Representation Of Constraints Research Articles

Integrating Spatiotemporal and Travel-Related Information for Accurate Urban Passenger Profiling Using GANs

The elaborate description of passenger travel profiles is of significant importance in urban planning, socioeconomic structural design, and individual travel preference analysis. Traditional models often lack consideration of personalized features and exhibit suboptimal performance in constructing spatiotemporal dependencies. To address these issues, this paper proposes a method that integrates spatiotemporal information with travel-related information and employs generative adversarial networks (GANs) for adversarial training. This method accurately fits the true distribution of user travel data, thereby providing detailed profiles of public transportation passengers’ travel behavior. Specifically, the proposed approach considers the complete travel chain of individuals, establishes a spatiotemporal constraint representation model, and utilizes GANs to simulate the distribution of passenger travel, obtaining more compact and high-level travel vector features. The empirical results demonstrate that the proposed method accurately captures passengers’ travel patterns in both the temporal and spatial dimensions, offering technical support for urban transportation planning.

Rigid body modular modeling method for multiple reusable component weapon systems based on bond space theory

Abstract This article proposes a rigid body modular modeling method based on bond space theory. Aiming at the rigid components with the same characteristics that are heavily used in artillery weapon systems, the concept of bond space sub-model is proposed, and the rigid body module and constraint module are established and deduced. Considering the differences between ideal constraints and actual constraints such as gaps or heat losses, an elastic constraint bonding space module is proposed to improve the representation of ideal constraints. By combining bond space modules with the same characteristics and solving them, the process of analyzing complex systems will be greatly simplified. In addition, a study case of ammunition box feeding is presented to investigate the computational results of this modeling method; the analysis results show that this modeling method has good computational accuracy on the basis of improved computational efficiency.

Bond-graph representation of ideal constraints via the principle of virtual power

Bond-graph representation of ideal constraints via the principle of virtual power

Joint local smoothness and low-rank tensor representation for robust multi-view clustering

Low-rank tensor representation (LRTR) has become a significant method for achieving improved multi-view clustering (MVC) performance. Generally, most LRTR methods impose a tensor low-rank constraint (TLRC) on a tensor, which is spliced by the representation matrix of each view, to explore the low-rank prior hidden in these representation matrices. However, two problems remain unsolved. On the one hand, these representation matrices still contain noise since the raw data usually possess noise; on the other hand, and more importantly, is there any other prior information that can be explored among these representation matrices? Since samples located in the same cluster are similar, the coefficients of their representation matrices should exhibit similar. That is, these representation matrices should be local smoothness (LS), which is also verified by our numerical tests. In addition, the use of the LS prior helps to denoise the noise contain in the data. Then, in this paper, we propose a joint LS and LRTR for robust MVC method (LS-LRTR), which can simultaneously exploit the low-rank and LS priors. Specifically, we utilize a TLRC to explore the low-rank prior in the representation matrices. Subsequently, to mine the LS prior and further reduce the influence of noise, we introduce the Total Variation (TV) norm to the constraint representation matrices. Then, we fuse the TLRC and TV norm into a unified framework. Additionally, we apply an Augmented Lagrange Multiplier to solve the optimization problem of LS-LRTR. Experiments conducted on several datasets indicate that LS-LRTR outperforms the state-of-the-art clustering methods.

Partitioned scheduling with safety-performance trade-offs in stochastic conditional DAG models

This paper is motivated by robotic systems that solve difficult real-world problems such as search and rescue (SAR) or precision agriculture 11This work was supported by NSF under Grant CNS-1932074.. These applications require robots to operate in complex, uncertain environments while maintaining safe interactions with human teammates within a specified level of performance. In this paper, we study the scheduling of real-time applications on heterogeneous hardware platforms inspired by such contexts. To capture the stochasticity due to unpredictable environments, we propose the stochastic heterogeneous parallel conditional DAG (SHPC-DAG) model, which extends the most recent HPC-DAG model in two regards. First, it uses conditional DAG nodes to model the execution of computational pipelines based on context, while the stochasticity of DAG edges captures the uncertain nature of a system’s environment or the reliability of its hardware. Second, considering the pessimism of deterministic worst-case execution time (WCET), it uses probability distributions to model the execution times of subtasks (DAG nodes). We propose a new partitioning algorithm Least Latency Partitioned (LLP), which considers precedence constraints among nodes during the allocation process. Coupled with a scheduling algorithm that accounts for varying subtask criticality and constraints, the end-to-end latencies of safety-critical paths/nodes are then minimized. We use tasksets inspired by real robotics to demonstrate that our framework allows for efficient scheduling in complex computational pipelines, with more flexible representation of timing constraints, and ultimately, safety-performance tradeoffs.

Scalable Primal Heuristics Using Graph Neural Networks for Combinatorial Optimization

By examining the patterns of solutions obtained for various instances, one can gain insights into the structure and behavior of combinatorial optimization (CO) problems and develop efficient algorithms for solving them. Machine learning techniques, especially Graph Neural Networks (GNNs), have shown promise in parametrizing and automating this laborious design process. The inductive bias of GNNs allows for learning solutions to mixed-integer programming (MIP) formulations of constrained CO problems with a relational representation of decision variables and constraints. The trained GNNs can be leveraged with primal heuristics to construct high-quality feasible solutions to CO problems quickly. However, current GNN-based end-to-end learning approaches have limitations for scalable training and generalization on larger-scale instances; therefore, they have been mostly evaluated over small-scale instances. Addressing this issue, our study builds on supervised learning of optimal solutions to the downscaled instances of given large-scale CO problems. We introduce several improvements on a recent GNN model for CO to generalize on instances of a larger scale than those used in training. We also propose a two-stage primal heuristic strategy based on uncertainty-quantification to automatically configure how solution search relies on the predicted decision values. Our models can generalize on 16x upscaled instances of commonly benchmarked five CO problems. Unlike the regressive performance of existing GNN-based CO approaches as the scale of problems increases, the CO pipelines using our models offer an incremental performance improvement relative to CPLEX. The proposed uncertainty-based primal heuristics provide 6-75% better optimality gap values and 45-99% better primal gap values for the 16x upscaled instances and brings immense speedup to obtain high-quality solutions. All these gains are achieved through a computationally efficient modeling approach without sacrificing solution quality.

State Control Design of Ostensible Metzler Linear Systems with Unsigned Input Parameters

This paper deals with the design of a complete state control for input unsigned, rank deficient matrix parameters of a linear system with system dynamics defined by ostensible structures of Metzler matrices. The proposed solution is based on the principle of diagonal stabilization of positive systems and uses a stabilizing additional component over the decomposition of the Metzler matrix in solving the incomplete internal positivity of such linear system structures. The novelty of the proposed approach is the unified representation of the parametric constraints of the Metzler matrix and the structurally constrained system inputs using linear matrix inequalities, which guarantees that the closed-loop system will be asymptotically stable. Despite the complexity of the constraint conditions on this class of linear continuous systems, the design conditions are formulated using sharp linear matrix inequalities only. A detailed design process is presented using a system-linearized mathematical model to verify the superiority and practicality of the proposed method.

A hyperelastic extended Kirchhoff–Love shell model with out-of-plane normal stress: II. An isogeometric discretization method for incompressible materials

This is Part II of a multipart article on a hyperelastic extended Kirchhoff–Love shell model with out-of-plane normal stress. We introduce an isogeometric discretization method for incompressible materials and present test computations. Accounting for the out-of-plane normal stress distribution in the out-of-plane direction affects the accuracy in calculating the deformed-configuration out-of-plane position, and consequently the nonlinear response of the shell. The return is more than what we get from accounting for the out-of-plane deformation mapping. The traction acting on the shell can be specified on the upper and lower surfaces separately. With that, the model is now free from the “midsurface’ location in terms of specifying the traction. In dealing with incompressible materials, we start with an augmented formulation that includes the pressure as a Lagrange multiplier and then eliminate it by using the geometrical representation of the incompressibility constraint. The resulting model is an extended one, in the Kirchhoff–Love category in the degree-of-freedom count, and encompassing all other extensions in the isogeometric subcategory. We include ordered details as a recipe for making the implementation practical. The implementation has two components that will not be obvious but might be critical in boundary integration. The first one is related to the edge-surface moment created by the Kirchhoff–Love assumption. The second one is related to the pressure/traction integrations over all the surfaces of the finite-thickness geometry. The test computations are for dome-shaped inflation of a flat circular shell, rolling of a rectangular plate, pinching of a cylindrical shell, and uniform hydrostatic pressurization of the pinched cylindrical shell. We compute with neo-Hookean and Mooney–Rivlin material models. To understand the effect of the terms added in the extended model, we compare with models that exclude some of those terms.

Balancing Transferability and Discriminability for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) aims to leverage a sufficiently labeled source domain to classify or represent the fully unlabeled target domain with a different distribution. Generally, the existing approaches try to learn a domain-invariant representation for feature transferability and add class discriminability constraints for feature discriminability. However, the feature transferability and discriminability are usually not synchronized, and there are even some contradictions between them, which is often ignored and, thus, reduces the accuracy of recognition. In this brief, we propose a deep multirepresentations adversarial learning (DMAL) method to explore and mitigate the inconsistency between feature transferability and discriminability in UDA task. Specifically, we consider feature representation learning at both the domain level and class level and explore four types of feature representations: domain-invariant, domain-specific, class-invariant, and class-specific. The first two types indicate the transferability of features, and the last two indicate the discriminability. We develop an adversarial learning strategy between the four representations to make the feature transferability and discriminability to be gradually synchronized. A series of experimental results verify that the proposed DMAL achieves comparable and promising results on six UDA datasets.

Data-driven distributionally robust optimization approach for the coordinated dispatching of the power system considering the correlation of wind power

With the increasing penetration of large-scale wind power into the power grid, it is crucial to develop a precise model to accurately depict the stochasticity and correlation among wind farm outputs, which is highly important for ensuring the safe and efficient utilization of wind energy in grid dispatching. In this article, a data-driven distributionally robust optimization (DDRO) dispatching approach that accounts for spatial correlations among outputs from multiple wind farms is proposed. The proposed approach is applied to a source-network-load-storage grid system to ascertain unit start-stop schedules and resource allocation effectively. First, a truncated spatial correlation model is proposed, enabling a comprehensive representation of spatial correlations and output constraints between distinct wind farms. Second, the ISODATA clustering algorithm is employed to generate typical scenarios, reduce model complexity, and expedite the computation process. Third, a unit commitment model considering the demand response is constructed and solved using the DDRO approach. Finally, the proposed model is applied to the IEEE 30-bus system to test its robustness and cost-effectiveness compared to the traditional robust optimization model. Additionally, it is applied to the IEEE 118-bus system to demonstrate its scalability and stability.

MCRe: A Unified Framework for Handling Malicious Traffic With Noise Labels Based on Multidimensional Constraint Representation

MCRe: A Unified Framework for Handling Malicious Traffic With Noise Labels Based on Multidimensional Constraint Representation

Scalable enforcement of geometric non-interference constraints for gradient-based optimization

Many design optimization problems include constraints to prevent intersection of the geometric shape being optimized with other objects or with domain boundaries. When applying gradient-based optimization to such problems, the constraint function must provide an accurate representation of the domain boundary and be smooth, amenable to numerical differentiation, and fast-to-evaluate for a large number of points. We propose the use of tensor-product B-splines to construct an efficient-to-evaluate level set function that locally approximates the signed distance function for representing geometric non-interference constraints. Adapting ideas from the surface reconstruction methods, we formulate an energy minimization problem to compute the B-spline control points that define the level set function given an oriented point cloud sampled over a geometric shape. Unlike previous explicit non-interference constraint formulations, our method requires an initial setup operation, but results in a more efficient-to-evaluate and scalable representation of geometric non-interference constraints. This paper presents the results of accuracy and scaling studies performed on our formulation. We demonstrate our method by solving a medical robot design optimization problem with non-interference constraints. We achieve constraint evaluation times on the order of 10-6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{-6}$$\\end{document} seconds per point on a modern desktop workstation, and a maximum on-surface error of less than 1.0% of the minimum bounding box diagonal for all examples studied. Overall, our method provides an effective formulation for non-interference constraint enforcement with high computational efficiency for gradient-based design optimization problems whose solutions require at least hundreds of evaluations of constraints and their derivatives.

Performance gaps between energy system planning and operation: a study exploring the impacts of model fidelity and dispatch strategy

Effective integration of renewables is essential in the energy transition, which necessitates efficient management of intermittent renewable energy generation, system cost minimization and continuous balance between supply and demand. Due to the problem’s multifaceted nature, tools with a simplified representation of energy system operation are often used to ensure computational tractability, causing performance gaps between planning and operation stages. This work quantifies the gaps focusing on the impacts of model fidelity and dispatch strategy, such as the representation of physical constraints at different levels and varying forecast horizons. More specifically, a real-world case study with three energy hubs is considered. A Pareto front is first obtained considering the trade-offs between cost and carbon footprint using the Ehub tool, a state-of-the-art energy system planning tool. Following that, the cost-optimal and the emission-optimal designs are selected for evaluating the performance gaps, using the dispatch strategies obtained from the Ehub tool as the baseline. Results show that detailed considerations of physical constraints influence grid dependencies and fuel consumption but there are no significant impacts on resultant total costs. The cost increase due to detailed physical constraints is higher for the cost-optimal system than for the emission-optimal system. Moreover, limiting the forecast horizon to 24 hours has significant impacts on the emission-optimal system with an increase in total system cost by 20.3 %. In contrast, there is only a marginal increase of 0.8 % for the cost-optimal system.

Unified Implementation and Simplification for Task-Based Authorization Security in Workflows

Authorization-related security requirements are of great significance in workflow management systems. Existing studies are restricted in their scopes of research. There is no unified principle for their implementation. In this paper, we focus on the unification of authorization-related security requirements using Petri nets (PNs). These security requirements are expressed by constraints, being imposed on tasks, namely task-based security requirements (TSRs). By downgrading TSRs to a kind of authorization-conflict relationship, we provide a standardized expression for TSRs. Such standardized expression can be transformed to firing-based linear inequalities which are a more general representation of constraints. Then, we propose the firing control for the unified implementation of TSRs based on firing-based linear inequalities. In fact, firing control is enforced by structural controllers namely monitors which are structurally consistent with PNs. For the sake of conciseness, simplification techniques are provided for the monitors. Ultimately, the experiments and discussions are presented to show the performance and advantages of the proposed approach.

The psychophysics of bouncing: Perceptual constraints, physical constraints, animacy, and phenomenal causality.

In the present study we broadly explored the perception of physical and animated motion in bouncing-like scenarios through four experiments. In the first experiment, participants were asked to categorize bouncing-like displays as physical bounce, animated motion, or other. Several parameters of the animations were manipulated, that is, the simulated coefficient of restitution, the value of simulated gravitational acceleration, the motion pattern (uniform acceleration/deceleration or constant speed) and the number of bouncing cycles. In the second experiment, a variable delay at the moment of the collision between the bouncing object and the bouncing surface was introduced. Main results show that, although observers appear to have realistic representations of physical constraints like energy conservation and gravitational acceleration/deceleration, the amount of visual information available in the scene has a strong modulation effect on the extent to which they rely on these representations. A coefficient of restitution >1 was a crucial cue to animacy in displays showing three bouncing cycles, but not in displays showing one bouncing cycle. Additionally, bouncing impressions appear to be driven by perceptual constraints that are unrelated to the physical realism of the scene, like preference for simulated gravitational attraction smaller than g and perceived temporal contiguity between the different phases of bouncing. In the third experiment, the visible opaque bouncing surface was removed from the scene, and the results showed that this did not have any substantial effect on the resulting impressions of physical bounce or animated motion, suggesting that the visual system can fill-in the scene with the missing element. The fourth experiment explored visual impressions of causality in bouncing scenarios. At odds with claims of current causal perception theories, results indicate that a passive object can be perceived as the direct cause of the motion behavior of an active object.

An Innovative Approach for Removing Stripe Noise in Infrared Images.

The non-uniformity of infrared detectors' readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. Traditional denoising algorithms have limited effectiveness in maintaining effective information. This paper proposes a multi-level image decomposition method based on an improved LatLRR (MIDILatLRR). By utilizing the global low-rank structural characteristics of stripe noise, the noise and smooth information are decomposed into low-rank part images, and texture information is adaptively decomposed into several salient part images, thereby better preserving texture edge information in the image. Sparse terms are constructed according to the smoothness of the effective information in the final low-rank part of the image and the sparsity of the stripe noise direction. The modeling of stripe noise is achieved using multi-sparse constraint representation (MSCR), and the Alternating Direction Method of Multipliers (ADMM) is used for calculation. Extensive experiments demonstrated the proposed algorithm's effectiveness and compared it with state-of-the-art algorithms in subjective judgments and objective indicators. The experimental results fully demonstrate the proposed algorithm's superiority and efficacy.

Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation.

Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group comparison. To address these limitations, we propose a new approach to FBNs construction called the deep unrolling-based spatial constraint representation (DUSCR) model and integrate it with a convolutional classifier to create an end-to-end framework for ASD recognition. Specifically, the model spatial constraint representation (SCR) is solved using a proximal gradient descent algorithm, and we unroll it into deep networks using the deep unrolling algorithm. Classification is then performed using a convolutional prototype learning model. We evaluated the effectiveness of the proposed method on the ABIDE I dataset and observed a significant improvement in model performance and classification accuracy. The resting state fMRI images are preprocessed into time series data and 3D coordinates of each region of interest. The data are fed into the DUSCR model, a model for building functional brain networks using deep learning instead of traditional models, that we propose, and then the outputs are fed into the convolutional classifier with prototype learning to determine whether the patient has ASD disease.

Combining Clinical and Spatial Constraints into Temporal Planning to Personalize Physical Rehabilitation

This work investigate temporal planning to synthesize personalized physical rehabilitation programs. The first contribution of the work concerns the representation of (heterogeneous) clinical and spatial constraints into a planning framework. The second contribution is the integration of numerical and symbolic reasoning to synthesize technically valid and coherent plans with respect to different clinical objectives. The experimental section discusses the developed planner from a technical view, assessing solving and personalization capabilities, and from a clinical view, assessing the efficacy of plans on the involved patients.

Knowledge augmentation-based soft constraints for semi-supervised clustering

Cluster analysis discovers natural structures from different perspectives of data objects and has become an effective method in data mining. The emergence of semi-supervised clustering techniques has improved the performance of unsupervised clustering algorithms. Clustering with guidance information is a clustering method variant that uses pairwise constraints based on background knowledge. This method increases the interpretability of the results through a knowledge-guided perspective but simultaneously suffers from the problem of constraint conflict. This paper designs knowledge augmentation-based soft constraints as a new pairwise constraint representation and proposes a Soft Constraints Kmeans (SCop-Kmeans) method to resolve constraint conflicts. By describing constraint knowledge from multiple perspectives, the association strength of pairwise constraints is calculated to obtain the assignment basis of objects. SCop-Kmeans can solve the sample allocation conflict problem caused by the contradiction between different constraints and improve the clustering stability. Finally, experiments are performed using UCI public standard datasets. The proposed method further improves the accuracy of clustering and performs well in experiments with different numbers of constraints, which shows that the proposed method has advantages in using constraint information to guide clustering.

Development of Multibundle Virtual Ligaments to Simulate Knee Mechanics After Total Knee Arthroplasty.

Preclinical evaluation of total knee arthroplasty (TKA) components is essential to understanding their mechanical behavior and developing strategies for improving joint stability. While preclinical testing of TKA components has been useful in quantifying their effectiveness, such testing can be criticized for lacking clinical relevance, as the important contributions of surrounding soft tissues are either neglected or greatly simplified. The purpose of our study was to develop and determine if subject-specific virtual ligaments reproduce a similar behavior as native ligaments surrounding TKA joints. Six TKA knees were mounted to a motion simulator. Each was subjected to tests of anterior-posterior (AP), internal-external (IE), and varus-valgus (VV) laxity. The forces transmitted through major ligaments were measured using a sequential resection technique. By tuning the measured ligament forces and elongations to a generic nonlinear elastic ligament model, virtual ligaments were designed and used to simulate the soft tissue envelope around isolated TKA components. The average root-mean-square error (RMSE) between the laxity results of TKA joints with native versus virtual ligaments was 3.5 ± 1.8 mm during AP translation, 7.5 ± 4.2 deg during IE rotations, and 2.0 ± 1.2 deg during VV rotations. Interclass correlation coefficients (ICCs) indicated a good level of reliability for AP and IE laxity (0.85 and 0.84). To conclude, the advancement of virtual ligament envelopes as a more realistic representation of soft tissue constraint around TKA joints is a valuable approach for obtaining clinically relevant kinematics when testing TKA components on joint motion simulators.