Functional Constraints Research Articles

Cooperative Control for Stochastic Multiagent Systems With Deferred Dynamic Constraints via a Novel Universal Barrier Function Approach.

This article investigates the cooperative control problem for stochastic multiagent systems (MASs) with dynamic constraints. A new universal barrier function is proposed, which is applicable to many systems with different types of constraint functions, even unconstrained systems. Several mapping functions are constructed to constrain the state variables directly without feasibility conditions, and the tracking control is achieved for stochastic MASs with deferred full-state constraints under the backstepping framework. In order to regulate the tracking error more precisely, the funnel error transformation is improved and the deferred funnel controller is developed by introducing a preassigned finite-time function. Based on the deferred funnel controller, the tracking error can be maintained within the predetermined funnel in the preassigned time. The convergence time can be defined according to the actual requirements, and it is independent of the design controller parameters and initial conditions. Finally, some simulation results are given to demonstrate the effectiveness of the proposed control algorithm.

Alternate Forms to Lagrange–D'Alembert Principle for Treatment of Rheonomic Constraints

Abstract Lagrange–D'Alembert principle is based on the concept of the nonactual and nonmeasurable virtual displacement and the assumption that the system-constraint forces are workless. Because time is not considered in defining the virtual displacements, virtual changes in prescribed displacements that characterize rheonomic constraints, referred to as driving constraints, are zero. Consequently, Lagrange–D'Alembert principle does not account systematically, from the outset, for rheonomic constraints, which are not workless and have power associated with them. In multibody system implementations, rheonomic-constraint forces are considered as constraint forces and not as applied forces. Consequently, the statement of the virtual-work principle that virtual work of the system-inertia forces is equal to the virtual work of the system-applied forces because the virtual work of system-constraint forces is zero omits inclusion of rheonomic constraints forces. This paper discusses using alternate forms to Lagrange–D'Alembert principle to account for rheonomic constraints from the outset by using actual and measurable motion variables to replace the virtual displacements. The analysis presented in this paper, which is applicable to both holonomic and nonholonomic systems, shows that the power of the system-inertia forces is equal to the power of the system-applied forces plus the power of rheonomic-constraint forces. It is demonstrated that the existence of Lagrange multipliers is not rooted in the definition of the virtual displacement or virtual work, but in the independence of the constraint functions. It is shown that when redundant coordinates are used, the effect of the rheonomic constraints appears explicitly in the constraint equations while this effect appears as generalized inertia forces when using the independent coordinates.

Dehazing, enhancing the boundaries and corners in hazed images using optimal adaptive technique

ABSTRACT Objects or images captured from the remote area will not have good quality and will not be visible because of mist, smoke, moisture and fog. Here, we rub/remove the unwanted hazes from the image using the optimal adaptive technique. An examination of the built-in boundary constraint of the transmission function is extremely valuable to our method. This constraint is handled as an optimisation problem to predict the unknown scene transmission. In addition, transmission refining techniques are used to remedy the issue based on variable splitting methodology. With only a few broad assumptions, the proposed method may recover haze-free, high-quality images having original colours and better image information. Experiment findings are done with plenty of foggy images indicating the efficiency and effectiveness of the proposed technique by using image quality parameters.

Mathematical Modelling of Inventory Costs in Supply Chains with Horizontal Cooperation

Efficiency in the supply chain has become very important for companies in the era of increasingly fierce business competition. This has encouraged some companies to start implementing the concept of collaboration between companies or commonly called horizontal cooperation. Horizontal cooperation is important because companies need to work together to manage complex supply chains. To manage production in a collaborative supply chain requires designing a cost-effective model. The design of this model is expected to optimise inventory costs to achieve common goals in horizontal cooperation. This model is a form of linear programme consisting of objective function and constraint function. The objective function is to minimise the inventory cost in a four-tier supply chain with horizontal cooperation on post-production final storage at the production level only. The model was tested with 3 suppliers, 3 materials, 3 factories, 4 products, 3 distributors, and 3 consumers. The model with horizontal cooperation resulted in a total inventory cost of 1,349,830. This amount is 8% lower than the total inventory cost without horizontal cooperation. Thus, horizontal cooperation in the supply chain drastically reduces inventory costs throughout the supply chain.

Bi-Objective Optimization for Interval Max-Plus Linear Systems

This paper investigates the interval-valued-multi-objective-optimization problem, whose objective function is a vector-valued max-plus interval function and the constraint function is a real-affine function. The strong and weak solvabilities of the interval-valued-optimization problem are introduced, and the solvability criteria are established. A necessary and sufficient condition for the strong solvability of the multi-objective-optimization problem is provided. In particular, for the bi-objective-optimization problem, a necessary and sufficient condition of the weak solvability is provided, and all the solvable sub-problems are found out. The interval optimal solution is obtained by constructing the set of all optimal solutions of the solvable sub-problems. The optimal load distribution is used to demonstrate how the presented results work in real-life examples.

A constraint-driven conceptual design approach for product based on function-behavior-structure design process

Conceptual design is an important part of new product development (NPD) since it relies on performance, quality, and cost restrictions from users during the design process, and it has a direct impact on downstream design activities. However, in the function-behavior-structure (FBS) design model, constraints are only frequently used in the scheme evaluation or detailed optimization stage, ignoring the influence of various constraints in the function solution process, which leads to repeated revisions of design schemes. In addition, the expression of constraints in NPD is focused on the abstraction of qualitative requirements, and there is no regular classification and definition. Aiming at the above defects, a constraint-driven product conceptual design model based on FBS model is proposed. First, a constraint-driven FBS conceptual design model is proposed as an important theoretical basis for subsequent design. Second, FBS mapping is driven by constraints, and function-behavior association matrix under functional constraints and behavior-structure association matrix under behavioral constraints are established to obtain feasible principle solutions. Third, the principle solutions are combined according to the compatibility constraints, and the constraint evaluation criteria are integrated with the technology for order preference by similarity to an ideal solution (TOPSIS) to obtain the optimal conceptual scheme (CS). To aid the design process, a prototype solving CSs based on design constraints system is developed to assist designers to manage constraints and improve evaluation efficiency. Finally, a case study of the high-speed blender scheme is used to validate the proposed approach and the optimal CS driving by constraint is solved, then the compared results and prototype system showed the applicability of the proposed approach.

Regional centromere configuration in the fungal pathogens of the Pneumocystis genus.

Centromeres are constricted chromosomal regions that are essential for cell division. In eukaryotes, centromeres display a remarkable architectural and genetic diversity. The basis of centromere-accelerated evolution remains elusive. Here, we focused on Pneumocystis species, a group of mammalian-specific fungal pathogens that form a sister taxon with that of the Schizosaccharomyces pombe, an important genetic model for centromere biology research. Methods allowing reliable continuous culture of Pneumocystis species do not currently exist, precluding genetic manipulation. CENP-A, a variant of histone H3, is the epigenetic marker that defines centromeres in most eukaryotes. Using heterologous complementation, we show that the Pneumocystis CENP-A ortholog is functionally equivalent to CENP-ACnp1 of S. pombe. Using organisms from a short-term in vitro culture or infected animal models and chromatin immunoprecipitation (ChIP)-Seq, we identified CENP-A bound regions in two Pneumocystis species that diverged ~35 million years ago. Each species has a unique short regional centromere (<10 kb) flanked by heterochromatin in 16-17 monocentric chromosomes. They span active genes and lack conserved DNA sequence motifs and repeats. These features suggest an epigenetic specification of centromere function. Analysis of centromeric DNA across multiple Pneumocystis species suggests a vertical transmission at least 100 million years ago. The common ancestry of Pneumocystis and S. pombe centromeres is untraceable at the DNA level, but the overall architectural similarity could be the result of functional constraint for successful chromosomal segregation.IMPORTANCEPneumocystis species offer a suitable genetic system to study centromere evolution in pathogens because of their phylogenetic proximity with the non-pathogenic yeast S. pombe, a popular model for cell biology. We used this system to explore how centromeres have evolved after the divergence of the two clades ~ 460 million years ago. To address this question, we established a protocol combining short-term culture and ChIP-Seq to characterize centromeres in multiple Pneumocystis species. We show that Pneumocystis have short epigenetic centromeres that function differently from those in S. pombe.

Delay-dependent Lurie–Postnikov type Lyapunov–Krasovskii functionals for stability analysis of discrete-time delayed neural networks

This paper addresses the influence of time-varying delay and nonlinear activation functions with sector restrictions on the stability of discrete-time neural networks. Compared to previous works that mainly focuses on the influence of delay information, this paper devotes to activation nonlinear functions information to help compensate the analysis technique based on Lyapunov–Krasovskii functional (LKF). A class of delay-dependent Lurie–Postnikov type integral terms involving sector constraints of nonlinear activation function is proposed to complement the LKF construction. The less conservative criteria for the stability analysis of discrete-time delayed networks is given by using improved LKF. Numerical examples show that conservatism can be reduced by the delay-dependent integral terms involving nonlinear activation functions.

A Bidirectional Scoring Strategy-Based Transformation Matrix Estimation of Dynamic Factors in Environmental Sensing

Simultaneous localization and mapping (SLAM) is the technological basis of environmental sensing, and it has been widely applied to autonomous navigation. In combination with deep learning methods, dynamic SLAM algorithms have emerged to provide a certain stability and accuracy in dynamic scenes. However, the robustness and accuracy of existing dynamic SLAM algorithms are relatively low in dynamic scenes, and their performance is affected by potential dynamic objects and fast-moving dynamic objects. To solve the positioning interference caused by these dynamic objects, this study proposes a geometric constraint algorithm that utilizes a bidirectional scoring strategy for the estimation of a transformation matrix. First, a geometric constraint function is defined according to the Euclidean distance between corresponding feature points and the average distance of the corresponding edges. This function serves as the basis for determining abnormal scores for feature points. By utilizing these abnormal score values, the system can identify and eliminate highly dynamic feature points. Then, a transformation matrix estimation based on the filtered feature points is adopted to remove more outliers, and a function for evaluating the similarity of key points in two images is optimized during this process. Experiments were performed based on the TUM dynamic target dataset and Bonn RGB-D dynamic dataset, and the results showed that the added dynamic detection method effectively improved the performance compared to state of the art in highly dynamic scenarios.

Brief Communication: The Predictable Network Topology of Evolutionary Genomic Constraint.

Large-scale comparative genomics studies offer valuable resources for understanding both functional and evolutionary rate constraints. It is suggested that constraint aligns with the topology of genomic networks, increasing toward the center, with intermediate nodes combining relaxed constraint with higher contributions to the phenotype due to pleiotropy. However, this pattern has yet to be demonstrated in vertebrates. This study shows that constraint intensifies toward the network's center in placental mammals. Genes with rate changes associated with emergence of hibernation cluster mostly toward intermediate positions, with higher constraint in faster-evolving genes, which is indicative of a "sweet spot" for adaptation. If this trend holds universally, network node metrics could predict high-constraint regions even in clades lacking empirical constraint data.

Distinct functional constraints driving conservation of the cofilin N-terminal regulatory tail

Cofilin family proteins have essential roles in remodeling the cytoskeleton through filamentous actin depolymerization and severing. The short, unstructured N-terminal region of cofilin is critical for actin binding and harbors the major site of inhibitory phosphorylation. Atypically for a disordered sequence, the N-terminal region is highly conserved, but specific aspects driving this conservation are unclear. Here, we screen a library of 16,000 human cofilin N-terminal sequence variants for their capacity to support growth in S. cerevisiae in the presence or absence of the upstream regulator LIM kinase. Results from the screen and biochemical analysis of individual variants reveal distinct sequence requirements for actin binding and regulation by LIM kinase. LIM kinase recognition only partly explains sequence constraints on phosphoregulation, which are instead driven to a large extent by the capacity for phosphorylation to inactivate cofilin. We find loose sequence requirements for actin binding and phosphoinhibition, but collectively they restrict the N-terminus to sequences found in natural cofilins. Our results illustrate how a phosphorylation site can balance potentially competing sequence requirements for function and regulation.

LeCaSiM: Learning Causal Structure via Inverse of M-Matrices with Adjustable Coefficients

The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings.

Functional constraints on the number and shape of flight feathers

As a fundamental ecological aspect of most organisms, locomotor function significantly constrains morphology. At the same time, the evolution of novel locomotor abilities has produced dramatic morphological transformations, initiating some of the most significant diversifications in life history. Despite significant new fossil evidence, it remains unclear whether volant locomotion had a single or multiple origins in pennaraptoran dinosaurs and the volant abilities of individual taxa are controversial. The evolution of powered flight in modern birds involved exaptation of feathered surfaces extending off the limbs and tail yet most studies concerning flight potential in pennaraptorans do not account for the structure and morphology of the wing feathers themselves. Analysis of the number and shape of remex and rectrix feathers across a large dataset of extant birds indicates that the number of remiges and rectrices and the degree of primary vane asymmetry strongly correlate with locomotor ability revealing important functional constraints. Among these traits, phenotypic flexibility varies reflected by the different rates at which morphological changes evolve, such that some traits reflect the ancestral condition, whereas others reflect current locomotor function. While Mesozoic birds and Microraptor have remex morphologies consistent with extant volant birds, that of anchiornithines deviate significantly providing strong evidence this clade was not volant. The results of these analyses support a single origin of dinosaurian flight and indicate the early stages of feathered wing evolution are not sampled by the currently available fossil record.

Single-Pixel Infrared Hyperspectral Imaging via Physics-Guided Generative Adversarial Networks

A physics-driven generative adversarial network (GAN) was utilized to demonstrate a single-pixel hyperspectral imaging (HSI) experiment in the infrared spectrum, eliminating the need for extensive dataset training in most data-driven deep neural networks. Within the GAN framework, the physical process of single-pixel imaging (SPI) was integrated into the generator, and its estimated one-dimensional (1D) bucket signals and the actual 1D bucket signals were employed as constraints in the objective function to update the network’s parameters and optimize the generator with the assistance of the discriminator. In comparison to single-pixel infrared HSI methods based on compressive sensing and physics-driven convolution neural networks, our physics-driven GAN-based single-pixel infrared HSI exhibits superior imaging performance. It requires fewer samples and achieves higher image quality. We believe that our physics-driven network will drive practical applications in computational imaging, including various SPI-based techniques.

Network Reconfiguration Framework for CO2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms

This paper presents the development of a generic active distribution network (ADN) operation simulation framework that incorporates selected swarm optimization algorithms (SOAs) for the purpose of reducing CO2 emissions and line loss minimization through network reconfiguration (NR). The framework has been implemented in the ADN of Taipower. Network data, provided by the Distribution Mapping Management System and Distribution Dispatch Control Center (DDCC) of Taipower, were converted into an OpenDSS script to create ADN models. The SOA is integrated into the framework and utilized to determine the statuses of both four-way and two-way switches in the planning and operating stages, in accordance with the proposed multi-objective function and operational constraints. The weightings for these decisions can be customized by distribution operators to meet their specific requirements. In this paper, the weighting for line loss reduction is set to one for minimizing CO2 emissions. The numerical results demonstrate that the proposed ADN framework can recommend a feeder switching scheme to distribution operators, aiming to balance feeder loading and minimize the neutral line current. Finally, this approach leads to reduced line losses and minimizes CO2 emissions. In contrast to relying solely on historical operational experience, this generic ADN reconfiguration framework offers a systematic approach that can significantly contribute to reducing CO2 emissions and enhancing the operational efficiency of ADNs.

Optimizing generalized linear fractional program using the image space branch-reduction-bound scheme

This paper addresses solving the generalized linear fractional program problem. For this purpose, we first convert the original problem into an equivalent problem by introducing some new variables and equivalent variation. Next, by using the two-part approximation and bilinear relaxation, we construct the linear relaxation of the objective function and the constraint function, respectively. Based on the characteristics of the objective functions of the equivalence problem and the relaxation problem and the branch-and-bound structure of the algorithm, we construct some image space region reduction techniques to enhance the convergence speed of the algorithm. By combining the linear relaxation program problem and the image space region reduction techniques, we construct an image space branch-reduction-bound algorithm, prove its global convergence, and estimate its maximum number of iterations in the worst case by analysing the complexity. Finally, numerical results indicate high computational efficiency of the algorithm.

A unified model for cross-modal plasticity and skill acquisition.

Historically, cross-modal plasticity following early blindness has been largely studied in the context of visual deprivation. However, more recently, there has been a shift in focus towards understanding cross-modal plasticity from the perspective of skill acquisition: the striking plasticity observed in early blind individuals reflects the extraordinary perceptual and cognitive challenges they solve. Here, inspired by two seminal papers on skill learning (the "cortical recycling" theory) and cross-modal plasticity (the "metamodal" hypothesis) respectively, we present a unified hypothesis of cortical specialization that describes how shared functional, algorithmic, and structural constraints might mediate both types of plasticity.

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators.

This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts' uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon's theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.

Distributed Online Constrained Optimization With Feedback Delays.

We investigate multiagent distributed online constrained convex optimization problems with feedback delays, where agents make sequential decisions before being aware of the cost and constraint functions. The main purpose of the distributed online constrained convex optimization problem is to cooperatively minimize the sum of time-varying local cost functions subject to time-varying coupled inequality constraints. The feedback information of the distributed online optimization problem is revealed to agents with time delays, which is common in practice. Every node in the system can interact with neighbors through a time-varying sequence of directed communication topologies, which is uniformly strongly connected. The distributed online primal-dual bandit push-sum algorithm that generates primal and dual variables with delayed feedback is used for the presented problem. Expected regret and expected constraint violation are proposed for measuring the performance of the algorithm, and both of them are shown to be sublinear with respect to the total iteration span T in this article. In the end, the optimization problem for the power grid is simulated to justify the proposed theoretical results.

Rocket landing guidance based on second-order Picard-Chebyshev-Newton type algorithm

This paper proposes a rocket substage vertical landing guidance method based on the second-order Picard-Chebyshev-Newton type algorithm. Firstly, the continuous-time dynamic equation is discretized based on the natural second-order Picard iteration formulation and the Chebyshev polynomial. Secondly, the landing problem that considers terminal constraints is transformed into a nonlinear least-squares problem with respect to the terminal constraint function and solved with the Gauss-Newton method. In addition, the projection method is introduced to the iteration process of the Gauss-Newton method to realize the inequality constraints of the thrust. Finally, the closed-loop strategy for rocket substage vertical landing guidance is proposed and the numerical simulations of the rocket vertical landing stage are carried out. The simulation results demonstrate that compared with the sequential convex optimization algorithm, the proposed algorithm has higher computational efficiency.