Stochastic Sequences Research Articles

Spatial inhomogeneity analyses of extreme sea levels along Lianyungang coast based on numerical simulation and Monte Carlo model

The prediction of extreme sea levels in coastal areas has been a prominent requirement of coastal engineering design and planning. The research was based on the frequent disasters caused by typhoon storm surges in China and the demand for reasonable improvement of port structures in coastal areas. Delft3D hydrodynamic model and the Monte Carlo model were integrated to predict the spatial distribution of extreme sea levels along the coast of Lianyungang city in Jiangsu province. The Monte Carlo model was adopted to generate stochastic typhoon characteristic sequences as meteorological driving forces. In the hydrodynamic model, meteorological factors were coupled with the astronomical tide to simulate storm surge scenarios. Extreme sea levels were estimated by ranking maximum levels of simulated storm surge events. This programming was feasible compared to the prediction results of frequency analysis methods. The research showed that the predicted 1 in 100-year extreme sea level of Lianyungang station was 3.75 m, while the predicted 1 in 200-year value was 3.85 m. The programming was adopted to predict extreme sea levels of several stations and analyze the spatial distribution characteristics of extreme sea levels in four port areas of Lianyungang Port. Results revealed that extreme sea levels in modeling areas had significant spatial inhomogeneity, which might be related to the concavity of coastlines, islands, and terrains. Estimating spatial extreme sea levels could better cognize the influence of inundation disasters in coastal areas and be conducive to the design of port structures and the safety of port operations.

Distributed-observer-based adaptive fault tolerant formation maneuver for autonomous surface vehicles under stochastic cyber-attack

This paper investigates the distributed adaptive fault-tolerant formation maneuver control strategy for multiple autonomous surface vehicles (ASVs) systems subject to stochastic cyber-attack and actuator failures. Existing works have provided solutions against continuous attacks on communication channels while ignoring the increasingly prevalent intermittent attacks. Compared to the former, the latter are more challenging to detect, having been insufficiently described and addressed. Motivated by this observation, this article is concerned with the stochastic intermittent injection attacks, which are modeled as stochastic impulsive sequences, characterized by stochastic injection instant and intensity, leading to unpredictable changes in the state information transmitted by the neighbors. Taking this dilemma into consideration, a novel distributed observer is proposed, capable of effectively observing the target formation even in the presence of stochastic intermittent injection attacks. Meanwhile, a large feedback gain is incorporated into the observer to reduce the impact of attacks on system stability. A linear sliding manifold, together with the adaptive algorithm, is utilized to construct a fault tolerant control architecture for each follower ASV without employing model parameters which can confront the embarrassing scenario of actuator failures. Eventually, theoretical deduction and numerical simulations are conducted to confirm the feasibility of the proposed collaborative control scheme.

Solving a Stochastic Multi-Objective Sequence Dependence Disassembly Sequence Planning Problem with an Innovative Bees Algorithm

As the number of end-of-life products multiplies, the issue of their efficient disassembly has become a critical problem that urgently needs addressing. The field of disassembly sequence planning has consequently attracted considerable attention. In the actual disassembly process, the complex structures of end-of-life products can lead to significant delays due to the interference between different tasks. Overlooking this can result in inefficiencies and a waste of resources. Therefore, it is particularly important to study the sequence-dependent disassembly sequence planning problem. Additionally, disassembly activities are inherently fraught with uncertainties, and neglecting these can further impact the effectiveness of disassembly. This study is the first to analyze the sequence-dependent disassembly sequence planning problem in an uncertain environment. It utilizes a stochastic programming approach to address these uncertainties. Furthermore, a mixed-integer optimization model is constructed to minimize the disassembly time and energy consumption simultaneously. Recognizing the complexity of the problem, this study introduces an innovative bees algorithm, which has proven its effectiveness by showing a superior performance compared to other state-of-the-art algorithms in various test cases. This research offers innovative solutions for the efficient disassembly of end-of-life products and holds significant implications for advancing sustainable development and the recycling of resources.

T-controllability of higher-order fractional stochastic delay system via integral contractor

The existence, uniqueness and trajectory (T)-controllability results for mild solutions to the fractional neutral stochastic integro-delay differential system are presented in this article. To demonstrate the results, the concept of bounded integral contractors is combined with the stochastic result and sequencing technique. In contrast to previous publications, we do not need to specify the induced inverse of the controllability operator to prove the controllability results, and the relevant non-linear function does not have to meet the Lipschitz condition. Furthermore, T-controllability results for neutral integro-delay differential systems with non-local conditions have been established. Finally, an application to demonstrate the acquired results is discussed.

Lightweight and Error-Tolerant Stereo Matching with a Stochastic Computing Processor

Stereo matching, utilized in diverse fields, poses a challenge to systems in resource-constrained environments due to the significant growth of computational load with image resolution. The challenge is crucial for the systems because fields utilizing stereo matching require short operational time for real-time applications and low power architecture. Stochastic computing (SC) is able to be a valuable approach to address the challenge by reducing the computational load by representing binary numbers with stochastic sequences, which are encoded as a probability value, and by leveraging the concept of mathematical probability. Also, it is possible for a system to be error-tolerant by utilizing the characteristics of stochastic computing. Therefore, in this paper, we propose an approach for lightweight and error-tolerant stereo matching with a hardware-implemented stochastic computing processor. To verify the feasibility and error tolerance of the proposed system, we implemented the proposed system and conducted experiments comparing depth maps with or without stochastic computing by calculating similarities. According to the experimental results, the proposed system indicated no significant differences in output depth maps and achieved an improvement in the depth maps from error-injected input images by an average of 58.95%. Therefore, we demonstrated that stereo matching with stochastic computing is feasible and error-tolerant.

Fuzzy control for networked systems with mixed data missing under quantization based event-triggered scheme

This paper presents an innovative event-triggered fuzzy control approach for networked systems operating over unreliable network channels. Traditional complicated modeling and analysis process of network induced issues is replaced by a bran-new logarithmic quantization based event-triggered scheme method. The joint characterization of mixed data missing, arising from the event-triggered mechanism and unreliable network channels, becomes a challenging issue due to disrupted data sequences and variable relations. Consequently, this paper contributes a formulated auxiliary stochastic sequence approach to overcome the difficulty of dynamics modeling. With an formulated event driven controller, the modeling and implementation of the closed-loop systems are constructed and further simplified. Considering the difference and relation between the membership functions of plant and controller, we present a new fuzzy output feedback controller design method to obtain feasible result via uncertainty based method. Finally, we verify the validity of the designed control policy via a simulation example.

Illustrating the importance of edge constraints in backbones of bipartite projections.

Bipartite projections (e.g., event co-attendance) are often used to measure unipartite networks of interest (e.g., social interaction). Backbone extraction models can be useful for reducing the noise inherent in bipartite projections. However, these models typically assume that the bipartite edges (e.g., who attended which event) are unconstrained, which may not be true in practice (e.g., a person cannot attend an event held prior to their birth). We illustrate the importance of correctly modeling such edge constraints when extracting backbones, using both synthetic data that varies the number and type of constraints, and empirical data on children's play groups. We find that failing to impose relevant constraints when the data contain constrained edges can result in the extraction of an inaccurate backbone. Therefore, we recommend that when bipartite data contain constrained edges, backbones be extracted using a model such as the Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC).

A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network

Accurate real-time prediction of link quality is crucial for enhancing the reliable responsiveness of wearable devices within Wireless Wearable Sensor Networks (WWSNs). Specifically, the Signal-to-Noise Ratio (SNR), a pivotal parameter for predicting link quality, exhibits complex temporal characteristics influenced by stochastic and non-stochastic factors. To improve the accuracy of link quality prediction in WWSNs, we aim to explore a novel predictive model, introducing a filtering layer that seeks to enhance the precision of predicting upper and lower boundaries of link reliability confidence intervals. First, we adopt the SNR time series as the evaluation metric and decompose the SNR sequences into time-varying and stochastic standard deviation sequences by wavelet decomposition. Subsequently, we propose an innovative SCNN-LSTM model, incorporating the SincNet filtering layer to extract specific frequency components from the input SNR sequences. Afterward, integrating standard deviation sequences, the model predicts upper and lower boundaries of link reliability confidence intervals. Finally, we conduct the validation experiments on the public dataset LightGBM-LQP and our WWSN dataset Basketball shot. Compared to BPNN, ARIMA, and WNN, the evaluation matrices of MAE, RMSE, R2 in SCNN-LSTM have been improved, and the deviation between the predicted standard deviation and the actual standard deviation has reached the minimum of 0.1. The results demonstrate that SCNN-LSTM outperforms classical prediction models in predicting upper and lower limits of link reliability confidence intervals in WWSNs.

Non-Markovian processes on heteroclinic networks.

Sets of saddle equilibria connected by trajectories are known as heteroclinic networks. Trajectories near a heteroclinic network typically spend a long period of time near one of the saddles before rapidly transitioning to the neighborhood of a different saddle. The sequence of saddles visited by a trajectory can be considered a stochastic sequence of states. In the presence of small-amplitude noise, this sequence may be either Markovian or non-Markovian, depending on the appearance of a phenomenon called lift-off at one or more saddles of the network. In this paper, we investigate how lift-off occurring at one saddle affects the dynamics near the next saddle visited, how we might determine the order of the associated Markov chain of states, and how we might calculate the transition probabilities of that Markov chain. We first review methods developed by Bakhtin to determine the map describing the dynamics near a linear saddle in the presence of noise and extend the results to include three different initial probability distributions. Using Bakhtin's map, we determine conditions under which the effect of lift-off persists as the trajectory moves past a subsequent saddle. We then propose a method for finding a lower bound for the order of this Markov chain. Many of the theoretical results in this paper are only valid in the limit of small noise, and we numerically investigate how close simulated results get to the theoretical predictions over a range of noise amplitudes and parameter values.

Characterization of Gauss–Markov stochastic sequences for mission analysis

In real scenarios, the spacecraft deviates from the intended paths owing to uncertainties in dynamics, navigation, and command actuation. Accurately quantifying these uncertainties is crucial for assessing the observability, collision risks, and mission viability. This issue is further magnified for CubeSats because they have limited control authority and thus require accurate dispersion estimates to avoid rejecting viable trajectories or selecting unviable ones. Trajectory uncertainties arise from random variables (e.g., measurement errors and drag coefficients) and processes (e.g., solar radiation pressure and low-thrust acceleration). Although random variables generally present minimal computational complexity, handling stochastic processes can be challenging because of their noisy dynamics. Nonetheless, accurately modeling these processes is essential, as they significantly influence the uncertain propagation of space trajectories, and an inadequate representation can result in either underestimation or overestimation of the stochastic characteristics associated with a given trajectory. This study addresses the gap in characterizing process uncertainties, represented as Gauss–Markov processes in mission analysis, by presenting models, evaluating derived quantities, and providing results on the impact of spacecraft trajectories. This study emphasizes the importance of accurately modeling random processes to properly characterize stochastic spacecraft paths.

Resource-rational account of sequential effects in human prediction.

An abundant literature reports on 'sequential effects' observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume non-stationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from 0.05 to 0.95. Although in this task the environment is static and the Bayesian belief converges, subjects' predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this 'attractive effect', but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a 'repulsive effect', observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.

Solving a Real-Life Stochastic Car Batching and Sequencing Problem With Dynamic Programming Approaches

Solving a Real-Life Stochastic Car Batching and Sequencing Problem With Dynamic Programming Approaches

Approximate Multiplier Design With LFSR-Based Stochastic Sequence Generators for Edge AI

Approximate Multiplier Design With LFSR-Based Stochastic Sequence Generators for Edge AI

A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant

This article focuses on addressing the chiller sequencing problem of chiller plants by establishing a comprehensive energy management framework. The contribution of this work is threefold. Firstly, a distributive modeling architecture is presented that establishes five concurrent models as input to the framework. A synergy among these models is exploited to formulate a chance-constrained stochastic chiller sequencing problem. Secondly, a quantified life expectancy model of the chiller plant is introduced and a case is made for why it is influential in delivering industrially applicable solutions. The model attempts to strike a balance between economic optimality and improved reliability. Thirdly, a chiller data pre-processing protocol incubating two heuristic algorithms is proposed to address measurement uncertainties of the chiller plant state variables. Furthermore, this work develops a robust ensemble model to accurately forecast the cooling load and embeds a chain of reformulations to improve the global solution's optimality. The developed framework is realized with the plant at the Indian Institute of Technology Gandhinagar. The results confirm that the proposed framework leads to a significant amount of power savings. In comparison to conventional scheduling, the chiller plant power consumption can be reduced by up to 6.2 %, thereby illustrating its efficacy.

Filtering problem for sequences with periodically stationary multi-seasonal increments with spectral densities allowing canonical factorizations

We consider a stochastic sequence $\xi(m)$ with periodically stationary generalized multiple increments of fractional order which combines cyclostationary, multi-seasonal, integrated and fractionally integrated patterns. The filtering problem is solved for this type of sequences based on observations with a periodically stationary noise. When spectral densities are known and allow the canonical factorizations, we derive the mean square error and the spectral characteristics of the optimal estimate of the functional $A{\xi}=\sum_{k=0}^{\infty}{a}(k) {\xi}(-k)$. Formulas that determine the least favourable spectral densities and the minimax (robust) spectralcharacteristics of the optimal linear estimate of the functional are proposed in the case where the spectral densities are not known, but some sets of admissible spectral densities are given.

Stochastic power processing through logic operation of power packets.

This article presents an application of the recently proposed logic operation of power based on power packetization. In a power packet despatching system, the power supply can be considered as a sequence of power pulses, where the occurrence of pulses follows a probability that corresponds to the capacity of the power sources or power lines. In this study, we propose a processing scheme to reshape a stream of power packets from such stochastic sequences to satisfy the load demand. The proposed scheme is realized by extending the concept of stochastic computing to the power domain. We demonstrate the operation of the proposed scheme through experiments and numerical simulations by implementing it as a function of a power packet router, which forms a power packet despatching network. The stochastic framework proposed in this study provides a new design foundation for low-power distribution networks as an embodiment of the close connection between the cyber and physical components.

DECENTRALIZED CONDITIONAL GRADIENT METHOD ON TIME-VARIABLE GRAPHS

In this paper, we consider a generalization of the decentralized Frank-Wulff algorithm for network time variables, study the convergence properties of the algorithm, and carry out the corresponding numerical experiments. The changing network is modeled as a deterministic or stochastic sequence of graphs.

Distributed observer for LTI systems under stochastic impulsive sequential attacks

In this paper, we consider the distributed state estimation problem of continuous-time linear time-invariant (LTI) systems, where communication channels are attacked by stochastic impulsive sequences. In a networked system, the communication channel between an agent and its neighbor is vulnerable to being attacked. Continuous attacks have been widely studied so far, but with the development of attack techniques, intermittent attacks have been emerging, which are more difficult to be identified and detected, and have not been well portrayed and studied. Therefore, this paper investigates a random intermittent injection attack, which leads to abrupt and random changes of the state information transmitted by the neighbors. First, this attack is modeled as a stochastic impulsive sequential attack, where both its impulsive time and impulsive intensity are random. Then a distributed observer under stochastic impulsive sequential attack is proposed. Since the impulsive attack destroys the stability of the system, in order to attenuate the impact of the impulsive attack, a large feedback gain is necessary. By using the graph theory, matrix theory and stochastic analysis, sufficient conditions for the existence of the distributed observer in the sense of mean square and in the sense of almost sure are presented, respectively. Finally, the effectiveness of the proposed distributed observer is verified by numerical simulations.

Direct Probability Integral Method for Seismic Performance Assessment of Earth Dam Subjected to Stochastic Mainshock–Aftershock Sequences

Studying the impact of mainshock–aftershock sequences on dam reliability is crucial for effective disaster prevention measures. With this purpose in mind, a new method for stochastic dynamic response analyses and reliability assessments of dams during seismic sequences has been proposed. Firstly, a simulation method of stochastic seismic sequences is described, considering the dependence between mainshock and aftershock based on Copula function. Then, a novel practical framework for stochastic dynamic analysis is established, combined with the improved point selection strategy and the direct probability integration method (DPIM). The DPIM is employed on a nonlinear system with one degree of freedom and compared with Monte Carlo simulation (MCS). The findings reveal that the method boasts exceptional precision and efficiency. Finally, the seismic performance of a practical dam was evaluated based on the above method, which not only accurately estimates the response probability distribution and dynamic reliability of the dam, but also greatly reduces the required calculations. Furthermore, the impact of aftershocks on dam seismic performance is initially evaluated through a probability approach in this research. It is found that seismic sequences will significantly increase the probability of earth dam failure compared with sequences of only mainshocks. In addition, the influence of aftershocks on reliability will further increase when the limit state is more stringent. Specifically, the novel analysis method proposed in this paper provides more abundant and objective evaluation indices, providing a dynamic reliability assessment for dams that is more effective than traditional evaluation methods.

Integrated deployment of dedicated lane and roadside unit considering uncertain road capacity under the mixed-autonomy traffic environment

The rapid development of automated vehicle (AV) technologies enables us to explore how to update the urban road infrastructure to cater for the upcoming mixed-autonomy traffic with both AVs and human-driven vehicles (HVs). In this study, we aim to seek an optimal solution to integrate the deployment of AV-dedicated lane and roadside unit assisting automated driving subject to a limited budget by considering the route choice behaviors of AVs and HVs. An AV-dedicated lane segregates AVs from the mixed traffic to create a fully automated driving environment and eliminate disturbances from HVs. The deployment of roadside units assisting automated driving could overcome the connectivity gap for AVs and lower their headway when they follow an HV. We seek to develop a robust and optimal integrated deployment solution that accommodates the uncertain road capacity caused by the stochastic mixed AV and HV fleet sequence. We first establish the stochastic network equilibrium conditions for the mixed traffic, and formulate the integrated deployment problem as a mathematical program with complementarity constraints (MPCC). The MPCC is approximated by a mixed-integer linear programing (MILP) model, which allows existing algorithms for its global optimum. We further develop two interesting strategies, including domain reduction and breakpoint selection, to enhance the effectiveness of the MILP model. Numerical experiments are finally carried out to evaluate the feasibility of research methodology proposed in this study and find some valuable insights.