Ladder Network Research Articles

A note on nonlocal metric dimension of some networks

For any u and v ∈ V ( G ) , uv ∉ E ( G ) , if there exists z ∈ Z ⊆ V ( G ) such that d G ( u , z ) ≠ d G ( v , z ) , then u and v are resolved by z. A set Z make each pair of vertices of G is resolved by Z is a nonlocal resolving set of G. Nonlocal metric dimension di m nl ⁡ ( G ) is the minimum cardinality of the nonlocal resolving sets of a graph G. In this paper, we calculate the nonlocal metric dimensions of some networks, which included hexagonal ladder network, radix triangular mesh network, Sierpiński network and small-world network.

Two-strand ladder network variants: Localization, multifractality, and quantum dynamics under an Aubry-André-Harper kind of quasiperiodicity

In this paper we demonstrate, using a couple of variants of a two-strand ladder network that, a quasiperiodic Aubry-André-Harper (AAH) modulation applied to the vertical strands, mimicking a deterministic distortion in the network, can give rise to certain exotic features in the electronic spectrum of such systems. While, for the simplest ladder network all the eigenstates become localized as the modulation strength crosses a threshold, for the second variant, modeling an ultrathin graphene nano-ribbon, the central part of the energy spectrum remains populated by extended wavefunctions. The multifractal character in the energy spectrum is observed for both these networks close to the critical values of the modulation. We substantiate our findings also by studying the quantum dynamics of a wave packet on such decorated lattices. Interestingly, while the mean square displacement (MSD) changes in the usual manner in a pure two-strand ladder network as the modulation strength varies, for the ultrathin graphene nanoribbon the temporal behavior of the MSD remains unaltered only up to a strong modulation strength. This, we argue, is due to the extendedness of the wavefunction at the central part of the energy spectrum. Other measurements like the return probability, temporal autocorrelation function, the time dependence of the inverse participation ratio, and the information entropy are calculated for both networks with different modulation strengths and corroborate our analytical findings.

Long‐Infrared Broadband Polarization‐Sensitive Absorber with Metasurface Based on Ladder Network

AbstractPlasmonic resonant and metamaterial structures allow for manipulating the polarization properties of electromagnetic waves to achieve polarization beam splitting. Broadband polarization‐sensitive metamaterial absorbers based on surface plasmon polaritons have the advantages of high‐energy utilization and excellent stability, which may replace traditional polarizing devices. Here, an absorber based on a ladder network construction is designed theoretically and investigated experimentally. The absorber can possess almost 92% average absorptivity for TM polarization light in 8–12 µm range (experimental result: 83% from 7 to 11 µm). It is especially sensitive to the polarization angle of incident light, and with the increase of polarization angle, the broadband absorption response can present analogously monotonous reduction. The theoretical and experimental results for TE polarization light are 12% and 25%, respectively. The broadband absorption response performs angle insensitive property under oblique incidence. The proposed metamaterial absorber opens a path to realize broadband polarization‐sensitive absorption based on simple nanostructure. It will possess great potential applications in high‐performance polarimetric photodetectors and imaging fields.

A Novel Methodology for the Transformer Winding Equivalent Ladder Network Circuit Parameters Identification by Employing the Frequency-Domain and Population Based Method

A Novel Methodology for the Transformer Winding Equivalent Ladder Network Circuit Parameters Identification by Employing the Frequency-Domain and Population Based Method

Embedded Fluidic Sensing and Control with Soft Open‐Cell Foams

AbstractThe synthesis of soft matter intelligence with circuit‐driven logic has enabled a new class of robots that perform complex tasks or conform to specialized form factors in unique ways that cannot be realized through conventional designs. Translating this hybrid approach to fluidic systems, the present work addresses the need for sheet‐based circuit materials by leveraging the innate porosity of foam—a soft material—to develop pneumatic components that support digital logic, mixed‐signal control, and analog force sensing in wearables and soft robots. Analytical tools and experimental techniques developed in this work serve to elucidate compressible gas flow through porous sheets, and to inform the design of centimeter‐sized foam resistors with fluidic resistances on the order of 109 Pa s m−3. When embedded inside soft robots and wearables, these resistors facilitate diverse functionalities spanning both sensing and control domains, including digital logic using textile logic gates, digital‐to‐analog signal conversion using ladder networks, and analog sensing of forces up to 40 N via compression‐induced changes in resistance. By combining features of both circuit‐based and materials‐based approaches, foam‐enabled fluidic circuits serve as a useful paradigm for future hybrid robotic architectures that fully embody the sensing and computing capabilities of soft fluidic materials.

Speech emotion recognition in real static and dynamic human-robot interaction scenarios

The use of speech-based solutions is an appealing alternative to communicate in human-robot interaction (HRI). An important challenge in this area is processing distant speech which is often noisy, and affected by reverberation and time-varying acoustic channels. It is important to investigate effective speech solutions, especially in dynamic environments where the robots and the users move, changing the distance and orientation between a speaker and the microphone. This paper addresses this problem in the context of speech emotion recognition (SER), which is an important task to understand the intention of the message and the underlying mental state of the user. We propose a novel setup with a PR2 robot that moves as target speech and ambient noise are simultaneously recorded. Our study not only analyzes the detrimental effect of distance speech in this dynamic robot-user setting for speech emotion recognition but also provides solutions to attenuate its effect. We evaluate the use of two beamforming schemes to spatially filter the speech signal using either delay-and-sum (D&S) or minimum variance distortionless response (MVDR). We consider the original training speech recorded in controlled situations, and simulated conditions where the training utterances are processed to simulate the target acoustic environment. We consider the case where the robot is moving (dynamic case) and not moving (static case). For speech emotion recognition, we explore two state-of-the-art classifiers using hand-crafted features implemented with the ladder network strategy and learned features implemented with the wav2vec 2.0 feature representation. MVDR led to a signal-to-noise ratio higher than the basic D&S method. However, both approaches provided very similar average concordance correlation coefficient (CCC) improvements equal to 116 % with the HRI subsets using the ladder network trained with the original MSP-Podcast training utterances. For the wav2vec 2.0-based model, only D&S led to improvements. Surprisingly, the static and dynamic HRI testing subsets resulted in a similar average concordance correlation coefficient. Finally, simulating the acoustic environment in the training dataset provided the highest average concordance correlation coefficient scores with the HRI subsets that are just 29 % and 22 % lower than those obtained with the original training/testing utterances, with ladder network and wav2vec 2.0, respectively.

FRLN: Federated Residual Ladder Network for Data-Protected QoS Prediction

FRLN: Federated Residual Ladder Network for Data-Protected QoS Prediction

Derivation of transformer winding equivalent circuit by employing the transfer function obtained from frequency response analysis data

AbstractPrecisely interpreting frequency response analysis (FRA) curves is crucial when investigating mechanical malfunctions in power transformer windings (TWs). However, many conventional explanations of FRA features cannot be validated by an equivalent circuit (EC) that represents winding structures using resistance, inductance, and capacitance components in a ladder network. Incorrect interpretation can lead to misdiagnosis and confusion in asset management. Previous ECs are often proposed by analysing winding structures without rigorous verification compared to the corresponding FRA data. The specific EC is acquired using the transfer function (TF) derived from the measured FRA data. This allows for the establishment of the relationship between TW FRA curves, TF equations, and EC topologies. The precise interpretation of FRA curves can be achieved by closely observing the FRA curves created from TFs and ECs. The remarkable similarity between the measured and modelled FRA curves verifies the authenticity of the derived ECs. This significant achievement clarifies many misunderstandings regarding FRA and represents a substantial advancement in FRA technology.

Cauer Ladder Network With Constant Basis Functions for Eddy Current Problems Involving Conductor Movement

Cauer Ladder Network With Constant Basis Functions for Eddy Current Problems Involving Conductor Movement

Second-Order Approximation of Nonlinear Eddy-Current Problems by a Cauer Ladder Network

Second-Order Approximation of Nonlinear Eddy-Current Problems by a Cauer Ladder Network

Toward unique electrical ladder network model synthesis of a transformer winding high-frequency modeling using K-means and metaheuristic-based method

PurposeAlthough, numerous optimization algorithms have been devoted to construct an electrical ladder network model (ELNM), they suffer from some frail points such as insufficient accuracy as well as the majority of them are unconstrained, which result in optimal solutions that violate certain security operational constraints. For this purpose, this paper aims to propose a flexible-constraint coyote optimization algorithm; the novelty lies in these points: penalty function is introduced in the objective function to discard any unfeasible solution, an advanced constraint handling technique and empirical relationship between the physical estimated parameters and their natural frequencies.Design/methodology/approachFrequency response analysis (FRA) is very significant for transformer winding diagnosis. Interpreting results of a transformer winding FRA is quite challenging. This paper proposes a new methodology to synthesize a nearly unique ELNM physically and electrically coupled for power transformer winding, basing on K-means and metaheuristic algorithm. To this end, the K-means method is used to cluster the setting of control variables, including the self-mutual inductances/capacitances, and the resistances parameters. Afterward, metaheuristic algorithm is applied to determine the cluster centers with high precision and efficiency.FindingsFRA is performed on a power transformer winding model. Basing on the proposed methodology, the prior knowledge in selecting the initial guess and search space is avoided and the global solution is ensured. The performance of the abovementioned methodology is compared using evaluation expressions to verify its feasibility and accuracy.Originality/valueThe proposed method could be generalized for diagnosis of faults in power transformer winding.

A hybrid twin based on machine learning enhanced reduced order model for real-time simulation of magnetic bearings

The simulation of magnetic bearings involves highly non-linear physics, with high dependency on the input variation. Moreover, such a simulation is time consuming and can’t run, within realistic computation time for control purposes, when using classical computation methods. On the other hand, classical model reduction techniques fail to achieve the required precision within the allowed computation window. To address this complexity, this work proposes a combination of physics-based computing methods, model reduction techniques and machine learning algorithms, to tackle the requirements. The physical model used to represent the magnetic bearing is the classical Cauer Ladder Network method, while the model reduction technique is applied on the error of the physical model’s solution. Later on, in the latent space a machine learning algorithm is used to predict the evolution of the correction in the latent space. The results show an improvement of the solution without scarifying the computation time. The solution is computed in almost real-time (few milliseconds), and compared to the finite element reference solution.

Homogenization Method Based on Cauer Ladder Network Representation of Unit Cell

Homogenization Method Based on Cauer Ladder Network Representation of Unit Cell

A Semi-Supervised Learning Model for Predicting User Attributes Based on Ladder Network

A Semi-Supervised Learning Model for Predicting User Attributes Based on Ladder Network

Analytical computation of driving point impedance in mutually coupled inhomogeneous ladder networks

SummaryElectrical systems are traditionally modeled as homogeneous ladder networks, disregarding the inherent inhomogeneity found in real‐world systems. This can result in inaccuracies in analysis and design. The driving point impedance (DPI) is a crucial parameter for studying electrical systems, as it can be conveniently measured at the terminals. However, existing methods for calculating DPI have limitations, including increased complexity, high computational demands, convergence issues, and challenges in capturing high‐frequency effects. A new approach for computing DPI of inhomogeneous ladder networks, reducible to series and shunt impedance circuit form, is introduced in this research. The method is based on state‐space formulations and projective matrix analysis. It offers improved accuracy as compared with existing methods and can be applied to a wide range of networks. Simulation results from a six‐section mutually coupled inhomogeneous ladder network validate the effectiveness of the proposed approach. This research addresses the disregarded factors of inhomogeneity and mutual coupling within traditional approaches, presenting a significant advancement in the analysis and design of electrical systems.

Modelling stranded wires using homogenization and the Cauer ladder method

PurposeThe purpose of this paper is to introduce a new method to model a stranded wire efficiently in 3D finite element simulations.Design/methodology/approachIn this method, the stranded wires are numerically approximated with the Cauer ladder network (CLN) model order reduction method in 2D. This approximates the eddy current effect such as the skin and proximity effect for the whole wire. This is then projected to a mesh which does not include each strand. The 3D fields are efficiently calculated with the CLN method and are projected in the 3D geometry to be used in simulations of electrical components with a current vector potential and a homogenized conductivity at each time step.FindingsIn applications where the stranded wire geometry is known and does not change, this homogenization approach is an efficient and accurate method, which can be used with any stranded wire configuration, homogenized stranded wire mesh and any input signal dependent on time steps or frequencies.Originality/valueIn comparison to other methods, this method has no direct frequency dependency, which makes the method usable in the time domain for an arbitrary input signal. The CLN can also be used to interconnected stranded cables arbitrarily in electrical components.

Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions.

The lack of labeled data and variable working conditions brings challenges to the application of intelligent fault diagnosis. Given this, extracting labeled information and learning distribution-invariant representation provides a feasible and promising way. Enlightened by metric learning and semi-supervised architecture, a triplet-guided path-interaction ladder network (Tri-CLAN) is proposed based on the aspects of algorithm structure and feature space. An encoder-decoder structure with path interaction is built to utilize the unlabeled data with fewer parameters, and the network structure is simplified by CNN and an element additive combination activation function. Metric learning is introduced to the feature space of the established algorithm structure, which enables the mining of hard samples from extremely limited labeled data and the learning of working condition-independent representations. The generalization and applicability of Tri-CLAN are proved by experiments, and the contribution of the algorithm structure and the metric learning in the feature space are discussed.

Reduced-Order Ladder Network Modeling for Common-Mode Characterization of Transformers

The ladder network with detailed parameters and explicit physical meanings is beneficial for the wideband modeling of transformers. However, due to an excessive number of sections, the high complexity of the model limits its application in the common-mode analysis of transformers working in power converters. In order to improve the applicability, in this article, the correlation between the resonant frequency of each section pair and the validity of the model is analyzed first. A valid frequency range prediction is derived then to evaluate whether the order of the model is reasonable, which forms the foundation of the proposed reduced-order modeling method. This method enables accurate transformer modeling using a ladder network with much fewer parameters. Furthermore, the reduced-order transformer model is also applied to meet the demand in the balance winding techniques and the wideband common-mode noise analysis. Finally, the effectiveness of the proposed method and its applications are verified by the insertion voltage gain of transformers and the common-mode current of the converter.

Using MLP to locate transformer winding fault based on digital twin

There is no doubt that transformer plays a fundamental role in power system. At the same time, transformer winding fault diagnosis is an important topic. Many works put the most emphasis on the identification of fault type and degree, while ignoring the fault location. However, fault location is an urgent problem to be solved, which is worth studying and discussing. The contribution of this paper lies in the location of Disk space variation (DSV) fault. The introduction of digital twin can solve the problem of insufficient fault cases, and pave the way for the intellectualization of fault diagnosis. In this paper, the digital twin of transformer winding is established based on double ladder network, in which the distributed parameters are calculated by finite element method. Frequency response analysis (FRA) is one of the most widely accepted methods for transformer winding mechanical deformation fault diagnosis. Aiming at the interpretation code of FRA, this paper disproves the view that phase information is useful. Then, by extracting the mathematical index of FRA, multi-layer perceptron (MLP) is trained and DSV fault location is realized. In addition, the popular support vector machine is also compared with the MLP model in this paper, which further highlights the advantages of MLP. The proposed method is verified by an actual transformer, and the results are satisfactory.

Monitoring edge-geodetic numbers of convex polytopes and four networks

Foucard, Krishna and Lekshmi introduced a new graph-theoretic concept in the area of network monitoring. Let G be a graph with vertex set , there exists a subset , if deleting any edge in G, one can find that there exist at least two vertices in S whose distance is changed, then we call S is a monitoring edge-geodetic set (MEG-set for short). The minimum size of the MEG-set of G is called the monitoring edge-geodetic number of G (meg(G) for short). In this paper, we study the monitoring edge-geodetic number of some well-known networks, including Ladder, butterfly, circulant and Benes networks and convex polytopes.