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In this paper, we consider the on-line estimation of current subsequences for Partially Observable P-time Petri Nets and their starting markings on a sliding horizon composed of steps defined by two successive occurrences of observable transition firings. We propose a general strategy composed of two phases: Phase 1 exploits a simplification of the P-time Petri net under the form of a Timed Petri net; considering a candidate count vector and the relevant starting marking proposed at Phase 1, Phase 2 makes a schedulability analysis by building a system of relations which can be represented by an acyclic conflict-free computation graph. The complete approach avoids the generation of sets which is generally time and space consuming, and provides an optimal solution for each subproblem by using efficient standard tools. [ABSTRACT FROM AUTHOR]

In the context of small unmanned aerial vehicles (UAVs), infrared imaging faces challenges such as low quality, difficulty in detecting small targets, high false alarm rates, and computational resource constraints. To address these issues, we introduce AIMED-Net, an enhancing infrared small target detection net in UAVs with multi-layer feature enhancement for edge computing. Initially, the network encompasses a multi-layer feature enhancement architecture for infrared small targets, including a generative adversarial-based shallow-feature enhancement network and a detection-oriented deep-feature enhancement network. Specifically, an infrared image-feature enhancement method is proposed for the shallow-feature enhancement network, employing multi-scale enhancement to bolster target detection performance. Furthermore, within the YOLOv7 framework, we have developed an improved object detection network integrating multiple feature enhancement techniques, optimized for infrared targets and edge computing conditions. This design not only reduces the model's complexity but also enhances the network's robustness and accuracy in identifying small targets. Experimental results obtained from the HIT-UAV public dataset indicate that, compared to YOLOv7s, our method achieves a 2.5% increase in F1 score, a 6.1% rise in AP for detecting OtherVehicle targets, and a 2.6% improvement in mAP across all categories, alongside a 15.2% reduction in inference time on edge devices. Compared to existing state-of-the-art approaches, our method strikes a balance between detection efficiency and accuracy, presenting a practical solution for deployment in aerial edge computing scenarios. [ABSTRACT FROM AUTHOR]

This work investigates quantitative supervisory control of discrete event systems modeled with Petri nets under the fixed‐initial‐credit energy objective. A weight function referred to as an energy function is defined on a Petri net to characterize the energy level of a transition. The proposed fixed‐initial‐credit energy problem aims to design a supervisor such that the energy level of a transition sequence in a supervised system is higher than 0 under a given initial energy level. The problem is eventually transformed into a two‐player game between a system and a supervisor; supervisor synthesis is reduced to finding a winning strategy in the two‐player game. Instead of enumerating the complete state space of the underlying Petri net, two information structures are utilized, namely the conventional basis reachability graph and the newly proposed essential marking graph, to construct two‐player games based on each of them. It is shown that a winning strategy for a supervisor decoded from the game based on the basis reachability graph of the Petri net is a solution to the problem but is in general restrictive. Further, it is shown that the set of strategies for a supervisor in the game based on the essential marking graph is consistent with that from the game based on the reachability graph of a Petri net. The two developed approaches do not require an exhaustive exploration of the state space of a plant, thus achieving higher efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces a virtual consensus‐based wide area differential protection method through cooperative control concepts and graph theory. Using multi‐agent systems eliminates the need for a direct telecommunication connection between the protective relays to implement the proposed differential protection. In addition, applying telecommunication subgraphs facilitates the establishment of numerous differential protection areas. Collaboration between protected areas is facilitated by common agents, establishing a wide‐area cooperative protection network. The capability of the network operator to define various protection areas and the collaboration between these areas ensure the versatility of the proposed method for various protection purposes. The present paper primarily represents the application of the proposed protection system as a wide‐area supervisor protection for distance relays. Performance evaluations on an IEEE 39‐bus test system illustrate the method's effectiveness in various scenarios. The results show enhanced power system stability and resilience, particularly when traditional methods struggle to detect power swings with high impedance variation rates. [ABSTRACT FROM AUTHOR]

The correspondence pruning task relies on both local and global contexts, which are considered to be essential in inferring the probability of inliers. Many previous approaches seek to devise various structures to make effective use of them, but they either use only a plain structure or base it on their own hypothetical relationships, which leads to some limitations remaining to be improved. Derived from this, we propose our LG-Net including a simple yet effective LGA block and a well-designed GPA block to extract local and global information respectively. Specifically, the LGA block combines local topology into the neighborhood and enhances the representative ability by enabling the interaction of the adjacency neighbors with a simple Softmax operation. Meanwhile, the GPA block prefers correspondences with higher inlier-probability to restrict the interference of outliers. With the guide of relatively reliable prior, it will facilitate the robustness of gathering rich global contextual information. As a consequence, our LG-Net takes both local and global context into account to help successfully recover correspondences. Extensive experiments have demonstrated the better performance of our method comparing with existing state-of-the-art methods on camera pose estimation and homography estimation. • Proposed LGA block captures local topology for completing neighborhood interactions. • GPA module leverages inlier information and enhances global context aggregation. • LG-Net surpasses current works on pose and homography estimation. [ABSTRACT FROM AUTHOR]

Cybersecurity research often focuses primarily or exclusively on the interactions between the attacker, trying to exploit the computer system, and the defender, trying to protect it. However, including the computer users is important because the users' requirements are the reason the computer system exists. An extension of the Petri net formalism, Petri Nets with Players, Strategies, and Costs (PNPSC) was used to model cyberattacks described in the MITRE Common Attack Pattern Enumeration and Classification database. PNPSC models include the attacker, defender, and computer user as "players" attempting to achieve competing goals. Each player can observe the current marking of a subset of the PNPSC net's places and change the stochastic firing rates of a subset of the net's transitions in order to achieve their goals. A mapping between the markings of a player's observable places and the desired firing rates of player's controllable transitions is the player's strategy. A reinforcement learning algorithm was integrated with PNPSC models of three cyberattack patterns to learn strategies for the defender in simulations both with and without a representation of the computer user. A simulation experiment showed that the defender's reward was lower and the defender's learned strategy was different when the user was represented. A second simulation experiment and statistical analysis confirmed that the differences were not due simply to randomness. With the user represented, the system defender must balance security against usability. This research provides a more complete cyberattack model and shows that user models are important in future cybersecurity simulation. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of control laws for Discrete Event Dynamic Systems (DEDSs) represented by Timed Event Graphs (TEGs) under Generalized Marking Constraints (GMCs). The behaviour of TEGs is described using linear equations while GMCs are expressed by weighted inequalities in Min-Plus algebra. We formulate the problem in terms of control linear Min-Plus models. Hence, an algebraic method to calculate control laws ensuring the respect of GMCs is suggested. And once sufficient conditions are satisfied, we propose casual feedbacks to guarantee these marking specifications. Besides, the proposed control strategy is applied to a dual-armed cluster tool, a well-known industrial application. [ABSTRACT FROM AUTHOR]

Accurate and reliable pose estimation of boom-type roadheaders is the key to the forming quality of the tunneling face in coal mines, which is of great importance to improve tunneling efficiency and ensure the safety of coal mine production. The multi-laser-beam target-based visual localization method is an effective way to realize accurate and reliable pose estimation of a roadheader body. However, the complex background interference in coal mines brings great challenges to the stable and accurate segmentation and extraction of laser beam features, which has become the main problem faced by the long-distance visual positioning method of underground equipment. In this paper, a semantic segmentation network for underground laser beams in coal mines, RCEAU-Net, is proposed based on U-Net. The network introduces residual connections in the convolution of the encoder and decoder parts, which effectively fuses the underlying feature information and improves the gradient circulation performance of the network. At the same time, by introducing cascade multi-scale convolution in the skipping connection section, which compensates for the lack of contextual semantic information in U-Net and improves the segmentation effect of the network model on tiny laser beams at long distance. Finally, the introduction of an efficient multi-scale attention module with cross-spatial learning in the encoder enhances the feature extraction capability of the network. Furthermore, the laser beam target dataset (LBTD) is constructed based on laser beam target images collected from several coal mines, and the proposed RCEAU-Net model is then tested and verified. The experimental results show that, compared with the original U-Net, RCEAU-Net can ensure the real-time performance of laser beam segmentation while increasing the Accuracy by 0.19%, Precision by 2.53%, Recall by 22.01%, and Intersection and Union Ratio by 8.48%, which can meet the requirements of multi-laser-beam feature segmentation and extraction under complex backgrounds in coal mines, so as to further ensure the accuracy and stability of long-distance visual positioning for boom-type roadheaders and ensure the safe production in the working face. [ABSTRACT FROM AUTHOR]

Most nonlinear dynamic systems are characterized by uncertainties in models and parameters. Deterministic models cannot account for these uncertainties; therefore, model-based control using such models cannot provide the required performance. It is crucial to establish a practical concept of model-free control as a powerful alternative to model-based control. This paper develops a model-free adaptive backstepping control (MFABC) based on type 2 fuzzy Petri nets for a class of uncertain nonlinear systems. To provide valuable robustness to the MFABC structure, we have exploited the universal approximation property of type 2 fuzzy Petri nets to approximate the different nonlinear functions of the uncertain nonlinear system. The parameter adaptive laws are designed by the Lyapunov function; the stability and error convergence can be guaranteed. The simulation tests show that the proposed MFABC can provide good performance and high accuracy compared with the backstepping control. Moreover, the stability of this control scheme is affirmed. [ABSTRACT FROM AUTHOR]

International institutions strive to achieve their own objectives while operating within a complex network of interdependencies. These interdependencies create an extensive web of relationships that serve as potential pathways for broader institutional impacts. The actions taken by individual institutions, their mutual influences, and the pattern of connectivity collectively shape the overall performance of institutional complexes. Understanding the performance of these complexes is crucial, yet we currently lack a methodology to assess it. To address this gap, we have developed a conceptual framework that integrates three distinct areas of study on three different scales: institutional effectiveness, institutional interlinkages, and institutional networks. This framework enables us to consider what we call the latent net effectiveness, or collective problem-solving potential, of a group of interconnected institutions. To put this framework into practice, we have devised a heuristic model, drawing from the extensive literature on international environmental institutions. As an illustrative example, we have applied this model to a network of 378 multilateral environmental agreements with 810 known issue linkages. Our work underscores the relevance of considering the system-level properties of institutional complexes and emphasizes the need for timely network-based governance and policy interventions to enhance the overall effectiveness of institutional complexes. This article is part of the theme issue 'A complexity science approach to law and governance'. [ABSTRACT FROM AUTHOR]

We develop a two-timing perturbation analysis to provide quantitative insights on the existence of temporal ratchets in an exemplary system of a particle moving in a tank of fluid in response to an external vibration of the tank. We consider two-mode vibrations with angular frequencies ω and α ω , where α is a rational number. If α is a ratio of odd and even integers (e.g., 2 1 , 3 2 , 4 3 ), the system yields a net response: here, a nonzero time-average particle velocity. Our first-order perturbation solution predicts the existence of temporal ratchets for α = 2 . Furthermore, we demonstrate, for a reduced model, that the temporal ratcheting effect for α = 3 2 and 4 3 appears at the third-order perturbation solution. More importantly, we find closed-form formulas for the magnitude and direction of the induced net velocities for these α values. On a broader scale, our methodology offers a new mathematical approach to study the complicated nature of temporal ratchets in physical systems. [ABSTRACT FROM AUTHOR]

The efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere. Plain Language Summary: The temporal and spatial distribution mechanism of physical parameters in near space is very complex. It is of great significance to carry out fine modeling and forecasting of atmospheric temperature profiles for spacecrafts in near space. In this study, taking a single‐station temperature profile in the stratosphere as the object, the mathematical mapping problem of temperature operator is realized by a new deep learning method (deep operator networks). This provides a new research perspective for fine spatiotemporal modeling of temperature profiles. The proposed temperature operator shows low cumulative error performance in forecasting low latitude temperature profiles, and is also suitable for high latitude temperature profiles and mid latitude wind profiles in the stratosphere. Key Points: A deep neural operator method is utilized to forecast hourly atmospheric temperature profiles in the stratosphereThe temperature profile operator network based on BiLSTM denotes lower cumulative error in multi‐step iterative forecastingThe proposed method exhibits potential performance in different locations and atmospheric variables [ABSTRACT FROM AUTHOR]

Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack. [ABSTRACT FROM AUTHOR]

This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and is applicable to Markov chains demonstrating structural regularity. Proportional lumpability extends this idea, proposing that the transition rates of a Markov chain can be modified by certain factors, resulting in a lumpable new Markov chain. This concept facilitates the derivation of precise performance indices for the original process. This paper establishes the well-defined nature of the problem of computing the coarsest proportional lumpability that refines a given initial partition, ensuring a unique solution exists. Additionally, a polynomial time algorithm is introduced to solve this problem, offering valuable insights into both the concept of proportional lumpability and the broader realm of partition refinement techniques. The effectiveness of proportional lumpability is demonstrated through a case study that consists of designing a model to investigate selfish mining behaviors on public blockchains. This research contributes to a better understanding of efficient approaches for handling large stochastic models and highlights the practical applicability of proportional lumpability in deriving exact performance indices. [ABSTRACT FROM AUTHOR]

The $ \mathtt {kppm} $ kppm is a standard procedure to estimate the parameters of the inhomogeneous Cox point process. However, the procedure cannot handle the problem when the models involve correlated covariates. In this study, we develop the $ \mathtt {kppmenet} $ kppmenet , the modified version of the $ \mathtt {kppm} $ kppm , for the inhomogeneous Cox point process involving correlated covariates by considering elastic net regularization. We compare the methodology in a simulation study and apply it to model major-shallow earthquake distribution in Sumatra, Indonesia. We conclude that the $ \mathtt {kppmenet} $ kppmenet outperforms $ \mathtt {kppm} $ kppm when correlated covariates are involved. [ABSTRACT FROM AUTHOR]

Modeling and verification of the correct behavior of embedded real-time systems with strict timing constraints is a well-known and important problem. Failing to fulfill a deadline in system operation can have severe consequences in the practical case. This paper proposes an approach to formal modeling and schedulability analysis. A novel extension of Petri Nets named Constraint Time Petri Nets (C-TPN) is developed, which enables the modeling of a collection of interdependent real-time tasks whose execution is constrained by the use of priority and shared resources like processors and memory data. A C-TPN model is reduced to a network of Timed Automata in the context of the popular Uppaal toolbox. Both functional and, most importantly, temporal properties can be assessed by exhaustive model checking and/or statistical model checking based on simulations. This paper first describes and motivates the proposed C-TPN modeling language and its formal semantics. Then, a Uppaal translation is shown. Finally, three models of embedded real-time systems are considered, and their properties are thoroughly verified. [ABSTRACT FROM AUTHOR]

In recent years, graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral image (HSI) classification owing to their exceptional representation capabilities. However, the high computational requirements of GCNs have led most existing GCN-based HSI classification methods to utilize superpixels as graph nodes, thereby limiting the spatial topology scale and neglecting pixel-level spectral–spatial features. To address these limitations, we propose a novel HSI classification network based on graph convolution called the spatial-pooling-based graph attention U-net (SPGAU). Specifically, unlike existing GCN models that rely on fixed graphs, our model involves a spatial pooling method that emulates the region-growing process of superpixels and constructs multi-level graphs by progressively merging adjacent graph nodes. Inspired by the CNN classification framework U-net, SPGAU's model has a U-shaped structure, realizing multi-scale feature extraction from coarse to fine and gradually fusing features from different graph levels. Additionally, the proposed graph attention convolution method adaptively aggregates adjacency information, thereby further enhancing feature extraction efficiency. Moreover, a 1D-CNN is established to extract pixel-level features, striking an optimal balance between enhancing the feature quality and reducing the computational burden. Experimental results on three representative benchmark datasets demonstrate that the proposed SPGAU outperforms other mainstream models both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

This study aims to optimize the productivity of the plywood manufacturing system within the wood industry. A Petri nets simulation-based technique has been used to evaluate the availability analysis of the plywood manufacturing system. A Petri nets model is created to represent the modeling of the plywood system. The model is subsequently simulated using the licensed program Petri Nets (PN) GRIF 2023.7. This simulation is used to evaluate the performance of the system. In the PN simulation model, timed transitions are fired based on the failure and repair rate of the system. Immediate transitions, on the other hand, have their own guard function for firing which is coded using a logical AND-OR gate. This study also assesses the impact of the repairman on the system's availability. The system's availability is optimized by increasing the number of repairmen. However, once a specific number of repairmen is reached, the system's availability remains constant. This research is highly valuable for determining the optimal number of maintenance staff needed for the wood industrial system. [ABSTRACT FROM AUTHOR]

In the industrial systems there is a requirement that systems should work efficiently for long time. System performance is an important aspect for failure free operation but in real practice complete failure free operation of any production system is seldom possible. Detailed critical literature review for the past thirty three years of Reliability, Maintainability and Availability (RAM) approaches has been carried out which can help to improve performance of Complex systems. Review of some papers provided the detailed information about past and current scenario of RAM practices in research field and industries. Different RAM tools and techniques extracted from the review may be helpful in qualitative and quantitative analysis of the complex systems. In this paper, author tried to focuss on some major aspects of RAM approaches. [ABSTRACT FROM AUTHOR]

The authors consider optimization transformations of the algorithm for constructing minimal generating sets of solutions of systems of linear homogeneous equations (SLHE) over the set of natural numbers. The paper describes the features of such SLHEs, substantiates optimization transformations, and presents examples of the algorithm before and after optimization transformations. It illustrates the applying the algorithm with examples of the analysis of Petri net properties and the construction of a basic solution set in the fields of complex, real, and rational numbers and over finite fields. [ABSTRACT FROM AUTHOR]

In this work, we proposed a numerical model based on the coupling of the volume of fluid–discrete element method and bond particle method (BPM). The simulation of particle bonding and the structural body formation process had been presented, and the inter-particle bonding mechanism was introduced. We also tested dam-busting impact elastic and wedge plates at high Reynolds numbers (1.26 × 107 and 2.16 × 106) and compared the results with numerical simulations. The results show that the model has mean errors of 3.9% and 6.5% for the large and the micro-deformations, respectively. It is in perfect agreement with the curve trends of the test and keeps good convergence for different particle sizes. In addition, we also used the model used to study the hydrodynamic changes in underwater box net structures in offshore aquaculture, and the deformation kinematic properties of box nets under different material strengths were evaluated. This numerical model of this study provides the effective theoretical support and engineering guidance for the further study of the behavior of structural bodies under hydrodynamic action. [ABSTRACT FROM AUTHOR]

The paper is devoted to the theoretical and numerical analysis of the two-step method, constructed as a modification of Polyak's heavy ball method with the inclusion of an additional momentum parameter. For the quadratic case, the convergence conditions are obtained with the use of the first Lyapunov method. For the non-quadratic case, sufficiently smooth strongly convex functions are obtained, and these conditions guarantee local convergence.An approach to finding optimal parameter values based on the solution of a constrained optimization problem is proposed. The effect of an additional parameter on the convergence rate is analyzed. With the use of an ordinary differential equation, equivalent to the method, the damping effect of this parameter on the oscillations, which is typical for the non-monotonic convergence of the heavy ball method, is demonstrated. In different numerical examples for non-quadratic convex and non-convex test functions and machine learning problems (regularized smoothed elastic net regression, logistic regression, and recurrent neural network training), the positive influence of an additional parameter value on the convergence process is demonstrated. [ABSTRACT FROM AUTHOR]

Pose estimation in crowded scenes is key to understanding human behavior in real-life applications. Most existing CNN-based pose estimation methods often depend on the appearance of visible parts as cues to localize human joints. However, occlusion is typical in crowded scenes, and invisible body parts have no valid features for joint localization. Introducing prior information about the human pose structure to infer the locations of occluded parts is a natural solution to this problem. In this paper, we argue that learning structural information based on human joints alone is not enough to address human body variations and could be prone to overfitting. From a perspective on the human pose as a dual representation of joints and limbs, we propose a pose refinement network, coined as dual graph network (DGN), to jointly learn its structural information of body joints and limbs by incorporating the cooperative constraints between two branches. Specifically, our DGN has two coupled graph convolutional network (GCN) branches to model the structure information of joints and limbs. Each stage in the branch is composed of a feature aggregator and a GCN module for inter-branch information fusion and intra-branch context extraction, respectively. In addition, to enhance the modeling capacity of GCN, we design an adaptive GCN layer (AGL) embedded in the GCN module to handle each pose instance based on its graph structure. We also propose a heatmap-guided sampling to leverage the features of the body parts to provide rich visual features for the inference of occluded parts. We perform extensive experiments on five challenging datasets to demonstrate the effectiveness of our DGN on pose estimation. Our DGN obtains significant performance improvement from 67.9 to 72.4 mAP in the CrowdPose dataset with the same CNN-based pose estimator and training strategy as the OPEC-Net. It shows that, compared to the OPEC-Net only considering joints, our DGN has a clear advantage due to the joint consideration of both joints and limbs. Meanwhile, our DGN is also helpful for pose estimation in general datasets (i.e., COCO and Pose track) with less occlusion and mutual interference, demonstrating the generalization power of DGN on refining human poses. [ABSTRACT FROM AUTHOR]

Space debris capture using a tether-net has attracted considerable research attention. To prevent the collapse of the tether-net before debris capture (caused by tension after deployment), various approaches have been proposed, such as the addition of an adhesive material to the tether-net perimeter, use of a net mouth-closing mechanism, or use of a thruster module attached to a weight that controls the weight trajectory after the ejection. However, these mechanisms complicate the net design. To prevent the tether-net collapse prior to debris capture and to ensure its full deployment just before contact with the debris, the tether-net ejection angle must be adjusted in accordance with the distance from the ejector to the debris. In this study, a tether-net ejection mechanism with an adjustable ejection angle is proposed. Moreover, to precisely predict the deployment and bouncing/shrinking behavior of the tether-net, the damping ratio of the net string was determined. Comparison between simulations based on the determined damping ratio and experiments demonstrated good agreement in the maximum deployment area, time to attain the maximum deployment, and the rebound and retraction speeds of the tether-net after complete deployment for large ejection angles. The study findings can contribute to the optimal ejection-angle selection for successful debris capture and prediction of the tether-net behavior after full deployment. [ABSTRACT FROM AUTHOR]

Building resilient urban atmospheric systems is of great importance to sustainable urban development. In order to clarify the impact of the cross-domain flow of pollutants on the resilience of urban atmospheric systems, this paper first defines the concept of resilience of an urban atmospheric system, characterizes the resilience based on the system performance in stages, and proposes a method to quantitatively assess the system resilience. Finally, a Petri net with a time-delay function is constructed to calculate the resilience level of each place under different pollutant emissions and analyze the impact of different pollutant emissions on the resilience of the places, taking the Guanzhong Urban Agglomeration as the research object. The results show that the resilience of atmospheric environmental systems in Tianshui, Pingliang, Qingyang, Linfen, Tongchuan, and Yuncheng is not affected by the cross-domain transmission of pollutants while the resilience of atmospheric environmental systems in Baoji, Xianyang, Xi'an, Shangluo, and Weinan was affected by the transboundary transport of pollutants in varying degrees. In addition, studies have shown that the cross-domain transport of pollutants does not always represent a negative impact on cities, and that transport at the right time can improve the resilience of urban atmospheric environmental systems, such as in Xianyang and Shangluo. The method provides a scientific assessment system for studying the resilience level of urban atmospheric systems from the perspective of cross-domain transport of industrial pollutants. [ABSTRACT FROM AUTHOR]

Location privacy is an important parameter to be addressed in the case of vehicular ad hoc networks. Each vehicle frequently communicates with location-based services to find the nearest location of interest. The location messages communicated with the location server may contain sensitive information like vehicle identity, location, direction, and other headings. A Location-Based Services (LBS) server is not a trusted entity; it can interact with an adversary, compromising the location information of vehicles on the road and providing a way for an adversary to extract the future location tracks of a target vehicle. The existing works consider two or three neighboring vehicles as a virtual shadow to conceal location information. However, they did not fully utilize the semantic location information and pseudonym-changing process, which reduces the privacy protection level. Moreover, a lot of dummy location messages are generated that increase overheads in the network. To address these issues, we propose a Semantic Group Obfuscation (SGO) technique that utilizes both location semantics as well as an efficient pseudonym-changing scheme. SGO creates groups of similar status vehicles on the road and selects random position coordinates for communication with the LBS server. It hides the actual location of a target vehicle in a vicinity. The simulation results verify that the proposed scheme SGO improves the anonymization and entropy of vehicles, and it reduces the location traceability and overheads in the network in terms of computation cost and communication cost. The cost of overhead is reduced by 55% to 65% compared with existing schemes. We also formally model and specify SGO using High-Level Petri Nets (HLPNs), which show the correctness and appropriateness of the scheme. [ABSTRACT FROM AUTHOR]

FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications. [ABSTRACT FROM AUTHOR]

Salient Object Detection (SOD) in RGB-D images plays a crucial role in the field of computer vision, with its central aim being to identify and segment the most visually striking objects within a scene. However, optimizing the fusion of multi-modal and multi-scale features to enhance detection performance remains a challenge. To address this issue, we propose a network model based on semantic localization and multi-scale fusion (SLMSF-Net), specifically designed for RGB-D SOD. Firstly, we designed a Deep Attention Module (DAM), which extracts valuable depth feature information from both channel and spatial perspectives and efficiently merges it with RGB features. Subsequently, a Semantic Localization Module (SLM) is introduced to enhance the top-level modality fusion features, enabling the precise localization of salient objects. Finally, a Multi-Scale Fusion Module (MSF) is employed to perform inverse decoding on the modality fusion features, thus restoring the detailed information of the objects and generating high-precision saliency maps. Our approach has been validated across six RGB-D salient object detection datasets. The experimental results indicate an improvement of 0.20~1.80%, 0.09~1.46%, 0.19~1.05%, and 0.0002~0.0062, respectively in maxF, maxE, S, and MAE metrics, compared to the best competing methods (AFNet, DCMF, and C2DFNet). [ABSTRACT FROM AUTHOR]

Point cloud completion aims to estimate the whole shapes of objects from their partial scans, and one of the main obstacles that prevents current methods from being applied in real-world scenarios is the variety of structural losses in real-scanned objects, which can hardly be fully included and reflected by the training samples. In this paper, we introduce Patch Sequence Autoregression Network (PSA-Net), a learning-based method that can be trained without the partial point clouds in dataset and is inherently adaptable to input scans with different levels of shape incompleteness: It makes restoring the unseen parts of objects be equivalent to predicting the missing tokens in local patch embedding sequences, and such prediction can start from any initial states. Specifically, we first introduce a Sequential Patch AutoEncoder that reconstructs complete point clouds from quantized patch feature sequences. Second, we establish a Mixed Patch Autoregression pipeline that can flexibly infer the whole sequence from any number of known tokens at any positions. Third, we propose a Voting-Based Mapping module that makes input points softly vote for their possible related tokens in sequences based on their local areas, which transforms partial point clouds to masked sequences in test. Quantitative and qualitative evaluations on two synthetic and four real-world datasets illustrate the competitive performances of our network when comparing with existing approaches. [Display omitted] • A Sequential Patch AutoEncoder for shape generation from quantized feature sequence. • A Mixed Patch Autoregression pipeline for token prediction from any initial states. • A Voting-based Mapping module for transformation from partial shapes to sequences. • Competitive performances on two synthetic and four real-world datasets. [ABSTRACT FROM AUTHOR]

Image smoothing constitutes a fundamental task within the realm of computer vision. Effectively preserving the structural integrity of edges during the image smoothing process represents an arduous challenge. Current methodologies persistently encounter issues in adequately exploiting and harnessing the intrinsic value of edge information within images, while simultaneously suffering from the limitation of employing overly simplistic constraints for achieving desirable smoothing outcomes. To solve these problems, we propose a new Edge Consistency and Region Piecewise Flatting Network (ECRPF-Net). To circumvent the impact of the convolution process on edge-preserving properties, our network adopts the approach of superimposing significant edge information onto the feature layer, while also incorporating a weak structure reinforcement module. To efficiently preserve the edge structure, we incorporate an edge consistency module (ECM), which utilizes the edge-response consistency relationship between the input and output images to ensure the preservation of edges. To enhance the quality of image smoothing and mitigate the occurrence of edge artifacts, we introduce the Region Piecewise Flatting Module (RPFM). This module partitions the image into different regions based on the similarities and differences in edge response features, and applies a flexible sub-region smoothing approach to constrain the final output, ensuring superior smoothing results. Experimental results demonstrate the excellent performance of the ECRPF-Net in preserving the prominent edge structure of the image, achieving more visual-appealing smoothing outcomes, and surpassing the majority of existing methods on public datasets. [Display omitted] • Edge consistency constraint can enhance the edge retention effect of images. • Varied smoothing criteria by region reduce artifacts and improve overall results. • Effective feature extraction preserves small structures, enhancing smoothing. • Superimposing edge features benefits image smoothing model. [ABSTRACT FROM AUTHOR]

Deep neural operators (DNOs) have been utilized to approximate nonlinear mappings between function spaces. However, DNOs are confronted with challenges stemming from expanded dimensionality and computational costs tied to unaligned observation data, which ultimately compromise the accuracy of predictions. In this study, we present a hybrid Decoder-DeepONet framework to effectively handle unaligned data. This framework is advanced through its extension to the Multi-Decoder-DeepONet, which leverages an average field to enhance input augmentation. Furthermore, on the basis of the universal approximation theorem, we demonstrate that these frameworks preserve consistencies with operator approximation theory despite the substitution of the product with a decoder net. Two numerical experiments, Darcy problem and flow-field around an airfoil, are conducted to demonstrate the advantages of the proposed methods over conventional DeepONet approaches. The results reveal that both Decoder-DeepONet and Multi-Decoder-DeepONet utilize more compact training data dimensions and occupy less space, markedly enhancing prediction accuracy in the context of unaligned data. [ABSTRACT FROM AUTHOR]

This paper presents a formal security analysis of the ISA100.11a standard protocol using the Colored Petri Net (CPN) modeling approach. Firstly, we establish a security threat model for the ISA100.11a protocol and provide a detailed description and analysis of the identified security threats. Secondly, we use the CPN tool to model the protocol formally and conduct model checking and security analysis. Finally, we analyze and discuss the results of the model checking, which demonstrate that the ISA100.11a standard protocol may have vulnerabilities when certain security threats exist, and provide some suggestions to enhance the security of the protocol. This research provides a certain level of security assurance for the ISA100.11a standard protocol and serves as a reference for similar security research on protocols. [ABSTRACT FROM AUTHOR]

Gas processing industry is associated with high risks which have the potential to cause catastrophic accidents. Safety Instrumented Systems (SISs) are considered the most effective safety barriers in this sector aiming to prevent undesired events and mitigate their consequences. However, several factors can affect their performance including maintenance strategy in place. In this paper, a Stochastic Petri Net (SPN) model is proposed for evaluating maintenance strategies related to SIS. The model is applied to analyze the performance of an Emergency Shutdown System (ESD) in a Flared Gases Recovery Unit located at south Algerian field. Furthermore, the paper investigates the financial impact of proof tests, including direct costs (such as manpower, equipment, and transportation) and indirect costs (such as production losses and gas flaring tax). These costs can be effectively managed and reduced by optimizing proof test intervals and scheduling tests during planned plant shutdowns. The results demonstrate that the proposed Stochastic Petri Net (SPN) model successfully analyzes the impact of imperfect full and partial proof tests on PFD average values of Safety Instrumented Functions (SIFs). In addition, it has been shown through reliability analysis that the proposed model is able to minimize spare parts expenses leading to significant cost savings while maintaining the required safety integrity levels (SILs), about 60% benefit achieved within two years compared to the actual procurement process over same period. [ABSTRACT FROM AUTHOR]

This paper investigates distributed cooperative learning event‐triggered control for discrete‐time strict‐feedback nonlinear multi‐agent systems with an identical system structure and different recurrent reference orbits. A clever system decomposition method is firstly proposed to divide every n$$ n $$‐order agent system into n$$ n $$ first‐order subsystems, which makes it possible to solve the tough problem that every estimated neural weight cannot converge to an unique ideal value based on the existing control scheme. By constructing two interaction typologies, a novel distributed cooperative weight updating law is designed by the introduction of the internal interaction terms between n$$ n $$ subsystems of every agent, the external interaction terms between agents, and the triggering mechanism between agents. With the combination of the graph multiplication operation, the consensus theory and the matrix null space property, the proposed cooperative event‐triggered control scheme guarantees that every agent can track their corresponding recurrent reference trajectories. And further the exponential convergence of all agents' neural estimated weights to a close vicinity around their mutual and unique ideal weights is verified under the condition that the directed interaction topology between agents is strongly connected and balanced. Such a weight convergence makes the proposed scheme has some significant advantages including the small data storage space, the convenient knowledge reuse, the good generalization ability, and the low communication burden. By reinvoking the saved constant weights, a static learning controller is presented for the high‐performance control of similar diversified tracking tasks. Simulation studies and some comparisons are given to show the advantages and effectiveness of the presented scheme. [ABSTRACT FROM AUTHOR]

A recent development in Fault Tree Analysis (FTA), known as Dynamic and Dependent Tree Theory (D2T2), accounts for dependencies between the basic events, making FTA more powerful. The method uses an integrated combination of Binary Decision Diagrams (BDDs), Stochastic Petri Nets (SPN) and Markov models. Current algorithms enable the prediction of the system failure probability and failure frequency. This paper proposes methods which extend the current capability of the D2T2 framework to calculate component importance measures. Birnbaum's measure of importance, the Criticality measure of importance, the Risk Achievement Worth (RAW) measure of importance and the Risk Reduction Worth (RRW) measure of importance are considered. This adds a vital ability to the framework, enabling the influence that components have on system failure to be determined and the most effective means of improving system performance to be identified. The algorithms for calculating each measure of importance are described and demonstrated using a pressure vessel cooling system. [ABSTRACT FROM AUTHOR]

P.M. Pu and Y.M. Liu extended Moore-Smith's convergence of nets to fuzzy topology and Y.M. Liu provided analogous results to J. Kelley's classical characterization theorem of net convergence by introducing the notion of fuzzy convergence classes. In a previous paper, the authors of this study provided modified versions of this characterization by using an alternative notion of convergence of fuzzy nets, introduced by B.M.U. Afsan, named fuzzy net ideal convergence. Our main scope here is to generalize and simplify the preceding results. Specifically, we insert the concept of a fuzzy function ideal convergence class, L , on a non-empty set, X, consisting of triads (f , e , I) , where f is a function from a non-empty set, D, to the set FP (X) of fuzzy points in X, which we call fuzzy function, e ∈ FP (X) , and I is a proper ideal on D, and we provide necessary and sufficient conditions to establish the existence of a unique fuzzy topology, δ , on X, such that (f , e , I) ∈ L iff f I -converges to e, relative to the fuzzy topology δ. [ABSTRACT FROM AUTHOR]

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain's intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence. [ABSTRACT FROM AUTHOR]

This work proposes a symbolic method to verify and enforce the current‐state opacity of labelled Petri nets (LPNs). The notion of basis markings of partially observed Petri nets currently dominates the development of opacity verification and enforcement for discrete event systems. However, the related computational efficiency, to a great extent, depends on the number of basis markings in a system, which increases exponentially with respect to the size of its corresponding LPN model. Binary decision diagrams (BDDs) are capable of computing a set of basis markings in a compact shared structure. To mitigate the computational overheads, a BDD‐based method to efficiently model the structure and behaviour of an LPN is proposed. Then, the current‐state opacity of LPNs is verified and enforced in a symbolic manner. Finally, a number of examples are provided to demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, an active research topic is the connection between Clar's rule, aromaticity, and magnetic properties of polycyclic benzenoid hydrocarbons. In the present work, we employ a meticulous magnetically induced current density analysis to define the net current flowing through any cyclic circuit, connecting it to aromaticity based on the ring current concept. Our investigation reveals that the analyzed polycyclic systems display a prominent global ring current, contrasting with subdued semi‐local and local ring currents. These patterns align with Clar's aromatic π‐sextets only in cases where migrating π‐sextet structures are invoked. The results of this study will enrich our comprehension of aromaticity and magnetic behavior in such systems, offering significant insights into coexisting ring current circuits in these systems. [ABSTRACT FROM AUTHOR]

Long-duration human space missions require intelligent regenerative life support systems that can recycle resources and automatically manage failures. This paper explores using Petri nets to model the reliability and complex interactions of such closed-loop systems. An architecture consisting of primary systems, backups, and consumable reserves is outlined. The automation system that controls everything is described. Petri nets can capture concurrency, failure modes, redundancy, and dynamic behavior. A modular modeling methodology is presented to develop hierarchical Petri net models that scale in fidelity. Elementary fragments represent failures and redundancy. Subsystem modules can be substituted for more detailed models. Analysis and simulation assess system reliability and failure response. This supports designing ultra-reliable systems to safely sustain human life in space. [ABSTRACT FROM AUTHOR]

A model of an information system describes its processes and how resources are involved in these processes to manipulate data objects. This paper presents an extension to the Petri nets formalism suitable for describing information systems in which states refer to object instances of predefined types and resources are identified as instances of special object types. Several correctness criteria for resource- and object-aware information systems models are proposed, supplemented with discussions on their decidability for interesting classes of systems. These new correctness criteria can be seen as generalizations of the classical soundness property of workflow models concerned with process control flow correctness. [ABSTRACT FROM AUTHOR]

In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language and are therefore often used. In this paper, we extend the eST-Miner process discovery algorithm. The eST-Miner computes a set of Petri net places which are considered to be fitting with respect to a certain fraction of the behavior described by the given event log as indicated by a given noise threshold. It evaluates all possible candidate places using token-based replay. The set of replayable traces is determined for each place in isolation, i.e., these sets do not need to be consistent. This allows the algorithm to abstract from infrequent behavioral patterns occurring only in some traces. However, when combining places into a Petri net by connecting them to the corresponding uniquely labeled transitions, the resulting net can replay exactly those traces from the event log that are allowed by the combination of all inserted places. Thus, inserting places one-by-one without considering their combined effect may result in deadlocks and low fitness of the Petri net. In this paper, we explore adaptions of the eST-Miner, that aim to select a subset of places such that the resulting Petri net guarantees a definable minimal fitness while maintaining high precision with respect to the input event log. Furthermore, current place evaluation techniques tend to block the execution of infrequent activity labels. Thus, a refined place fitness metric is introduced and thoroughly investigated. In our experiments we use real and artificial event logs to evaluate and compare the impact of the various place selection strategies and place fitness evaluation metrics on the returned Petri net. [ABSTRACT FROM AUTHOR]

In time Petri nets (TPNs), time and control are tightly connected: time measurement for a transition starts only when all resources needed to fire it are available. Further, upper bounds on duration of enabledness can force transitions to fire (this is called urgency). For many systems, one wants to decouple control and time, i.e. start measuring time as soon as a part of the preset of a transition is filled, and fire it after some delay and when all needed resources are available. This paper considers an extension of TPN called waiting nets that dissociates time measurement and control. Their semantics allows time measurement to start with incomplete presets, and can ignore urgency when upper bounds of intervals are reached but all resources needed to fire are not yet available. Firing of a transition is then allowed as soon as missing resources are available. It is known that extending bounded TPNs with stopwatches leads to undecidability. Our extension is weaker, and we show how to compute a finite state class graph for bounded waiting nets, yielding decidability of reachability and coverability. We then compare expressiveness of waiting nets with that of other models w.r.t. timed language equivalence, and show that they are strictly more expressive than TPNs. [ABSTRACT FROM AUTHOR]

Breast cancer is a highly lethal disease with the highest mortality rate among women worldwide. Breast tumor segmentation from ultrasound images plays a critical role in enabling early detection, leading to a reduction in mortality rates. However, the challenge of ultrasound breast cancer segmentation arises from factors such as indistinct lesion boundaries, noise artifacts, and inhomogeneous intensity distribution within the lesion region. To address the bottlenecks, a novel boundary‐aware shape feature fusion network (BCUIS‐Net) is proposed to segment breast lesion in ultrasound images. Firstly, a boundary‐aware module (BAM) is put forward to accurately localize the ambiguous tumor regions and boundaries by embedding the horizontal and vertical position information into the channel attention. Subsequently, a shape feature fusion (SFF) module is presented to fuse shape features and segmentation features, in order to adaptively extract their complementary features by aggregating contextual information in an attention module. Specifically, the different levels of features from the encoder are up‐sampled to the original image size and fed into the BAM to predict the boundary map. The boundary and decoder‐generated feature maps are thereafter fused by the SFF module to exploit the complementarity between them to correct errors in segmentation and shape features, effectively eliminating false detections and noise in the features to achieve accurate segmentation of pathological regions. Finally, the shape fusion loss is derived from a combination of the binary cross‐entropy loss and the distance map loss to intelligently penalize incorrect predictions and thus improve the attention to boundary locations. The performance of the network is evaluated in two public breast ultrasound datasets. Experimental results verify that the proposed method obtains superior segmentation results and outperforms the most recent state‐of‐the‐art, in which IOU is increased by 2.15% and 2.59% on UDIAT and BUSI, respectively. [ABSTRACT FROM AUTHOR]

We develop a method for estimating a simple matrix for a multidimensional item response theory model. Our proposed method allows each test item to correspond to a single latent trait, making the results easier to interpret. It also enables clustering of test items based on their corresponding latent traits. The basic idea of our approach is to use the prenet (product-based elastic net) penalty, as proposed in factor analysis. For optimization, we show that combining stochastic EM algorithms, proximal gradient methods, and coordinate descent methods efficiently yields solutions. Furthermore, our numerical experiments demonstrate its effectiveness, especially in cases where the number of test subjects is small, compared to methods using the existing L 1 penalty. [ABSTRACT FROM AUTHOR]

In the actual data migration project, in order to solve the problem of data mapping, it is necessary to determine the similarity between the two workflow models. This paper analyzed and expounded the similarity of workflow model, and proposed a path sequence similarity algorithm for the unfolding tree of workflow model based on Petri net. Firstly, it used the deep-first search algorithm and dynamic programming algorithm to search the model, and then obtained all path sequences of the unfolding tree by the proposed algorithm. Finally, it used the edit distance algorithm to calculate the pairwise similarity between the two model se quences, and then completed the model similarity calculation. Compared with other mainstream similarity algorithms, the main ad- vantage is that the partial structure and behavior similarity of the model could be accurately calculated, which could better deter mine the mapping between processes, so as to find a solution to data mapping. The experimental results show that the proposed method is more reasonable and accurate than the mainstream algorithms based on model structure and behavior similarity. [ABSTRACT FROM AUTHOR]