Dynamic systems often encounter disturbances like sensor outliers, which violate the Gaussian noise assumption in traditional Kalman filters. While maximum correntropy Kalman filters (MCKF) address this issue by utilizing higher-order statistical information, their performance critically depends on the manual selection of a kernel scale parameter. Existing methods with fixed or empirically adjusted kernel scales struggle to handle disturbances of varying intensities, limiting practical applications. This paper presents a novel adaptive Maximum Correntropy Kalman Filter (MCKF) framework. The key contributions are as follows: (1) Adaptive Kernel Scale Optimization: The kernel scale is modeled as a probabilistic variable, and variational Bayesian inference is employed to jointly estimate the system state, enabling automatic kernel scale optimization during the recursive process. (2) Theoretical Analysis and Extension: The computational complexity of the proposed algorithm is analyzed in the context of linear systems, and its theoretical connection to traditional correlation entropy filters is established. Furthermore, the method is extended to nonlinear systems. (3) Performance Enhancement: Experimental evaluations on typical single-target tracking task and complex nonlinear scenario demonstrate that the proposed approach outperforms existing methods. Under various types of noise interference, the average position estimation error is reduced by 40–60%, highlighting its superior adaptability and robustness. In addition, the algorithm is successfully applied to real-world battery State of Charge (SOC) estimation in Internet of Things (IoT) scenarios, demonstrating its practical value in embedded energy management systems.

The study addresses the sensor control problem for multi‐target tracking via delta generalised labelled multi‐Bernoulli ( ‐GLMB) filter, and proposes two novel single‐sensor control schemes. One is that the Rényi divergence is used as the objective function to measure the information gain between the predicted and posterior densities of the ‐GLMB filter, and it is superior for the overall performance of the system. Since most of the sensor control schemes, including the scheme the authors proposed, are faced the curse of computation, thus the other novel scheme is proposed. This scheme, in which the sum of the statistical distances between the predicted states of targets and sensor is used as the objective function, evades the updated step of the multi‐target filter, when computing the objective function for each admissible action. Moreover, these two sensor control schemes are applied to a distributed multi‐sensor system, in which the proposed schemes are used for each sensor node and the generalised covariance intersection method is used to compute the fused multi‐target posterior density. Finally, they adopt the sequential Monte‐Carlo method in bearing and range multi‐target tracking scenarios to illustrate the effectiveness of the proposed methods.

Organizing large scale projects (e.g., Conferences, IT Shows, F1 race) requires precise scheduling of multiple dependent tasks on common resources where multiple selfish entities are competing to execute the individual tasks. In this paper, we consider a well studied and rich scheduling model referred to as RCPSP (Resource Constrained Project Scheduling Problem). The key change to this model that we consider in this paper is the presence of selfish entities competing to perform individual tasks with the aim of maximizing their own utility. Due to the selfish entities in play, the goal of the scheduling problem is no longer only to minimize makespan for the entire project, but rather, to maximize social welfare while ensuring incentive compatibility and economic efficiency. We show that traditional VCG mechanism is not incentive compatible in this context and hence we provide two new practical mechanisms that extend on VCG. These new mechanisms referred to as Individual Completion based Payments (ICP) and Social Completion based Payments (SCP) provide strong theoretical properties including strategy proofness.

In this paper, we address the problem of signal detection in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system by using auto-encoder (AE) network and extreme learning machine (ELM). The existing signal detection algorithms, such as zero-forcing successive-interference-cancellation (ZF-SIC), minimum-mean-square-error successive-interference-cancellation (MMSE-SIC), maximum likelihood detection (MLD) and quantum-genetic radial-basis-function (QGA-RBF) etc., haven't considered the characteristics invariance of signals in the process of transmission. Combined AE network with ELM, a novel signal detection scheme for MIMO-OFDM system is proposed. The proposed algorithm can obtain the features of received signals effectively through AE and recognize the corresponding original signals quickly via ELM. Moreover, the channel matrix is not required in the process of signal detection. We have derived a theoretically model and analyze the feasibility of feature extraction in received signals, and simulations are also carried out to evaluate the performance and compare that with some traditional and state-of-the-art algorithms. The simulation results confirm that the performance of the proposed scheme outperforms that of many detection schemes such as zero-forcing (ZF), ZF-SIC, minimum-mean-square-error (MMSE), MMSE-SIC, and reaches the similar bit-error-rate (BER) performance of MLD and QGA-RBF with much lower complexity.

Uncertainty in activity durations is a key characteristic of many real world scheduling problems in manufacturing, logistics and project management. RCPSP/max with durational uncertainty is a general model that can be used to represent durational uncertainty in a wide variety of scheduling problems where there exist resource constraints. However, computing schedules or execution strategies for RCPSP/max with durational uncertainty is NP-hard and hence we focus on providing approximation methods in this paper. We provide a principled approximation approach based on Sample Average Approximation (SAA) to compute proactive schedules for RCPSP/max with durational uncertainty. We further contribute an extension to SAA for improving scalability significantly without sacrificing on solution quality. Not only is our approach able to compute schedules at comparable runtimes as existing approaches, it also provides lower α-quantile makespan (also referred to as α-robust makespan) values than the best known approach on benchmark problems from the literature.

Scheduling problems in manufacturing, logistics and project management have frequently been modeled using the framework of Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max). Due to the importance of these problems, providing scalable solution schedules for RCPSP/max problems is a topic of extensive research. However, all existing methods for solving RCPSP/max assume that durations of activities are known with certainty, an assumption that does not hold in real world scheduling problems where unexpected external events such as manpower availability, weather changes, etc. lead to delays or advances in completion of activities. Thus, in this paper, our focus is on providing a scalable method for solving RCPSP/max problems with durational uncertainty. To that end, we introduce the robust local search method consisting of three key ideas: (a) Introducing and studying the properties of two decision rule approximations used to compute start times of activities with respect to dynamic realizations of the durational uncertainty; (b) Deriving the expression for robust makespan of an execution strategy based on decision rule approximations; and (c) A robust local search mechanism to efficiently compute activity execution strategies that are robust against durational uncertainty. Furthermore, we also provide enhancements to local search that exploit temporal dependencies between activities. Our experimental results illustrate that robust local search is able to provide robust execution strategies efficiently.

In this study, the degradation pattern of the steppe was taken as the study object. Based on Gutman model, four kinds of degraded grassland were extracted from TM image in 1991 and 2005. And the slope of the research area was computed from DEM. In addition, the degradation pattern of the grassland on different slope was analyzed. The result suggests that the study of degradation pattern of the steppe is helpful to control the grassland degradation, and to attain the aim of the sustainable grassland ecosystem management.

A linear unmixing model considering NDVI and rainfall is used to estimate vegetation fractional coverage, which wet season precipitation and sensitivity of mean NDVI to wet season precipitation are used as state variables in the model. The fractional coverage for each 8 × 8-km area in this region is classified into three categories: (1) a portion of the land surface that always remains as bare soil, fcb; (2) a portion of the fractional coverage that is shrubs, fcS; (3) a remaining portion that consists of bare soil and grasses cover, fcg/b. Study results of average fractional coverage from 1982 to 1999 show that, the fractional coverage of bare soil indicates a clear decrease from West to East, and that of shrubs indicates a general increase, which is not similar to variability of the mean precipitation of wet season.