Crosstalk Prediction Research Articles

Efficient Electromagnetic Compatibility Optimization Design Based on the Stochastic Collocation Method

Nowadays, in the field of electromagnetic compatibility (EMC), numerical methods such as finite element analysis are often used for simulation analysis. These numerical methods take a long time to solve some complex simulation problems, which is not conducive to the optimal design of EMC. In particular, the intelligent optimization algorithm that needs continuous iterative calculation will not be realized because of the long optimization time. This paper realizes the innovative application of the uncertainty analysis method (Stochastic Collocation Method) in EMC optimization design. Two typical EMC optimization design problems, namely, the prediction of cable crosstalk and the design of shielding performance of metal boxes, are proposed to verify the effectiveness of the optimization algorithm. Meanwhile, its performance is compared with the classical intelligent optimization algorithms such as genetic algorithms and immune algorithms.

Prediction of post-translational modification cross-talk and mutation within proteins via imbalanced learning

Post-translational modification (PTM) is crucial for various cell signaling pathways and biological processes. However, the interactions between PTMs (PTM cross-talk) or PTMs and other sites (PTM mutation) are still in there infancy, with many challenges remaining unexplored. Since PTMs play a fundamental role in cell biology, many mutations cause abnormalities in many PTM sites and affect many human diseases. In this research, we propose an algorithm to improve PTM sites and PTM sites (PTM cross-talk) prediction and to study the interaction between PTM sites and mutation sites (PTM mutation). In addition to the primary network features of three-dimensional protein structure and sequence features, the dynamic features based on ENMs (elastic network models) are also used to improve model performance. Through feature selection, we reserved 48 features and developed a predictor for predicting PTM cross-talk and mutation. According to the evaluation based on PTM cross-talk, the area under the curve of our best prediction model reaches 0.911, which exceeds the state-of-art-model PCTpred. Based on the evaluation of PTM mutation, our prediction model is highly reliable, with an AUC score of 0.935. Even with the removal of the distance, the performance of our model (which is the most excellent model of the nine models, Random Forest model) is relatively stable.

59‐2: Invited Paper: High‐Color‐Gamut Organic Light‐Emitting Diode Microdisplay for Augmented‐Reality/Virtual‐Reality Devices

In this paper, we developed high color gamut OLED microdisplay. To minimize the crosstalk, a single stack white OLED structure with RGB main peaks was adopted, and RGB color filters were directly patterned on the OLED. In addition, we designed the optical crosstalk prediction model, which could predict the light loss according to the thickness of the passivation layer. As a result, we achieved high color gamut of 125.0% at sRGB, which demonstrated the high color gamut OLED microdisplay.

Prediction of overshoot and crosstalk of low‐voltage GaN HEMT using analytical model

Ultra-fast switching speed and low switching loss of the gallium nitride high electron mobility transistors enable the realisation of high power density converter with excellent conversion efficiency. However, the rapid switching transition leads to significant overshoot and crosstalk issues that can degrade the performance of the devices. To facilitate the evaluation of these effects on low-voltage gallium nitride devices, this paper develops an analytical model to predict overshoot and crosstalk during switching transitions accurately and efficiently. The model is constructed based on the detailed circuit deduction of various stages of the device's switching process. It also considers the voltage-dependent junction capacitances as well as the forward and the reverse transconductances. The simulated results obtained from the model are validated experimentally. With the model, the impacts of parasitic elements, especially the power loop inductance, on voltage/current overshoots and spurious voltage due to crosstalk can be easily evaluated, which provides valuable design guidelines for power conversion applications using low-voltage gallium nitride high electron mobility transistor.

Crosstalk prediction and statistical properties of transmission line

In this paper, for the crosstalk error problem of multi-core stranded wires caused by non-uniform pitch, the Monte Carlo method is used to establish the probability distribution model of non-uniform pitch of stranded wires. For stranded wire crosstalk prediction, a transmission line pa-rameter prediction model is constructed using the BSO-BP neural network algorithm, and the effec-tiveness of the algorithm in this paper is verified by simulation. The final calculation gives the statis-tical characteristics of crosstalk on multicore stranded wires.

PTM-X: Prediction of Post-Translational Modification Crosstalk Within and Across Proteins.

Posttranslational modifications (PTMs), which are processes of adding covalent groups in protein amino acids after the translation, play an important role in regulating proteins' localization, degradation, and functions. Different PTMs both within a single protein and across multiple proteins can work together or regulate reciprocally, known as PTM cross talk. However, high-throughput experimental identifications of PTM cross talk are lack due to technical limitations. In this chapter, we review in silico prediction approaches and illustrate the usage of PTM-X, a suite of recently proposed machine learning methods to predict both intra- and interprotein PTM cross talk.

Crosstalk Prediction Study of Power Secondary Cables

Crosstalk Prediction Study of Power Secondary Cables

Crosstalk Prediction Study of Power Secondary Cables

Crosstalk Prediction Study of Power Secondary Cables

Crosstalk Prediction of Handmade Cable Bundles for New Energy Vehicles

This paper presents an effective solution for the crosstalk prediction of hand made cable bundles. The outer- and inner- layer topology of the cross section are analyzed respectively, combined with the actual physical model of the cable bundles. The cascading method is used to deal with the relationship between the structure of cable bundles and the distributed per unit length (p.u.l.) parameter matrices. The random exchange of the wires in the cable bundles is equivalent to the row-column transformation of the p.u.l. parameter matrix, and the values of the p.u.l. parameter matrix after the transformation are modified by equal interval rotation degree. Then, the unconditionally stable finite difference time domain (FDTD) method is used to solve the crosstalk. The verification analysis shows that the change of the element value of the p.u.l. parameter matrix caused by the rotation of the cross section relative to the ground can not be ignored. The accuracy of the proposed method is evaluated through comparison to the probability method for a seven-core hand made cable bundles, especially in the high frequency.

Crosstalk Prediction between Single Wire and Non-uniform Twisted-wire Pair

ABSTRACT Aiming at the crosstalk error of twisted-wire pair (TWP) caused by nonuniform pitch, this article innovatively uses Monte Carlo (MC) method to establish the probability distribution model of TWP with nonuniform pitch, and genetic algorithm-back propagation neural network is used to extract the unit length parameter of the transmission line. The chain parameter method is used to solve the multi-conductor transmission line equation based on the idea of cascade. Finally, the CST simulation results show that near-end and far-end crosstalk can be predicted effectively. In TWP modeling process, according to the probability distribution existing in the principle of stranding, the MC method is used to correct the probability distribution of the nonuniform pitch on TWP into a normal distribution.

Prediction and Suppression of Twisted-wire Pair Crosstalk Based on Beetle Swarm Optimization Algorithm

Based on the theory of multi-conductor transmission lines (MTL), this paper proposes a new method for predicting and suppressing crosstalk of twisted-wire pair (TWP). The per unit length (p.u.l) RLCG parameters change caused by the inconsistent cross-sectional shape of TWP, changes in parameters make it difficult to solve the telegraph equation. In this paper, the method of transmission lines cascade is used. TWP is divided into several segments, and p.u.l parameters of each segment are predicted. Compared with before method, we propose a higher precision algorithm—beetle swarm optimization (BSO) to optimize the weights of back-propagation (BP) neural network, which predict p.u.l parameters at each segment. On this basis, it is divided into two steps: 1) Use MTL frequency domain method combined with lines’ terminal conditions to solve crosstalk and compare with CST simulation results; 2) Use the singular value decomposition (SVD) method to add matrix modules at both ends of lines for suppressing crosstalk. The results show that proposed method in this paper is consistent with the simulation, and the accuracy is higher than before

A New Method for Crosstalk Prediction Between Triple-twisted Strand (Uniform and Non-uniform) and Signal Wire based on CDBAS-BPNN Algorithm

This paper proposes a novel crosstalk prediction method between the triple-twisted strand (uniform and non-uniform) and the signal wire, that is, using back-propagation neural network optimized by the beetle antennae search algorithm based on chaotic disturbance mechanism (CDBAS-BPNN) to extract the per unit length (p.u.l) parameter matrix, and combined with the chain parameter method to obtain crosstalk. Firstly, the geometric model and cross-sectional model between the uniform triple-twisted strand and the signal wire are established, and the corresponding model between the non-uniform triple-twisted strand and the signal wire is obtained by the Monte Carlo (MC) method. Then, the beetle antennae search algorithm based on chaotic disturbance mechanism (CDBAS) and backpropagation neural network (BPNN) are combined to construct a new extraction network of the p.u.l parameter matrix, and the chain parameter method is combined to predict crosstalk. Finally, in the verification and analysis part of the numerical experiments, comparing the crosstalk results of CDBAS-BPNN, BAS-BPNN and Transmission Line Matrix (TLM) algorithms, it is verified that the proposed method has better accuracy for the prediction of the model.

Crosstalk Prediction in Twisted-Wire Pairs Based on Beetle Swarm Optimization Algorithm

In this paper, a new algorithm for predicting per unit length (p.u.l) parasitic parameters of transmission line is proposed. In the twisted-wire pair (TWP), different rotation degrees correspond to different parasitic parameters, which brings difficulties to the solution of telegraph equation. At present, the mainstream method is to divide TWP and use the cascade theory to solve each segment as a parallel transmission line. Therefore, we propose to use the beetle swarm optimization (BSO) algorithm to optimize the weights of the back propagation neural network, use a certain sample to train the network. This algorithm is used to predict the p.u.l parameters of uniform and non-uniform TWP, and the finite-difference time-domain (FDTD) method is used to solve telegraph equation. Among them, the crosstalk value of the uniform TWP is compared with the simulation value in the CST to verify the effectiveness of the proposed algorithm; the non-uniform TWP performs 500 random generation calculations to give the maximum envelope value of the crosstalk.

Measurement and simulation of steered acoustic fields generated by a multielement array for therapeutic ultrasound.

Modelling of fields generated by therapeutic ultrasound arrays can be prone to errors arising from differences from nominal transducer parameters, and variations in relative outputs of array elements when driven under different conditions, especially when simulating steered fields. Here, the effect of element size, element positions, relative source pressure variations, and electrical crosstalk on the accuracy of modelling pressure fields generated by a 555 kHz 32-element ultrasonic array were investigated. For this transducer, errors in pressure amplitude and focal position were respectively reduced from 20% to 4% and 3.3 mm to 1.5 mm using crosstalk prediction, and experimentally determined positions.

Overview on routing and resource allocation based machine learning in optical networks

For optical networks, routing and resource allocation which considerably determines the resource efficiency and network capacity is one of the most important works. It has been widely studied and many excellent algorithms have been developed. However, theoretical analysis shows that routing and resource allocation belongs to the Nondeterministic Polynomial Complete (NP-C) problem no matter in wavelength division multiplexing (WDM) optical networks, elastic optical networks (EONs), or space division multiplexing (SDM) optical networks. At presents, there doesn't exist a polynomial-time algorithm for routing and resource allocation. In recent years, machine learning which shows great advantages in solving complex problems has been widely concerned and researched. Using machine learning to conduct routing and resource allocation has aroused a great interest of researchers. This paper provides an overview on routing and resource allocation based on machine learning in optical networks. At first, we briefly introduce the routing and wavelength allocation (RWA) problem in WDM optical networks, the routing and spectrum allocation (RSA) problem in EONs, and the routing, core, spectrum allocation (RCSA) problem in SDM optical networks respectively. Commonly used machine learning techniques in optical networks are briefly elaborated. Then, the problems of quality of transmission (QoT) estimation, traffic estimation, and crosstalk prediction which can help to routing and resource allocation are also elaborated. The machine learning enabled RWA algorithms, RSA algorithms, and RCSA algorithms are elaborated, analyzed and compared in detail. In addition, the applications of machine learning in the QoT estimation, traffic estimation, and crosstalk prediction, etc., are also elaborated. Based on the existing research results, we present future research directions about how to use machine learning techniques to conduct routing and resource allocation in multidimensional time–space-frequency optical networks and satellite optical networks.

A Novel Crosstalk Estimation Method for Twist Non-Uniformity in Twisted-Wire Pairs

Based on the research of Monte Carlo (MC) method and adaptive beetle antennae search (ABAS) algorithm, a new crosstalk estimation method for non-uniform pitch twisted pair is proposed in this paper. First, the model of non-uniform pitch twisted pair is established based on the principle of twisted pair production. Then, the MC method and ABAS-BPNN (back propagation neural network) algorithm are used to construct a parasitic parameter mean extraction network for non-uniform pitch twisted pairs. Finally, the network is combined with the finite difference time domain (FDTD) algorithm to predict crosstalk. In the verification and analysis part of the numerical experiments, on the one hand, the ABAS-BPNN algorithm model is compared with the basic BAS-BPNN algorithm model, the BPNN algorithm model and the GA (genetic algorithm) -BPNN algorithm model, verifying the accuracy and efficiency of the improved BAS-BPNN algorithm. On the other hand, the validity and applicability of the proposed method in crosstalk prediction for non-uniform pitch twisted pair are verified by comparison with the results of the transmission line matrix (TLM) algorithm.

Analysis of the Influence of the Structural Parameters of Aircraft Braided-Shield Cable on Shielding Effectiveness

As the more-electric aircraft concept is accepted as the key feature of the next-generation civil aircraft, the onboard electromagnetic environment is getting much worse than traditional models, and consequently the interconnecting cables as the major coupling path of the electromagnetic interference (EMI) are required to provide more sufficient EMI protection ability. Shielded cables have been considered as reliable carriers and widely used in the aircraft for years, but any differences in shielding structure or the number of shielding layers may lead to a significant change in shielding performance. Based on the transmission line theory, this paper builds the crosstalk prediction model for two different shielded cables first, and compare the calculated crosstalk with simulation result to verify the reliability and the effectiveness of the prediction model. Furthermore, the impact of the structural parameters of the cable shield on the shielding performance is studied, such as the number of shielding layer, the thickness of the shielding layer and insulating layer.

Physically Based Modeling of Hand-Assembled Wire Bundles for Accurate EMC Prediction

In this paper, a new modeling approach to generate wire bundles with geometry accurately mimicking the random displacements of the wires in real, hand-assembled bundles is proposed. To this end, the wire trajectories are modeled by three-dimensional (3-D) curves that retain continuity of the wire path and its first derivative, allow enforcing random fluctuations of wire position in the bundle cross section and controlling bundle density. An iterative algorithm involving both local and global perturbations of initially generated trajectories is used to prevent wire overlapping. As a whole, the proposed modeling approach is able to reproduce (through the use of a limited number of parameters) the main physical properties of real hand-assembled wire bundles. In order to get either deterministic or statistical estimates of the electromagnetic compatibility performance, the obtained bundle geometry can be easily imported into 3-D electromagnetic solvers or modeled as a multiconductor transmission line by approximating the nonuniform wire paths as a sequence of uniform cascaded sections. Application examples aimed at the prediction of crosstalk and field-to-wire coupling are used to prove the importance of accurate modeling of the bundle geometry and proper digitization of the bundle along its length for prediction at high frequencies of the electromagnetic noise induced in the terminal units.

Crosstalk modeling in high-speed transmission lines by multilayer perceptron neural networks

Signal degradation due to crosstalk-related issues has become increasingly important particularly in high-speed signal transmissions. Conventional analysis of crosstalk requires a full electromagnetic modeling of the signal transmission path along with a time-domain transient simulation which is computationally demanding. In this work, we apply a multilayer perceptron neural network for crosstalk prediction in coupled transmission lines. The well-trained neural networks can be used to predict the time-domain crosstalk directly, thereby replacing complex circuit simulations. Numerical results show a high degree of generalization of the neural networks, which are able to produce accurate results and can be trained to include effects such as reflections and input mismatches.

Structure-based prediction of post-translational modification cross-talk within proteins using complementary residue- and residue pair-based features.

Post-translational modification (PTM)-based regulation can be mediated not only by the modification of a single residue but also by the interplay of different modifications. Accurate prediction of PTM cross-talk is a highly challenging issue and is in its infant stage. Especially, less attention has been paid to the structural preferences (except intrinsic disorder and spatial proximity) of cross-talk pairs and the characteristics of individual residues involved in cross-talk, which may restrict the improvement of the prediction accuracy. Here we report a structure-based algorithm called PCTpred to improve the PTM cross-talk prediction. The comprehensive residue- and residue pair-based features were designed for paired PTM sites at the sequence and structural levels. Through feature selection, we reserved 23 newly introduced descriptors and 3 traditional descriptors to develop a sequence-based predictor PCTseq and a structure-based predictor PCTstr, both of which were integrated to construct our final prediction model. According to pair- and protein-based evaluations, PCTpred yielded area under the curve values of approximately 0.9 and 0.8, respectively. Even when removing the distance preference of samples or using the input of modeled structures, our prediction performance was maintained or moderately reduced. PCTpred displayed stable and reliable improvements over the state-of-the-art methods based on various evaluations. The source code and data set are freely available at https://github.com/Liulab-HZAU/PCTpred or http://liulab.hzau.edu.cn/PCTpred/.