travel times than the well-known S-shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on the CR policy, we model the stochastic behavior of the system using a semiopen queuing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to optimize the total annual cost as a function of the warehouse shape, the number of robots, and tote buffer positions on the robots for a given average tote throughput time and throughput capacity. Compared with robots that retrieve a single tote per trip, an MTSR system with at least five buffer positions can achieve lower operational costs while meeting given average tote throughput time and tote throughput capacity constraints. Funding: This work was supported by National Natural Science Foundation of China [Grant 72372088] and the Shenzhen Science and Technology Program [Grant GJHZ20220913143003006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0397 .

Magnetic field-free stochastic computing based on the voltage-controlled magnetic tunnel junction

The stochastic computing (SC) has been proven to be an energy-efficient way to perform neural network. In this study, we propose a field-free voltage-controlled spintronics SC system based on the magnetic tunnel junction (MTJ). We observe a stochastic switching behavior of the MTJ and that it could be controlled by the voltage applied on the device. The voltage-controlled stochastic switching behavior is used to encode numbers ranging from 0 to 1 into a series of random bit-streams in the SC system. Furthermore, the handwritten recognition task is performed on the MTJ-based SC system, achieving a 95% maximum accuracy, which is comparable with the floating-point based neural network. Our work provides inspiration for the energy-efficient neural network systems.

Ethical Considerations in AI Simulations for Designing Assistive Technologies

Current ethical debates on the use of artificial intelligence (AI) in healthcare approach AI technology in three primary ways. First, they assess the risks and potential benefits of current AI-enabled products using ethical checklists. Second, they propose ex ante lists of ethical values relevant to the design and development of assistive technologies. Third, they advocate for incorporating moral reasoning into AI's automation processes. These three perspectives dominate the discourse, as evidenced by a brief literature summary. We propose a fourth approach: viewing AI as a methodological tool to aid ethical reflection. This involves an AI simulation concept informed by three elements: 1) stochastic human behavior models based on behavioral data for simulating realistic scenarios, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components to illustrate the impact of variable changes. This approach aims to inform an interdisciplinary field about anticipated ethical challenges or trade-offs in specific settings, prompting a re-evaluation of design and implementation plans. This is particularly useful for applications involving complex values and behaviors or limited communication resources, such as dementia care or care for individuals with cognitive impairments. While simulation does not replace ethical reflection, it allows for detailed, context-sensitive analysis during the design process and before implementation.Finally, we discuss the quantitative analysis methods enabled by stochastic simulations and the potential for these simulations to enhance traditional thought experiments and future-oriented technology assessments.

Simulation Study on Student Residential Energy Use Behaviors: A Case Study of University Dormitories in Sichuan, China

Studying the energy use behavior of occupants is crucial for accurately predicting building energy consumption. However, few studies have considered the impact of occupant behaviors on energy consumption in university dormitories. The objective of this study is to establish an agent-based model of energy consumption for university dormitories based on energy use behavior. The dormitories of a typical university in Sichuan, China, were subdivided into three clusters using a two-step cluster analysis. Subsequently, the energy use behaviors of occupants in each type of dormitory were characterized to establish a stochastic energy use behavior model. On the basis of the above, NETLOGO was used to construct an agent-based model for dormitories’ energy consumption to dynamically simulate energy use behavior. The accuracy of the model was verified by comparing the simulated values with the measured data. Finally, a building-energy-friendly retrofit scheme was proposed, and it was found that the optimized dormitory reduced energy consumption by 16.07%. Therefore, the results can provide information support for energy-saving decisions during the early design and retrofit phases of buildings. With the popularity of centralized supply, the research methodology may provide an extensive reference for energy management policies and sustainable strategies in the building sector.

Markov Chains and Kinetic Theory: A Possible Application to Socio-Economic Problems

A very important class of models widely used nowadays to describe and predict, at least in stochastic terms, the behavior of many-particle systems (where the word “particle” is not meant in the purely mechanical sense: particles can be cells of a living tissue, or cars in a traffic flow, or even members of an animal or human population) is the Kinetic Theory for Active Particles, i.e., a scheme of possible generalizations and re-interpretations of the Boltzmann equation. Now, though in the literature on the subject this point is systematically disregarded, this scheme is based on Markov Chains, which are special stochastic processes with important properties they share with many natural processes. This circumstance is here carefully discussed not only to suggest the different ways in which Markov Chains can intervene in equations describing the stochastic behavior of any many-particle system, but also, as a preliminary methodological step, to point out the way in which the notion of a Markov Chain can be suitably generalized to this aim. As a final result of the discussion, we find how to develop new very plausible and likely ways to take into account possible effects of the external world on a non-isolated many-particle system, with particular attention paid to socio-economic problems.

Performance gap analysis for Korean building energy efficiency certification

Performance gap between predicted and measured building energy use can be caused by simplifications and assumptions introduced in the building energy modeling process, uncertainties in building operation such as heating and cooling setpoint temperature, operational hours, occupant behavior, heat generated from lights and equipment, etc. In South Korea, the building energy efficiency certification system (BEEC) evaluates annual energy use intensity (EUI, kWh/m2/yr) and assigns ten certificate levels using ECO2, a EUI calculation tool developed by the Korean government that is based on ISO 52016 and DIN V 18599. Firstly, we collected information on 158 non-residential BEEC-certified buildings and their actual energy usage data. Subsequently, we analyzed the performance gap between predicted and measured EUI for 158 non-residential buildings in terms of building types, heating and cooling degree days, chiller types, gross floor area, window-wall ratio, and envelope U-value. The study reveals that the performance gap is significant, showing mean absolute error ranging from 21.3 to 417.4 kWh/m2/yr, mean biased error from −3.2 to 73.4 %, and the coefficient of variance of the root mean squared error from 57.0 to 304.9 %, respectively. It is also noteworthy that even individual building’s annual consumption during eight years (2014–2021) is annually fluctuating, indicating that even the performance gap is a moving target. This study reassures that building energy certification in the design stage should be understood as a rating, not for a prediction because of many unpredictable and stochastic behavior of building energy systems and occupants.

Approaching the Ideal Linearity in Epitaxial Crystalline-Type Memristor by Controlling Filament Growth.

Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behavior with high asymmetry, which aggravates the complexity of peripheral circuit system. Controllable growth of conductive filaments is highly demanded for achieving the highly linear conductance modulation. However, the stochastic behavior of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, the epitaxially grown Hf0.5Zr0.5O2-based memristor with the linearity and symmetry approaching ideal case is reported. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5Zr0.5O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly linear and multi-level conductance modulation capacity. This method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing.

Wiener and Lévy processes to prevent disease outbreaks: Predictable vs stochastic analysis

The study considers stochastic behavior along with modeling, mathematical analysis, theory development, and numerical simulation of the COVID-19 virus. To evaluate current trends and make future projections regarding the basic reproduction number and infection case, we have taken the modified five-compartment SEIRD mathematical model. Since the basic reproduction number R0 is not sufficient to predict the outbreak, we applied the Itô stochastic differential equations (SDEs) along with the Weiner process and Lévy jump to investigate the nature of the disease outbreak. Since COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 “(SARS-CoV-2)”, that has common symptoms including fever, cough, and difficulty breathing, we have illustrated the boundedness and positivity of solutions and investigated the stability of endemic and disease-free equilibrium points. The findings show that the dynamics of disease epidemics are influenced by contact patterns. For all the models, the threshold quantity, R0 is calculated which is the key reason to prove the global and local stability analysis of disease-free equilibrium and endemic equilibrium points. Using the least squares method for data fitting, we have performed a case study on COVID-19 data in Italy for this article. A sensitivity analysis is part of our investigation to find important factors. This paper investigates a stochastic epidemic model (SDE) using Lévy jumps and Weiner processes. Local and global stability in a stochastic setting are examined in the analysis. To view the analytical study, a multitude of numerical results are achieved. The study is important to comprehend the stochastic behavior of the virus in the contagious model in order to prevent similar disease outbreaks in the future.