State-space Model Research Articles

Symbolic state-space exploration meets statistical model checking

Efficient reachability analysis, as well as statistical model checking have been proposed for the evaluation of Hybrid Petri nets with general transitions (HPnGs). Both have different (dis-)advantages. The performance of statistical simulation suffers in large models and the number of required simulation runs to achieve a relatively small confidence interval increases considerably. The approach introduced for analytical reachability analysis of HPnGs, however, becomes infeasible for a large number of random variables. To overcome these limitations, this paper applies statistical model checking (SMC) for a stochastic variant of the Signal Temporal Logic (STL) to a pre-computed symbolic state-space representation of HPnGs, i.e., the Parametric Location Tree (PLT), which has previously been used for model checking HPnGs. Furthermore, we define how to reduce the PLT for a given state-based and path-based STL property, by introducing a three-valued interpretation of a given STL property for every location of the PLT. This paper applies learning in the presence of nondeterminism and considers four different scheduler classes. The proposed improvement is especially useful if a large number of training runs is necessary to optimize the probability that a given STL property holds. A case study on a water tank model shows the feasibility of the approach, as well as improved computation times, when applying the above-mentioned reduction for varying time bounds. We validate our results with existing analytical and simulation tools, as applicable for different types of schedulers.

A Dynare toolbox for social learning expectations

Social learning (SL) is a behavioral model in which expectations and the resulting aggregate dynamics stem from the interactions of a large number of heterogeneous agents. Nonetheless, this framework has so far lacked a parsimonious development with a general-solution method. This paper bridges this gap and introduces a Dynare toolbox to solve any linear state-space model with SL expectations, opening up a wide range of potential applications. As an illustration, optimal monetary policy rules are studied in a microfounded New Keynesian (NK) model under SL and rational expectations (RE).

ACMamba: A State Space Model-Based Approach for Multi-Weather Degraded Image Restoration

In computer vision, eliminating the effects of adverse weather conditions such as rain, snow, and fog on images is a key research challenge. Existing studies primarily focus on image restoration for single weather types, while methods addressing image restoration under multiple combined weather conditions remain relatively scarce. Furthermore, current mainstream restoration networks, mostly based on Transformer and CNN architectures, struggle to achieve an effective balance between global receptive field and computational efficiency, limiting their performance in practical applications. This study proposes ACMamba, an end-to-end lightweight network based on selective state space models, aimed at achieving image restoration under multiple weather conditions using a unified set of parameters. Specifically, we design a novel Visual State Space Module (VSSM) and a Spatially Aware Feed-Forward Network (SAFN), which organically combine the local feature extraction capabilities of convolutions with the long-range dependency modeling capabilities of selective state space models (SSMs). This combination significantly improves computational efficiency while maintaining a global receptive field, enabling effective application of the Mamba architecture to multi-weather image restoration tasks. Comprehensive experiments demonstrate that our proposed approach significantly outperforms existing methods for both specific and multi-weather tasks across multiple benchmark datasets, showcasing its efficient long-range modeling potential in multi-weather image restoration tasks.

TTMGNet: Tree Topology Mamba-Guided Network Collaborative Hierarchical Incremental Aggregation for Change Detection

Change detection (CD) identifies surface changes by analyzing bi-temporal remote sensing (RS) images of the same region and is essential for effective urban planning, ensuring the optimal allocation of resources, and supporting disaster management efforts. However, deep-learning-based CD methods struggle with background noise and pseudo-changes due to local receptive field limitations or computing resource constraints, which limits long-range dependency capture and feature integration, normally resulting in fragmented detections and high false positive rates. To address these challenges, we propose a tree topology Mamba-guided network (TTMGNet) based on Mamba architecture, which combines the Mamba architecture for effectively capturing global features, a unique tree topology structure for retaining fine local details, and a hierarchical feature fusion mechanism that enhances multi-scale feature integration and robustness against noise. Specifically, the a Tree Topology Mamba Feature Extractor (TTMFE) leverages the similarity of pixels to generate minimum spanning tree (MST) topology sequences, guiding information aggregation and transmission. This approach utilizes a Tree Topology State Space Model (TTSSM) to embed spatial and positional information while preserving the global feature extraction capability, thereby retaining local features. Subsequently, the Hierarchical Incremental Aggregation Module is utilized to gradually align and merge features from deep to shallow layers to facilitate hierarchical feature integration. Through residual connections and cross-channel attention (CCA), HIAM enhances the interaction between neighboring feature maps, ensuring that critical features are retained and effectively utilized during the fusion process, thereby enabling more accurate detection results in CD. The proposed TTMGNet achieved F1 scores of 92.31% on LEVIR-CD, 90.94% on WHU-CD, and 77.25% on CL-CD, outperforming current mainstream methods in suppressing the impact of background noise and pseudo-change and more accurately identifying change regions.

Mixed orthogonality graphs for continuous‐time state space models and orthogonal projections

In this article, we derive (local) orthogonality graphs for the popular continuous‐time state space models, including in particular multivariate continuous‐time ARMA (MCARMA) processes. In these (local) orthogonality graphs, vertices represent the components of the process, directed edges between the vertices indicate causal influences and undirected edges indicate contemporaneous correlations between the component processes. We present sufficient criteria for state space models to satisfy the assumptions of Fasen‐Hartmann and Schenk (2024a) so that the (local) orthogonality graphs are well‐defined and various Markov properties hold. Both directed and undirected edges in these graphs are characterised by orthogonal projections on well‐defined linear spaces. To compute these orthogonal projections, we use the unique controller canonical form of a state space model, which exists under mild assumptions, to recover the input process from the output process. We are then able to derive some alternative representations of the output process and its highest derivative. Finally, we apply these representations to calculate the necessary orthogonal projections, which culminate in the characterisations of the edges in the (local) orthogonality graph. These characterisations are given by the parameters of the controller canonical form and the covariance matrix of the driving Lévy process.

Design of a Path-Tracking Controller with an Adaptive Preview Distance Scheme for Autonomous Vehicles

This paper presents a method to design a path-tracking controller with an adaptive preview distance scheme for autonomous vehicles. Generally, the performance of a path-tracking controller depends on tire–road friction and is severely deteriorated on low-friction roads. To cope with the problem, it is necessary to design a path-tracking controller that is robust against variations in tire–road friction. In this paper, a preview function is introduced into the state-space model built for better path-tracking performance. With the preview function, an adaptive preview distance scheme is proposed to adaptively adjust the preview distance according to the variations in tire–road friction. Front-wheel steering (FWS) and four-wheel steering (4WS) are adopted as actuators for path tracking. With the state-space model, a linear quadratic regulator (LQR) is adopted as a controller design methodology. In the adaptive preview distance scheme, the best preview distance is obtained from simulation for several tire–road friction conditions. Curve fitting with an exponential function is applied to those preview distances with respect to the tire–road friction. To verify the performance of the adaptive preview distance scheme under variations in tire–road friction, a simulation is conducted on vehicle simulation software. From the simulation results, it was shown that the path-tracking controller with an adaptive preview distance scheme presented in this paper was effective for path tracking against variations in tire–road friction in the peak’s center offset, and the settling delays were reduced by 60% and 23%, respectively.

Workpiece temperature control in friction stir welding of Inconel 718 through integrated numerical analysis and process control

Friction stir welding (FSW) offers significant advantages over fusion welding, particularly for high-strength alloys like Inconel 718. However, achieving optimal surface quality in Inconel 718 FSW remains challenging due to its sensitivity to temperature fluctuations during welding. This study integrates finite element simulations, statistical analysis, and advanced control methodologies to enhance weld surface quality through adequate thermal management. High-fidelity simulations of the FSW process were conducted using a validated 3D transient COMSOL Multiphysics model, producing a comprehensive dataset correlating process parameters (rotational speed, axial force, and welding speed) with workpiece temperature. This dataset facilitated statistical analysis and parameter optimization through Analysis of variance (ANOVA) method, leading to a deeper understanding of process variables. A nonlinear state-space system model was subsequently developed using experimental data and the system identification toolbox in Matlab, incorporating domain-specific insights. This model was rigorously validated with an independent dataset to ensure predictive accuracy. Utilizing the validated model, tailored control strategies, including proportional-integral-derivative (PID) and model predictive control (MPC) in both single and multivariable configurations, were designed and evaluated. These control strategies excelled in maintaining welding temperatures within optimal ranges, demonstrating robustness in response times and disturbance handling. This precision in thermal management is poised to significantly refine the FSW process, enhancing both surface integrity and microstructural uniformity. The strategic implementation of these controls is anticipated to substantially improve the quality and consistency of welding outcomes.

Parameter-coupled state space models based on quasi-Gaussian fuzzy approximation

The accuracy of a fuzzy system’s approximation is closely tied to the performance of fuzzy control systems design, while this system’s interpretability depends on the description of a mechanical model using human language. This research introduces a quasi-Gaussian membership function characterized by a pair of parameters to achieve the sensitivity of a triangular membership function along with the interpretability of Gaussian membership functions. Consequently, a two-dimensional (2-D) quasi-Gaussian membership function is derived, and a method for establishing quasi-Gaussian fuzzy systems (QGFS) using a rectangular grid is proposed. After validating the approximation properties using the sine function for the one-dimensional (1-D) and 2-D QGFS, the systems are applied to approximate the depyrogenation tunnel, a significant piece of equipment in the pharmaceutical industry with various mechanical designs. Validation results indicate that the 1-D and 2-D QGFS can achieve an approximation error varying within a ± 5% range. Meanwhile, the 1-D and 2-D QGFSs are applied to mechanical models of the depyrogenation tunnel with satisfactory final approximation results. Lastly, the 2-D QGFS is capable of demonstrating an excellent description of models with coupled parameters.

CMMamba: channel mixing Mamba for time series forecasting

Transformer-based methods have achieved excellent results in the field of time series forecasting due to their powerful ability to model sequences and capture their long-term dependencies. However, Transformer-based methods are still computationally inefficient and have high costs when modeling long sequences. In addition, previous work ignored the destruction of the original in-channel features by too many mixed features when performing channel mixing. To address the above issues, we introduce Mamba into the field of time series forecasting. The selective state-space model Mamba models long sequences more efficiently by expanding linearly according to the sequence length. Based on this, we propose Channel Mixing Mamba (CMMamba), which uses bidirectional Mamba to model sequences and uses a channel mixing mechanism to select appropriate mixed features to enhance the feature representation within the original channel. By introducing Mamba, we achieve efficient modeling of sequences. We also propose Deep Convolutional Structure (DCS) to learn cross-channel dependencies and temporal order information. Extensive experimental results on six real-world public datasets demonstrate the effectiveness of CMMamba.

Multi-objective optimal control of a hybrid offshore wind turbine platform integrated with multi-float wave energy converters

Offshore floating wind turbines will play a pivotal role in achieving net-zero emissions. This study explores a hybrid platform combining an offshore floating wind turbine with wave energy converters (WECs) from an active control perspective to mitigate turbine damage risk while maximizing wave energy conversion. Initially, the control-oriented state-space modelling of the hybrid platform and the validation of the time-domain Cummins-type models of different orders are performed by the Eulerian-Lagrangian method with model linearization and downscaling. Then, a multi-objective non-causal optimal controller is introduced, balancing wave energy capture and penalizing the nacelle acceleration. When wave energy capture maximization is set as the control target, the proposed optimal controller can improve energy output by 184% as compared against a well-tuned passive damper across all peak periods tested. However this causes peak hub acceleration to increase marginally beyond the desirable limits of 3–4 m/s2 for wind turbine operation. When hub acceleration is penalized, the multi-objective optimal controller can reduce peak values below limits by between 40% and 61% with trivial loss of wave energy capture.

Random Forest Spatial-Temporal and State Space Models to Assess the Impact of Bushfire-Induced Aerosol Events on Ozone Depletion in Australia

Serious concerns exist that the increasing frequency of fires may delay the recovery of ozone given increasing temperatures due to climate change. Australian bushfires from September 2019 to February 2020 were catastrophic. A random forest spatial-temporal (RF sp) analysis using satellite data to detect an association between Australian bushfires and stratosphere ozone on the local depletion of ozone in the vicinity of fires in three regions of Australia (Pacific Ocean, Victoria, NSW) has shown a significant reduction in ozone attributable to aerosols from fires. By intervention analysis, increases in aerosols in all three regions were shown to have a significant and ongoing impact 1–5 days later on reducing ozone (p < 0.0001). Intervention analysis also gave similar periods of aerosol exceedance to those found by Hidden Markov models (HMMs). HMMs established a significant and quantifiable decline in ozone due to bushfire-induced aerosols, with significant lags of 10–25 days between times of aerosol exceedance and subsequent ozone level decline in all three regions.

MUCM-Net: a Mamba powered UCM-Net for skin lesion segmentation

Aim: Skin lesion segmentation is critical for early skin cancer detection. Challenges in automatic segmentation from dermoscopic images include variations in color, texture, and artifacts of indistinct lesion boundaries. This study aims to develop and evaluate MUCM-Net, a lightweight and efficient model for skin lesion segmentation, leveraging Mamba state-space models integrated with UCM-Net architecture optimized for mobile deployment and early skin cancer detection. Methods: MUCM-Net combines Convolutional Neural Networks (CNNs), multi-layer perceptions (MLPs), and Mamba elements into a hybrid feature learning module. Results: The model was trained and tested on the International Skin Imaging Collaboration (ISIC) 2017 and ISIC2018 datasets, consisting of 2,000 and 2,594 dermoscopic images, respectively. Critical metrics for evaluation included Dice Similarity Coefficient (DSC), sensitivity (SE), specificity (SP), and accuracy (ACC). The model’s computational efficiency was also assessed by measuring Giga Floating-point Operations Per Second (GFLOPS) and the number of parameters. MUCM-Net demonstrated superior performance in skin lesion segmentation with an average DSC of 0.91 on the ISIC2017 dataset and 0.89 on the ISIC2018 dataset, outperforming existing models. It achieved high SE (0.93), SP (0.95), and ACC (0.92) with low computational demands (0.055–0.064 GFLOPS). Conclusions: The model’s innovative Mamba-UCM layer significantly enhanced feature learning while maintaining efficiency that is suitable for mobile devices. MUCM-Net establishes a new standard in lightweight skin lesion segmentation, balancing exceptional ACC with efficient computational performance. Its ability to perform well on mobile devices makes it a scalable tool for early skin cancer detection in resource-limited settings. The open-source availability of MUCM-Net supports further research and collaboration, promoting advances in mobile health diagnostics and the fight against skin cancer. MUCM-Net source code will be posted on https://github.com/chunyuyuan/MUCM-Net.

Coordinative optimization for wind farms considering improved fatigue load index

In the wind farm(WF), there exists a serious uneven distribution of damage equivalent load(DEL) on the wind turbine(WT), which increases the maintenance cost of wind power generation. Previous researches focus on a single objective such as the total or the standard deviation of fatigue loads for optimal control. To address these, this paper proposes a coordinative optimization by considering an improved fatigue load index. The coordinative optimization is achieved on both WF level and WT level. For WF level, a state-space model is built for the active power dispatch by utilizing small signal linearization. Then, an improved sensitivity analysis is performed to evaluate the fatigue load on the component of the tower and shaft. Further, a coordinative multi-objective function is designed, which minimizes the total DEL of the WF, and balances the individual fatigue load distribution of the WT. For WT level, an adaptive internal model control is proposed to suppress both tower vibrations and torque fluctuation of the main shaft. Several experiments are performed to verify the significance of the proposed method with the weight analysis of power and load coefficient. The comparison results reveals the superiority of the coordinative optimization in restraining the total DEL of the WF, balancing WF fatigue distribution, decreasing the DEL of the single WT, and satisfying the power reference tracking simultaneously.

Declining population of Harlequin Duck (<i>Histrionicus histrionicus</i>) on the Bow River, Alberta, Canada: 25 years of monitoring

Harlequin Duck (Histrionicus histrionicus) is a small sea duck that winters in coastal waters and breeds on fast-movingmountain streams in western North America. Because of its dependency on streams and coastal near-shore habitat with healthy macroinvertebrate populations, population trends of this species can be used as an indication of healthy freshwater and marine ecosystems. From 1995 to 2020 we conducted roadside surveys for Harlequin Ducks on the Bow River in Banff National Park, Alberta. We calculated the population’s trend by modelling maximum annual count, which showed a population decline over the 25 years of 3.3% per year. The trajectory varied over time: a relatively stable population from 1995 to 2005, a steep decline until 2011, then stabilising at a much lower level with a slight rebound in recent years. The predicted number of ducks from our state-space model closely tracked the maximum number of ducks observed in annual counts. During stable or slightly increasing population estimates the male:female (M:F) ratio fluctuated considerably but stayed high (1.4:1 and 1.3:1, respectively), and during the period of steep population decline the M:F ratio was at its lowest (1.1:1). This declining population trend is concerning because it is occurring in a protected area, but it is similar to data from other studies in the Rocky Mountains and at the coastal wintering area, suggesting that causes may not be solely due to issues on the breeding streams.

Single-view 3D reconstruction via dual attention

Constructing global context information and local fine-grained information simultaneously is extremely important for single-view 3D reconstruction. In this study, we propose a network that uses spatial dimension attention and channel dimension attention for single-view 3D reconstruction, named R3Davit. Specifically, R3Davit consists of an encoder and a decoder, where the encoder comes from the Davit backbone network. Different from the previous transformer backbone network, Davit focuses on spatial and channel dimensions, fully constructing global context information and local fine-grained information while maintaining linear complexity. To effectively learn features from dual attention and maintain the overall inference speed of the network, we do not use a self-attention layer in the decoder but design a decoder with a nonlinear reinforcement block, a selective state space model block, and an up-sampling Residual Block. The nonlinear enhancement block is used to enhance the nonlinear learning ability of the network. The Selective State Space Model Block replaces the role of the self-attention layer and maintains linear complexity. The up-sampling Residual Block converts voxel features into a voxel model while retaining the voxels of this layer. Features are used in the up-sampling block of the next layer. Experiments on the synthetic dataset ShapeNet and ShapeNetChairRFC with random background show that our method outperforms recent state of the art (SOTA) methods, we lead by 1% and 2% in IOU and F1 scores, respectively. Simultaneously, experiments on the real-world dataset Pix3d fully prove the robustness of our method. The code will be available at https://github.com/epicgzs1112/R3Davit.

Thermal resistance capacity model for transient simulation of capillary-box heat exchangers

In this paper, a novel simplified hybrid model is developed to simulate the transient thermal behaviors of capillary-box heat exchangers (CBHEs) buried in the seabed, serving as the front-end heat exchangers in seawater source heat pump system (SWHPs). The thermal resistance and capacity (TRC) approach is applied for derivation of the governing equations inside and outside the heat exchangers. Also, an analytical solution is integrated to model heat transfer in the seabed base. The effects of seawater seepage on the thermal performance of CBHEs are taken into account in the present model. The state-space representation is used to solve the governing equations. The model is verified against experimental data, achieving very good agreement with the mean bias error (MBE) of 7.2 %. A comparison with a three-dimensional computational fluid dynamic (CFD) model indicates that the TRC model's maximum relative error and MBE are 0.7 % and 2.0 % lower than those of the CFD model. Additionally, the ratio of the time required by the CFD and TRC models for a 31-day run was 138. These results demonstrate that the TRC model is sufficiently accurate and fast in the thermal simulation of CBHEs. Furthermore, the thermal properties of CBHEs are examined using the present model. The model in this study provides practical implications for heat transfer analysis and design improvement of CBHEs utilized in SWHPs.

A comprehensive review of deep learning for medical image segmentation

Medical image segmentation provides detailed mappings of regions of interest, facilitating precise identification of critical areas and greatly aiding in the diagnosis, treatment, and understanding of diverse medical conditions. However, conventional techniques frequently rely on hand-crafted feature-based approaches, posing challenges when dealing with complex medical images, leading to issues such as low accuracy and sensitivity to noise. Recent years have seen substantial research focused on the effectiveness of deep learning models for segmenting medical images. In this study, we present a comprehensive review of the various deep learning-based approaches for medical image segmentation and provide a detailed analysis of their contributions to the domain. These methods can be broadly categorized into five groups: CNN-based methods, Transformer-based methods, Mamba-based methods, semi-supervised learning methods, and weakly supervised learning methods. Convolutional Neural Networks (CNNs), with their efficient feature self-learning, have driven major advances in medical image segmentation. Subsequently, Transformers, leveraging self-attention mechanisms, have achieved performance on par with or surpassing Convolutional Neural Networks. Mamba-based methods, as a novel selective state-space model, are emerging as a promising direction. Furthermore, due to the limited availability of annotated medical images, research in weakly supervised and semi-supervised learning continues to evolve. This review covers common evaluation methods, datasets, and deep learning applications in diagnosing and treating skin lesions, hippocampus, tumors, and polyps. Finally, we identify key challenges such as limited data, diverse modalities, noise, and clinical applicability, and propose future research in zero-shot segmentation, transfer learning, and multi-modal techniques to advance the development of medical image segmentation technology.

Exchange-rate regime and sectorial profitability in a small open economy: evidence from Argentina’s recent experience

This paper studies, both theoretically and empirically, tradable ( T ) and non-tradable ( N ) profit rates dynamics in a small, price-taker peripheral economy under foreign exchange controls and parallel exchange rates (ER). Using a state-space econometric representation of the Argentine economy for the period 2016–2023, we found evidence to support three main hypotheses derived from the theoretical models. First, an official exchange rate depreciation increases tradable goods profit rates but has no short-term effect on non-tradeable goods profitability. Second, the rise of the parallel exchange rate increases sector N ’s profit rate but has no effect on T ’s. Moreover, this effect depends on the magnitude of the ER gap in a positive, but non-linear way. Third and finally, over sufficient time, both profit rates tend to influence each other, through the action of competition. This means that, eventually, and increase (depreciation) in the official exchange rate exerts its influence on sector N ’s profit rate; while, if sufficiently persistent and big enough, a rise in the financial ER ends up affecting sector T ’s profit rate too.

Enhancing bridge damage detection with Mamba-Enhanced HRNet for semantic segmentation.

With the acceleration of urbanization, bridges, as crucial infrastructure, their structural health and stability are paramount to public safety. This paper proposes Mamba-Enhanced HRNet for bridge damage detection. Mamba-Enhanced HRNet integrates the advantages of HRNet's multi-resolution parallel design and VMamba's visual state space model. By replacing the residual convolutional blocks in HRNet with a combination of VSS blocks and convolution, this model enhances the network's capability to capture global contextual information while maintaining computational efficiency. This work builds an extensive dataset with multiple damage kinds and uses Mean Intersection over Union (Mean IoU) as the assessment metric to assess the performance of Mamba-Enhanced HRNet. Experimental results demonstrate that Mamba-Enhanced HRNet achieves significant performance improvements in bridge damage semantic segmentation tasks, with Mean IoU scores of 0.963, outperforming several other semantic segmentation models.

‘Slim pickings?’: Extreme large recruitment events may induce density-dependent reductions in growth for Alaska sablefish (Anoplopoma fimbria) with implications for stock assessment

Growth processes mediate survival and fecundity within fish populations, which are fundamental in regulating population dynamics. Therefore, accurate estimates of population scale and sustainable exploitation levels in contemporary fishery stock assessment models rely heavily on understanding and accurately characterizing growth processes. However, empirical studies that relate population-level changes in growth patterns to observable ecosystem and population conditions remain scarce. In particular, few studies directly consider the influence of intra-specific competition (i.e., cohort effects) on growth variability, and its associated implications for stock assessment estimates. Focusing on Alaska sablefish as a case study, we illustrate how multiple unprecedented large recruitment events since 2014 resulted in density-dependent declines in growth on the population-level scale, using a state-space growth model. Furthermore, we demonstrate how incorporating cohort-specific growth variability within the Alaska sablefish stock assessment model resulted in substantial differences in estimates of spawning biomass and recommended harvest levels. Overall, results from this study underscore the significance of cohort effects on growth processes and their implications for stock assessment models and associated harvest recommendations.