Recursive Structure Research Articles

What is the driving mechanism for the carbon emissions in the building sector? An integrated DEMATEL-ISM model

Clarifying the critical driving factors and the driving mechanism for building carbon emissions is fundamental for carbon peak and carbon neutrality prediction. This study comprehensively identifies 23 representative factors using the literature research and questionnaire survey method. Then, by coupling the decision-making trial and evaluation laboratory (DEMATEL) method and the interpretive structural model (ISM), an integrated DEMATEL-ISM model is developed. The proposed model is further employed to quantify the causal relationships among the factors and establish a multi-level recursive structure model to determine the action mechanism of those drivers. The results show that the 11 cause factors, 12 effect factors and the three most important factors including the economic development level, building end-use energy consumption and end-use energy intensity are identified based on the calculated cause and center degree. Based on the obtained hierarchy, the 23 factors are divided into six levels, among which the population factors, the economic development, the urbanization level and the share of the tertiary sector are the root factors leading to the building carbon emissions. In comparison, policy factors are the most critical inhibiting factors. This study can not only promote the theory and model development for the driving mechanism of carbon emissions, but also help decision-makers develop scientific carbon reduction strategies.

DTCC: Multi-level dilated convolution with transformer for weakly-supervised crowd counting

Crowd counting provides an important foundation for public security and urban management. Due to the existence of small targets and large density variations in crowd images, crowd counting is a challenging task. Mainstream methods usually apply convolution neural networks (CNNs) to regress a density map, which requires annotations of individual persons and counts. Weakly-supervised methods can avoid detailed labeling and only require counts as annotations of images, but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually ignored. We propose a weakly-supervised method, DTCC, which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd counting. Its main components include a recursive swin transformer and a multi-level dilated convolution regression head. The recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features, including global features. The multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction module. This module can capture both low- and high-level features simultaneously to enhance the receptive field. In addition, two regression head fusion mechanisms realize dynamic and mean fusion counting. Experiments on four well-known benchmark crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF_QNRF, and JHU-Crowd++) show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.

Robust steering control for a steer-by-wire automated guided vehicle via fixed-time adaptive recursive sliding mode

In this paper, a fixed-time adaptive recursive sliding mode (FTARSM) control scheme is addressed for a steer-by-wire (SbW) automated guided vehicle against model uncertainties and disturbances. First, based on a newly constructed faster fixed-time stable system, a fixed-time recursive sliding structure is developed to guarantee the SbW system fixed-time convergence, where the setting time is independent of initial conditions. By making appropriate initialization settings for the recursive structure, the sliding reaching phase is removed and the control robustness is improved. Then, the extreme learning machine (ELM) is incorporated into the FTARSM controller to estimate the lumped uncertainties upper bound, thus not only the requirement for prior bounds information in controller design is eliminated but also the control chattering is suppressed effectively. Rigorous Lyapunov analyses are further employed to ensure fixed-time closed-loop stability. Finally, the superior performance of the derived control law is verified by experimental results.

Acquiring recursive structures through distributional learning

ABSTRACT Languages differ regarding the depth, structure, and syntactic domains of recursive structures. Even within a single language, some structures allow infinite self-embedding while others are more restricted. For example, when expressing ownership relation, English allows infinite embedding of the prenominal genitive -s, whereas the postnominal genitive of is much more restricted. How do speakers learn which specific structures allow infinite embedding and which do not? The distributional learning proposal suggests that the recursion of a structure (e.g., X1’s-X2 ) is licensed if the X1 position and the X2 position are productively substitutable in non-recursive input. The present study tests this proposal with an artificial language learning experiment. We exposed adult participants to X1-ka-X2 strings. In the productive condition, almost all words attested in X1 position were also attested in X2 position; in the unproductive condition, only some were. We found that, as predicted, participants from the productive condition were more likely to accept unattested strings at both one- and two-embedding levels than participants from the unproductive condition. Our results suggest that speakers can use distributional information at one-embedding level to learn whether or not a structure is recursive.

Topological Recursion and Uncoupled BPS Structures II: Voros Symbols and the tau -Function

We continue our study of the correspondence between BPS structures and topological recursion in the uncoupled case, this time from the viewpoint of quantum curves. For spectral curves of hypergeometric type, we show the Borel-resummed Voros symbols of the corresponding quantum curves solve Bridgeland’s “BPS Riemann–Hilbert problem”. In particular, they satisfy the required jump property in agreement with the generalized definition of BPS indices Omega in our previous work. Furthermore, we observe the Voros coefficients define a closed one-form on the parameter space, and show that (log of) Bridgeland’s tau -function encoding the solution is none other than the corresponding potential, up to a constant. When the quantization parameter is set to a special value, this agrees with the Borel sum of the topological recursion partition function Z_textrm{TR}, up to a simple factor.

A High-Performantal and Server-Centric Based Data Center Network

With the increasing demand of data center interconnection in the network cloud era, data center networks (DCNs) play key roles in computing and communication, becoming the center of a great deal of resources and business. Therefore, how to design a DCN with good performance has been a significant issue in networks. The hypercube is a popular interconnection network topology with low diameter, symmetry and recursive structure, and scalability. As a variant of the hypercube, the crossed cube not only retains the excellent properties of the hypercube, but also performs better in terms of Hamiltonian-connectivity, embeddability, diameter, etc. In this paper, we first propose a novel and server-centric DCN, called CSDC, which is based on the crossed cube network. The performance of CSDC can be characterized by the properties of its logical structure, so we then give its logical structure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> , and determine the connectivity and edge-connectivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> . Furthermore, we explore the fault-tolerant paths and disjoint paths between any two distinct nodes in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> , and obtain the diameter of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> . Moreover, we propose a method to construct two completely independent spanning trees (CISTs) in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$C_{n}$</tex-math></inline-formula> . Extensive evaluations demonstrate that CSDC is a magnetic DCN for building large-scale data centers.

Boomerang Algorithm based on Swarm Optimization for Inverse Kinematics of 6 DOF Open Chain Manipulators

In this study, a feasible swarm intelligence algorithm is proposed that computes the inverse kinematics solution of 6 degree of freedom (DOF) industrial robot arms, which are frequently used in industrial and medical applications. The proposed algorithm is named as Boomerang algorithm due to its recursive structure. The proposed algorithm aims to reduce the computation time to feasible levels without increasing the position and orientation errors. In order to reduce the computational time in swarm optimization algorithms and increase feasibility, an alternative definition method was used instead of the DH method in defining the robot arm kinematic configuration. The effect of the proposed alternative definition method in reducing the computational time is presented through example inverse kinematic analysis. The proposed algorithm was compared with 3 different particle swarm optimization (PSO) variants that include orientation in the inverse kinematic solution of 6 DOF robot arms. Comparative simulation studies were carried out with 20 randomly selected position and orientation data from the workspaces of PUMA 560 and ABB IRB120 manipulators to measure performance of the algorithms. Using the error and computation time values obtained from the simulation results, the algorithms are compared using the Wilcoxon nonparametric statistical test. When the simulation results are analysed by considering the calculation time, positioning accuracy and solution finding rates, it is seen that the Boomerang algorithm is more feasible than the other PSO variants. Verification of the simulation results, and the physical applications were carried out with the ABB IRB120 6 DOF robot arm. Simulation studies and experimental studies showed that the proposed algorithm may be an efficient method for inverse kinematics of time-critical applications.

An anti-interference dynamic integral neural network for solving the time-varying linear matrix equation with periodic noises

In order to solve a time-varying linear matrix equation with periodic noises, an anti-interference dynamic integral neural network (AI-DINN) is proposed. Based on an indefinite unbounded vector/matrix-type error function, the proposed AI-DINN includes an integral structure, a recursive structure, and an adjustment module. It has the excellent ability to overcome the interference of periodic noises. This paper theoretically proves the convergence and robustness of the proposed AI-DINN for solving the time-varying linear matrix equation with the interference of periodic noises. Computer simulation results verify that the proposed AI-DINN method based on different activation functions can achieve convergence within limited time with the interferences of different periodic noises. In addition, the proposed AI-DINN with different activation functions has its own advantages with the interference of different types of periodic noises. Furthermore, comparative simulation experiments verify that the proposed AI-DINN has better convergence and anti-interference performance compared with state-of-the-art methods.

Detection-estimation strategies for delayed systems with unavailable packet sequence

In this paper, we study the problem of remote state estimation on networks with random delays and unavailable packet sequence due to malicious attacks. Two maximum a posteriori (MAP) schemes are proposed to detect the unavailable packet sequence. The first MAP strategy detects the packet sequence using data within a finite time horizon; the second MAP strategy detects the packet sequence by a recursive structure, which effectively reduces the computation time. With the detected packet sequence, we further design a linear minimum mean-squared error (LMMSE) estimation algorithm based on smoothing techniques, rather than using the classic prediction and update structure. A wealth of information contained in the combined measurements is utilized to improve the estimation performance. Finally, the effectiveness of the proposed algorithms is demonstrated by simulation experiments.

Abnormal Operation Detection of Access Equipment in Dispatching Exchange Network Based on Data Mining

To meet the requirements of a power dispatching service, a dispatching switching network has been widely used instead of circuit switching technology. However, with the growth of the number of access devices, the problem of network security is becoming more and more serious. At present, the commonly used access equipment abnormal operation detection method mainly relies on a two-stage clustering method to identify the abnormal operation state, but the processing effect is not good in the face of a large number of data, resulting in a low F1 value of the final detection result. Therefore, aiming at the access equipment of the dispatching exchange network, an abnormal operation detection method based on data mining is proposed. Scapy, a network tool, is used to capture the traffic data of access devices, and the collected data are normalized and fused. Data feature information is extracted through time sliding window and incremental calculation mode. With the clustering algorithm and related rules in data mining as the core, the abnormal operation detection method of access equipment is designed to obtain accurate abnormal detection results. Finally, based on the recursive graph structure, the detection results are visualized. The experimental results show that the average F1 value of the detection results of the proposed method is 0. 98, which indicates that the detection results of the proposed method have higher accuracy, and based on this, the network security isolation is carried out, which effectively improves the security of dispatching switching network.

Both syntactic and pragmatic sentence adequacy matters for recursive theory of mind in 5-year-olds

Syntax comprehension, vocabulary, and executive cognitive function are important predictors of first-order theory of mind (ToM) ability. The development of second-order or recursive ToM abilities may require more advanced linguistic skills, such as the ability to use syntactically adequate sentences. However, the recursive structure of ToM may be also based on less complex but pragmatically adequate constructions. Using two novel recursive syntax production tasks, this study investigated the relation between syntactic and pragmatic adequacy of sentence productions and second-order false belief (FB) reasoning in five-year-old children (N = 142). Syntactic adequacy of sentence productions explained 6% of variance in second-order FB reasoning above and beyond grammar comprehension, vocabulary production, and working memory, while pragmatic adequacy explained an additional 4% of variance in the recursive ToM. The new recursive syntax production tasks differed with respect to how pragmatically and syntactically adequate sentences were elicited. The paper discusses the question of how to measure recursive syntax production and whether it is a necessary precondition of more complex forms of theory of mind reasoning.

Breaking the Communication-Privacy-Accuracy Trilemma

Two major challenges in distributed learning and estimation are 1) preserving the privacy of the local samples; and 2) communicating them efficiently to a central server, while achieving high accuracy for the end-to-end task. While there has been significant interest in addressing each of these challenges separately in the recent literature, treatments that simultaneously address both challenges are still largely missing. In this paper, we develop novel encoding and decoding mechanisms that simultaneously achieve optimal privacy and communication efficiency in various canonical settings. In particular, we consider the problems of mean estimation and frequency estimation under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -local differential privacy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -bit communication constraints. For mean estimation, we propose the SQKR mechanism, a scheme based on Kashin’s representation and random sampling, with order-optimal estimation error under both constraints. We further apply SQKR to distributed SGD and obtain a communication efficient and (locally) differentially private distributed SGD protocol. For frequency estimation, we present the RHR mechanism, a scheme that leverages the recursive structure of Walsh-Hadamard matrices and achieves order-optimal estimation error for all privacy levels and communication budgets. As a by-product, we also construct a distribution estimation mechanism that is rate-optimal for all privacy regimes and communication constraints, extending recent work that is limited to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b=1$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon =O(1)$ </tex-math></inline-formula> . Our results demonstrate that intelligent encoding under joint privacy and communication constraints can yield a performance that matches the optimal accuracy achievable under either constraint alone. In other words, the optimal performance is determined by the more stringent of the two constraints, and the less stringent constraint can be satisfied for free.

Recursive structures in involutive bases theory

We study characterisations of involutive bases using a recursion over the variables in the underlying polynomial ring and corresponding completion algorithms. Three key ingredients are (i) an old result by Janet recursively characterising Janet bases for which we provide a new and simpler proof, (ii) the Berkesch–Schreyer variant of Buchberger's algorithm and (iii) a tree representation of sets of terms also known as Janet trees. We start by extending Janet's result to a recursive criterion for minimal Janet bases leading to an algorithm to minimise any given Janet basis. We then extend Janet's result also to Janet-like bases as introduced by Gerdt and Blinkov. Next, we design a novel recursive completion algorithm for Janet bases. We study then the extension of these results to Pommaret bases. It yields a novel recursive characterisation of quasi-stability which we use for deterministically constructing “good” coordinates more efficiently than in previous works. A small modification leads to a novel deterministic algorithm for putting an ideal into Nœther position. Finally, we provide a general theory of involutive-like bases with special emphasis on Pommaret-like bases and study the syzygy theory of Janet-like and Pommaret-like bases.

Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

Based on the artificial intelligence algorithm of RetinaNet, we propose the Ghost-RetinaNet in this paper, a fast shadow detection method for photovoltaic panels, to solve the problems of extreme target density, large overlap, high cost and poor real-time performance in photovoltaic panel shadow detection. Firstly, the Ghost CSP module based on Cross Stage Partial (CSP) is adopted in feature extraction network to improve the accuracy and detection speed. Based on extracted features, recursive feature fusion structure is mentioned to enhance the feature information of all objects. We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression, respectively. Finally, in order to achieve fast detection, the Ghost strategy is chosen to lighten the size of the algorithm. The results of the experiment show that the average detection accuracy (mAP) of the algorithm can reach up to 97.17%, the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second (FPS), which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment. The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.

Finding Hierarchical Structure in Binary Sequences: Evidence from Lindenmayer Grammar Learning.

In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. To this end, we implemented a sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling for the use of low-level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants' pattern of anticipation could not be accounted for by "flat" statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions.

Pansharpening Using Unsupervised Generative Adversarial Networks With Recursive Mixed-Scale Feature Fusion

Panchromatic sharpening (pansharpening) is an important technology for improving the spatial resolution of multispectral (MS) images. The majority of the models are implemented at the reduced resolution, leading to unfavorable results at the full resolution. Moreover, the complicated relationship between MS and panchromatic (PAN) images is often ignored in detail injection. For the mentioned problems, unsupervised generative adversarial networks with recursive mixed-scale feature fusion for pansharpening (RMFF-UPGAN) are modeled to boost the spatial resolution and preserve the spectral information. RMFF-UPGAN comprises a generator and two U-shaped discriminators. A dual-stream trapezoidal branch is designed in the generator to obtain multiscale information. Further, a recursive mixed-scale feature fusion subnetwork is designed. Perform a prior fusion on the extracted MS and PAN features of the same scale. A mixed-scale fusion is conducted on the prior fusion results of the fine-scale and coarse-scale. The fusion is executed sequentially in the above manner building a recursive mixed-scale fusion structure and finally generating key information. A compensation information mechanism is also designed for the reconstruction of key information to compensate for information. A nonlinear rectification block for the reconstructed information is developed to overcome the distortion induced by neglecting the complicated relationship between MS and PAN images. Two U-shaped discriminators are designed and a new composite loss function is defined. The presented model is validated using two satellite data and the outcomes reveal better than the prevalent approaches regarding both visual assessment and objective indicators.

Speed up of AMG-CG by recursive structure reordering

Speed up of AMG-CG by recursive structure reordering

A Stochastic Model for an Input Control Problem in a Two-Level Supply Chain for Production-Time-Dependent Products with Random Demands

Inspired by the poultry farming process, we studied an input control problem in a two-level supply chain for production-time-dependent products with random demands. Poultry farms deliver chicks in batches, and the raising process begins at timed intervals. Chicks become broilers after a predetermined raising time. The broilers in the process are shipped to manufacturing plants to satisfy the demand. The remaining chicks grow to the next product size to satisfy the demands for larger chickens. This procedure is repeated until the chicks are fully grown. After the chicks are grown to satisfy the demand for the largest size, the remaining chicks are discarded. In this paper, a stochastic model is presented to study an input control problem in the poultry farming process. Because of the production density, feed, and temperature control, one important issue in the operation of a poultry company is the determination of the raising interval and quantity of input (chicks). While existing mathematical models can provide effective information on the production-planning problem of systems, research has not been conducted on cases of random demand. Identifying a recursive structure and Markovian property for the number of raw materials (chicks) and the unfulfilled demand for each product type in the system, we demonstrate that embedded Markov chain models can be obtained. The equilibrium probabilities of the models can be calculated using matrix analytic methods or probability generating functions. Various numerical experiments are conducted to analyze how performance measures such as amount of disposal, unsatisfied demands, and total cost (considering disposal cost and opportunity cost) change with system parameters.

Adaptive Dynamic Programming for Networked Control Systems Under Communication Constraints: A Survey of Trends and Techniques

Survey/review study Adaptive Dynamic Programming for Networked Control Systems under Communication Constraints: A Survey of Trends and Techniques Xueli Wang 1, Ying Sun 1,*, and Derui Ding 2 1 Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2 School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia * Correspondence: yingsun1991@163.com Received: 22 September 2022 Accepted: 28 November 2022 Published: 22 December 2022 Abstract: The adaptive dynamic programming (ADP) technology has been widely used benefiting from its recursive structure in forward and the prospective conception of reinforcement learning. Furthermore, ADP-based control issues with communication constraints arouse ever-increasing research consideration in theoretical analysis and engineering applications due mainly to the wide participation of digital communications in industrial systems. The latest development of ADP-based optimal control with communication constraints is systematically surveyed in this paper. To this end, the development of ADP-based dominant methods is first investigated from their structures and implementation. Then, technical challenges and corresponding approaches are comprehensively and thoroughly discussed and the existing results are reviewed according to the constraint types. Furthermore, some applications of the ADP method in practical systems are summarized. Finally, future topics are lighted on ADP-based control issues.

Simulation of a Cellular Energy System including hierarchies and neighborhoods

The massive use of small energy resources and storage units causes a transition from a traditionally centralized to a decentralized energy system. To structure and coordinate the emerging changes in the energy system, the concept of Energy Cells (ECs) was developed. Several ECs can be combined to form a hierarchically superordinate EC. These hierarchically superordinate ECs can in turn be combined and thus form a complex hierarchy. An EC encapsulates coherent parts of an energy system and can communicate as well as exchange energy with other ECs at a different or the same level. It follows the idea of local balance of energy provision and demand. A network of ECs forms a Cellular Energy System (CES). In this paper, we develop a concept for modeling and simulating a CES. We accomplish this by beginning with atomic components like consumers, producers, and storage units and aggregating them with Hierarchical Controllers (HCs). Such a hierarchically structured energy system is part of various proposals. However, we are able to add neighborhood relations by introducing Local Controllers (LCs). This is more realistic and also opens many degrees of freedom for control strategies in such a system. Following the recursive structure of the CES itself, we define recursive functions for visiting the CES architecture and realizing various control strategies. We evaluate our approach in a series of partially randomized scenarios, showing notable differences in the performance of the CES regarding different control strategies in a larger example. We also provide a theoretical analysis of the computational complexity of the suggested approach.