Flexibility can Hurt Dynamic Matching System Performance

A dynamic bipartite matching model is given by a bipartite matching graph which determines the possible matchings between the various types of supply and demand items. Both supply and demand items arrive to the system according to a stochastic process. Matched pairs leave the system and the others wait in the queues, which induces a holding cost. We model this problem as a Markov Decision Process and study the discounted cost and the average cost case. We first consider a model with two types of supply and two types of demand items with an N-shaped matching graph. For linear cost function, we prove that an optimal matching policy gives priority to the end edges of the matching graph and is of threshold type for the diagonal edge. In addition, for the average cost problem, we compute the optimal threshold value. According to our numerical experiments, threshold-type policies perform also very well for more general bipartite graphs.

Matching two natural language sentences is a fundamental problem in both natural language processing and information retrieval. Preliminary studies have shown that the syntactic structures help improve the matching accuracy, and different syntactic structures in natural language are complementary to sentence semantic understanding. Ideally, a matching model would leverage all syntactic information. Existing models, however, are only able to combine limited (usually one) types of syntactic information due to the complex and heterogeneous nature of the syntactic information. To deal with the problem, we propose a novel matching model, which formulates sentence matching as a representation learning task on a syntactic-informed heterogeneous graph. The model, referred to as SIGN (Syntactic-Informed Graph Network), first constructs a heterogeneous matching graph based on the multiple syntactic structures of two input sentences. Then the graph attention network algorithm is applied to the matching graph to learn the high-level representations of the nodes. With the help of the graph learning framework, the multiple syntactic structures, as well as the word semantics, can be represented and interacted in the matching graph and therefore collectively enhance the matching accuracy. We conducted comprehensive experiments on three public datasets. The results demonstrate that SIGN outperforms the state of the art and also can discriminate the sentences in an interpretable way.

The variable stator vane (VSV) system is a key component of gas turbine supercharged rotor components for anti‐surge, and its performance directly affects the working stability and economy of the gas turbine. The effect of the VSV system on the dynamic characteristics and control performance of the gas turbine cannot be known. Then a refined model of the VSV system refined to design parameters established and studied to explore the effect of gas turbine dynamic performance based on co‐simulation. The effects of key parameters on the performance of the gas turbine system are analyzed by embedding the refined VSV model into the engine model. The simulation results prove that servo amplifier gain, viscous friction coefficient and dead band are sensitive structural parameters that affect the dynamic performance of the gas turbines. In the proposed appropriate interval, the optimal sensitive parameters can reduce the overshoot and adjustment time by about 51% and 7%. The gain of the servo amplifier is positively correlated with the maximum opening of the VSV. When the dynamic performance of the control system has been exerted to its extreme, some nonlinear parameters of the actuator can greatly improve the overall performance of the complex system.

We introduce and study a new model that we call the matching model. Items arrive one by one in a buffer and depart from it as soon as possible but by pairs. The items of a departing pair are said to be matched. There is a finite set of classes 𝒱 for the items, and the allowed matchings depend on the classes, according to a matching graph on 𝒱. Upon arrival, an item may find several possible matches in the buffer. This indeterminacy is resolved by a matching policy. When the sequence of classes of the arriving items is independent and identically distributed, the sequence of buffer-content is a Markov chain, whose stability is investigated. In particular, we prove that the model may be stable if and only if the matching graph is nonbipartite.

Maximum bipartite matching (MBM) is a fundamental problem in combinatorial optimization with a long and rich history. A classic result of Hopcroft and Karp (1973) provides an O(m √n)-time algorithm for the problem, where n and m are the number of vertices and edges in the input graph, respectively. For dense graphs, an approach based on fast matrix multiplication achieves a running time of O(n2.371). For several decades, these results represented state-of-the-art algorithms, until, in 2013, Madry introduced a powerful new approach for solving MBM using continuous optimization techniques. This line of research, that builds on continuous techniques based on interior-point methods, led to several spectacular results, culminating in a breakthrough m1+o(1)-time algorithm for min-cost flow, that implies an m1+o(1)-time algorithm for MBM as well. These striking advances naturally raise the question of whether combinatorial algorithms can match the performance of the algorithms that are based on continuous techniques for MBM. One reason to explore combinatorial algorithms is that they are often more transparent than their continuous counterparts, and that the tools and techniques developed for such algorithms may be useful in other settings, including, for example, developing faster algorithms for maximum matching in general graphs. A recent work of Chuzhoy and Khanna (2024) made progress on this question by giving a combinatorial Õ(m1/3n5/3)-time algorithm for MBM, thus outperforming both the Hopcroft-Karp algorithm and matrix multiplication based approaches, on sufficiently dense graphs. Still, a large gap remains between the running time of their algorithm and the almost linear-time achievable by algorithms based on continuous techniques. In this work, we take another step towards narrowing this gap, and present a randomized n2+o(1)-time combinatorial algorithm for MBM. Thus in dense graphs, our algorithm essentially matches the performance of algorithms that are based on continuous methods. Similar to the classical algorithms for MBM and the approach used in the work of Chuzhoy and Khanna (2024), our algorithm is based on iterative augmentation of a current matching using augmenting paths in the corresponding (directed) residual flow network. Our main contribution is a recursive algorithm that exploits the special structure of the resulting flow problem to recover an Ω(1/log2 n)-fraction of the remaining augmentations in n2+o(1) time. Finally, we obtain a randomized n2+o(1)-time algorithm for maximum vertex-capacitated s-t flow in directed graphs when all vertex capacities are identical, using a standard reduction from this problem to MBM.

This paper introduces the direct-drive volumetric control (DDVC), focusing on the direct-drive pump-controlled hydraulic motor electro-hydraulic system as the primary subject of research. Aiming to address the problem of parameter uncertainties and unknown nonlinear disturbances in the system, a robust backstepping controller based on the extended state observer (ESO) is designed to improve the system's dynamic performance and robustness. In the controller, the ESO is used to estimate the total uncertainties in the system. Then, the robust backstepping controller is designed to enhance both the dynamic and robust performance. Moreover, Lyapunov theory is utilised to rigorously demonstrate the stability of both the ESO and the closed-loop control system. Finally, the results indicate the designed controller has excellent robustness and dynamic performance.

Frequent subgraph mining refers usually to graph matching and it is widely used when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structural semantic graph matching to discover a set of frequent subgraphs. It uses both similarity measures. An approximate structural similarity function based on graph edit distance function and a semantic vertices similarity function based on possibilistic information affinity function. Both structural and semantic filters contribute together to prune extracted frequent sets. Indeed, new hybrid structural-semantic frequent subgraph mining approach will be suitable to be applied to several applications such as community detection and social network analysis.

The dynamic performance of electric power systems during disturbances is a function of many factors. The effect of these factors vary with the nature and configuration of the electrical system and is complicated due to the interrelated, and sometimes compounding effect of the automatic control equipment which makes up a system. The post-event evaluation of a dynamic system condition requires automatic and instantaneous recording of many system quantities which will show initial conditions, the transient period, the dynamic period, and the steady state or final result of all contributing factors. The instrumentation required to monitor the influence of various system quantities must provide precise records even though separated by great distances and must continue to perform accurately regardless of wide variations in voltage, current or frequency.

In this paper, a novel dynamic wireless power transfer system is designed for roadway EV powering application. Multiple LCL resonators are parallel connected in a high-power inverter and are turned on/off through vehicle positioning result. Compared to the conventional long-rail manner, firstly, the miniaturized transmitter can greatly reduce the electromagnetic radiation. Secondly, construction costs are significantly reduced due to both the transmitter and receiver do not require ferrite. Finally, the generalized state-space averaging and small signal model are built to analyze both the steady-state and transient response process. Experimental results validate the proposed dynamic model and system performance.

Educational standards alignment, which matches similar or equivalent concepts of educational standards, is a necessary task for educational resource discovery and retrieval. Automated or semi-automated alignment systems for educational standards have been recently available. However, existing systems frequently result in inconsistency in interpreting a correct alignment or give only a "yes" or no" Boolean decision for alignment. In this research, we present a novel semi-automatic alignment method for math educational standards that goes beyond simple Boolean decision making. Our approach gives seven different degrees of alignments: Strongly Fully-aligned (SFA), Weakly Fully-aligned (WFA), Partially-aligned*** (PA***), Partially-aligned** (PA**), Partially-aligned* (PA*), Poorly-aligned (PR), and Not-aligned (NA). We aim to clarify and extend the notion of alignment for math educational standards, and to broaden categories of resource discovery and retrieval. First, we propose the MP (Materialization Pattern) model for representing the semantics of math educational standards for the purpose of aligning these standards. The MP model captures the semantics of English sentences used in math educational standards based on the Reed-Kellogg sentence diagram. We develop a semi-automatic tool, MPViz, for creating the MP model using the UML notation. The MPViz also converts an MP diagram to two graphs-a verb-phrase graph and a noun-phrase graph-which facilitate the process of automatic alignments. We align math educational standard statements using graph matching with the Bloomtaxonomy, the WordNet, and taxonomies of math concepts. We also develop a semiautomatic tool, MPComp, for aligning math educational standards. This dissertation describes a novel semi-automatic alignment method that utilizes the MP modeling and graph matching. Our experiments show that our alignment method provides the result that is comparable to human judgment. The contributions of our alignment method are as follows: 1) We propose the MP model that can explicitly model the semantics of English sentence structures used in math educational standards; 2) Using the MP model we develop a semi-automatic alignment method that produces seven different degrees of alignments, instead of simple Boolean decisions in existing alignment systems; 3) The multiple degrees of alignments empower education professionals by broadening categories of search or retrieval for educational resources.

We consider a stochastic matching model with a general compatibility graph, as introduced by Mairesse and Moyal (2016). We show that the natural necessary condition of stability of the system is also sufficient for the natural ‘first-come, first-matched’ matching policy. To do so, we derive the stationary distribution under a remarkable product form, by using an original dynamic reversibility property related to that of Adan, Bušić, Mairesse, and Weiss (2018) for the bipartite matching model.

In this paper, the dynamic performance of an interconnected two-area non-reheated thermal power system with inherent nonlinearity due to generation rate constraint (GRC) present in the turbine has been investigated using a robust fractional order controller. The controller parameters have been optimized with the help of a recently proposed Salp Swarm Algorithm (SSA) based on the social behavior of Salpidae found in the deep ocean. Extensive simulation studies carried out in this paper establish that SSA optimized controllers provide significant improvement in the dynamic performance of the system even under parametric variations in comparison to genetic algorithm and bacterial foraging optimization algorithm optimized controller.

H∞ control can guarantee the performance of a nominal ICPT system. However, the dynamic performance may suffer degradation when the parameter deviates from its nominal value. Thus, to enhance the overall dynamic performance of the ICPT system under parameter perturbations, an H∞ control method with a selected weighting function considering parameter perturbations is proposed in this article. First, a generalized state-space model is established to analyze the influence of parameter perturbations on the open-loop system in detail. After that, an uncertain model is established via frequency-domain analysis, generating a numerical solution of a weighting function used to describe the parameter perturbations. Then, the generalized plant with uncertainty is obtained and transferred into a standard H∞ optimal problem via small gain theory. Based on the proposed controller, the dynamic performance and robustness of the closed-loop system are analyzed and verified theoretically. Finally, simulation and experimental results further verify that the proposed H∞ control method can make the closed-loop system obtain good dynamic performance and strong robustness against parameter perturbations. The settling time is less than 6.8 ms without any overshoot when the system suffers parameter perturbations. Besides, the controller can maintain good dynamic performance in the startup and reference tracking experiments.

A dynamical systems approach to weighted graph matching

<div class="htmlview paragraph">In this investigation a robotic system's dynamic performance is optimized for high reliability under uncertainty. The dynamic capability equations allow designers to predict the dynamic performance of a robotic system for a particular configuration (i.e.,point design). While the dynamic capability equations are a powerful tool, they can not account for performance variations due to aleatory uncertainties inherent in the system. To account for the inherent aleatory uncertainties, a reliability-based design optimization (RBDO) strategy is employed to design robotic systems with robust dynamic performance. RBDO has traditionally been implemented as a nested multilevel optimization process in which reliability constraints require solution to an optimization problem (i.e., reliability analysis). In this work a robust unilevel performance measure approach(PMA) is developed for performing reliability-based design optimization which eliminates the lower level problem in RBDO. A robotic test problem is used to illustrate the efficacy of the approach.</div>