Modal Selection Research Articles

“The computer gives us lots of ideas”

This article offers insights into elementary-aged students’ perspectives as they embark on composing collaborative multimodal narratives. Contributions from research literature on the writing process, conference practices, and multimodality situate the study. Analysis of students’ responses on a retrospective interview protocol that focused on students’ recollections of the experience, illuminated three findings that may be used to generate questions for effective writing conferences for multimodal compositions. Questions can be asked to support students as they navigate technology, gain insights into modal selection, and determine their collaborative approach to the composing process. Moreover, learning more about students’ decisions as they compose multimodal texts leads to a richer understanding of the affordances of multiple modes in writing and recommendations for creative effective writing conferences.

Loss-induced modal selection by a resistive wiremesh.

This work examines the impact of local losses produced by a resistive wiremesh on the modes of an acoustic cavity. In the one-dimensional case, we demonstrate the ability to selectively affect the modes, ranging from being completely unaffected by the wiremesh to being fully absorbed by it. This effect can be used to filter the cavity modes. In the two-dimensional case, higher order modes are considered and we discuss the effect of wiremesh tilt on the cavity modes. A new type of mode that is localized on the wiremesh with a purely imaginary eigenfrequency has been identified. These findings show that wiremeshes are ultrabroadband lossy metasurfaces offering a straightforward way to explore passive non-Hermitian systems.

How to design driver takeover request in real-world scenarios: A systematic review

Conditional automated driving systems have become a reality. Ensuring the safety during the driving takeover process has become a crucial concern, with the takeover request (TOR) playing an important role. Existing review studies often focus only on a particular aspects of the TOR and fail to consider the varied requirements of TOR in specific scenarios. In this review, we conducted a systematic literature search and identified 75 studies related to TOR design. Through the summarization of literature, we naturally arrived at the final research themes, which encompassed the key aspects of TOR design and specific situational factors. Our findings indicate the necessity for dynamic time budget (TB) in automated driving systems, emphasizing that longer TB contributes to improved safety. There is a need for multimodal capabilities within TOR, followed by tailored modal selection and combinations based on specific takeover scenarios. The integration of additional informative prompt in TOR is beneficial, provided that the type and content are carefully considered. Our study provides a comprehensive theoretical framework for future TOR design and suggests directions for future research efforts.

Signal Denoising of Traffic Speed Deflectometer Measurement Based on Partial Swarm Optimization-Variational Mode Decomposition Method.

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization-variational mode decomposition (PSO-VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO-VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising.

Fault diagnosis method of rolling bearing based on MSSA-VMD algorithm

Abstract Early fault signals of rolling bearings are affected by environmental noise, which makes it challenging to extract fault characteristics. In this paper, a bearing fault diagnosis method based on Sparrow Search Algorithm (SSA) and Variable Modal Decomposition (VMD) is proposed to achieve adaptive signal decomposition and feature extraction. First, the fusion shock index is constructed based on the envelope spectrum pulse factor and correlation coefficient to measure the shock fault components. Then, the VMD parameters are adaptively optimized by the improved sparrow search algorithm MSSA, and the fusion shock index is established as the optimal modal component selection method of the fitness function. Finally, the envelope spectrum of the signal after noise reduction is analyzed to extract the fault characteristic frequency. By analyzing the bearing outer ring fault signal and inner ring fault signal under strong noise, the results show that this method can adaptively determine the mode number and penalty factor of VMD, effectively extract the fault signal characteristics, and realize rolling bearing fault diagnosis.

Predicting modal choice for urban transport using an algebraic equation

Demand estimation and forecasting is an essential step in urban passenger transport planning. Relating the factors that influence the modal choice behavior of individuals facilitates demand estimation. In this study, we develop machine learning models that consider individuals' demographic, socioeconomic, and travel characteristics to justify their mode choice. Two datasets are used to train and validate the models. We use logistic regression and multilayer perceptron models to classify public or private transportation trips. It was observed that a multilayer perceptron model with a low number of parameters could predict modal selection with an accuracy exceeding 90%. We derive an algebraic equation from this result to perform modal selection prediction. Our results show that the models can effectively predict the mode of transportation of individuals based on their demographic and travel characteristics.

A Markov chain Monte Carlo-based Bayesian framework for system identification and uncertainty estimation of full-scale structures

Identifying modal parameters and updating finite element models (FEMs) of real structures through ambient tests is essential in Structural Health Monitoring (SHM). However, efficiently solving the high-dimensional identification problem for full-scale structures and rigorously quantifying the uncertainties remain challenging. To address these two difficulties, a novel Bayesian framework based on Markov chain Monte Carlo (MCMC) is developed. The basic idea is to identify the high-dimensional posterior probability density functions (PDFs) in both modal analysis and model updating through constructing Markov chains by sampling in the important region of the parameter spaces. The sampling is done by Bayes-Mode-ID and the adaptive sequential Monte Carlo (ASMC) for modal analysis and model updating, respectively. A new formulation is developed to link the results from modal analysis, model updating and model class selection using the Markov chain samples. Thus, the propagation of the posterior uncertainties from the identified modal parameters to the updated model parameters can be quantified. Furthermore, the original ASMC method, which was developed by the authors, is extended for model class selection based on the set of generated Markov chain samples. To demonstrate the effectiveness of the proposed framework, multi-setup ambient vibration tests on a busy footbridge were conducted for measuring the accelerations at some pre-selected measurement points. The most probable modal and model parameters of the footbridge were identified together with the associated uncertainties by the proposed framework.

Assessing the performance of subspace-based modal identification procedures for systems subjected to structural damage and Coulomb-friction non-linearities

In this paper, the robustness of subspace-based identification methods for modal analysis is assessed for damaging systems including local Coulomb-friction mechanisms (or equivalently elasto-plasticity). The underlying objective is to capture damage-induced modal feature changes of structures, mainly frequency drop-off and damping increase, knowing that the presence of components with participating friction forces also affects modal signature. The performance of several modal detection procedures is assessed using the data acquired during the JVP (french for ‘Jonction Voile-Plancher’) test campaign during which a reinforced-concrete specimen, anchored to a shaking table by the means of swivels, is submitted to several kind of ground motion inputs of varied level. A comparison between Input/Output versus Output-Only identification is made using both seismic and broad-band random inputs and the potentialities of the modal selection procedures are highlighted for different cases where the two kind of non-linearities (damage occurrence and friction at swivels) are activated in different proportions. Regarding the presence of friction forces when conducting modal analysis, experimental results are backed up by a numerical study that enables a theoretical quantification of the apparent frequency and damping ratio values that should be expected after modal analysis for varied input-to-nonlinear forces ratios.

Multiobjective-clustering-based optimal heterogeneous sensor placement method for thermo-mechanical load identification

The direct measurement of external loads acting on structures remains a challenge in many engineering applications. In this context, mechanical load identification has received considerable attention to inverse them using some response signals. Obviously, sensor placement is fundamental to the success of load identification. Considering temperature effects in the environment, this study investigates an optimal heterogeneous sensor placement framework for multi-case mechanical load identification. Firstly, the temperature field approximation method is developed using the measuring points with the minimum error in RBF interpolation. Based on the modal superposition theory, the formulas of mechanical load identification under static and dynamic cases are then deduced utilizing the responses eliminating thermal-oriented components. With the specific loading position, an index of the modal contribution rate is defined to provide a reasonable modal selection. Further, an optimal placement framework of multi-type response sensors (strain gauges and accelerometers) considering distance constraints is constructed for mechanical load identification under static/dynamic multiple cases, in which the optimization objective integrates the global performance and local evaluation of the interested modal loads. More specifically, an algorithm of collaborative clustering for heterogeneous modal matrices is involved to avoid redundant information and alleviate the computation burden. Eventually, two numerical examples are discussed to demonstrate the validity and feasibility of the developed approach.

Analysis of the Influence of Structural Parameters on the Modes of a Gantry Machine Tool Crossbeam Based on Analytical Method

Modal analysis and structural parameters selection are essential in the structural design of the crossbeam of a gantry machine tool. In order to explore an efficient modal analysis method, the analytical method of modes of the beam with variable cross-section is derived based on the Euler beam theory and transfer matrix method. By dividing the machine tool crossbeam into 27 segments, the crossbeam modes are obtained analytically, which is validated by the finite element method. Compared with the FEM, the analytical method shows high efficiency. On this basis, the influence of various structural parameters, including bending stiffness and linear density, on the crossbeam mode is explored, and the sensitivity analysis is carried out. The results show that the first-order bending frequency of the crossbeam is mainly affected by the bending stiffness in the middle part of the crossbeam, followed by the linear density in the middle and end part. The first-order bending frequency of the crossbeam increases by increasing the bending stiffness in the middle part and decreasing the linear density of the crossbeam. The linear density both in the middle and end part has a great effect on the mode shape of the crossbeam. This study can provide a reference and method for structural design and modal analysis of machine tool crossbeams.

Multidimensional Psychological Model Construction of Public English Teaching Based on Deep Learning from Multimodal Perspective

Public English instruction focuses on language skill development while also enhancing students’ business professional knowledge and all-around ability under the direction of public English teaching objectives. From a knowledge standpoint, public English instruction contains a lot of tacit knowledge, making it appropriate for case teaching. This paper analyses the knowledge-locking factors in traditional case teaching based on deep learning from the multimodality perspective and builds a mental model to compensate for the shortcomings of the traditional case teaching. In this paper, a multidimensional psychological model is built that overcomes the limitations of conventional approaches. It is based on the multimodal feature extraction and modal selection algorithm of deep learning and multidimensional psychology in public English teaching. The method suggested in this paper outperforms the benchmark model created for the three evaluation indices and also demonstrates the benefits of multimodality. Create a fully integrated multidimensional mental model of the teaching and activity situations so that students can learn with a passion, become more engaged in practising their English, and ultimately develop the language skills required for employment.

A simple model for arbitrary pollution effects on rotating free-surface flows

In an experimental context, the contamination of an air–liquid interface by ambient pollutants can strongly affect the dynamics and the stability of a given flow. In some configurations, the interfacial flow can even be blocked by surface tension effects. A cylindrical free-surface flow driven by a slow rotating disc is considered here as a generic example of such effects and is investigated both experimentally and numerically. We suggest here a simple numerical model, without any superficial transport of the pollutants, adaptable into any code for single-phase flows. For the stationary axisymmetric base flow, the radial velocity at the interface is set to zero whereas the usual stress-free boundary conditions are retained for the perturbations. The model does not feature any free parameter. For a geometrical aspect ratio of 1/4, known to display ambiguous behaviour regarding stability thresholds, the modal selection as well as a nonlinear stability island found in the experiments are well reproduced by the model, both qualitatively and quantitatively. The robustness of the model has also been validated by replacing the radial velocity profile by a more accurate experimental fit, with very little influence on the stability results.

Multimodal classroom discourse analysis of TCSL teachers based on ELAN

With the advent of the information age, a lot of diversified language symbols such as discourse, text, pictures, body language, audio and video are widely used in teaching. Therefore, the construction of discourse meaning has the characteristics of multimodality. Multimodal discourse analysis theory provides a new perspective for the research of international Chinese classroom teaching. Based on ELAN6.0, this paper analyses the multimodal discourse of four TCSL teachers, so as to reveal the modal selection strategies of excellent international Chinese teachers, and based on this, attempts to summarize the teaching mode suitable for international Chinese classroom teaching.

Nonlinear compressed sensing-based adaptive modal shapes selection approach for efficient dynamic response analysis of flexible multibody system

Nonlinear compressed sensing-based adaptive modal shapes selection approach for efficient dynamic response analysis of flexible multibody system

A novel transient balancing technology of the rotor system based on multi modal analysis and feature points selection

In this paper, a novel transient balancing technology for identifying the unbalance parameters of the rotor system is proposed. Through basic coordinate transformation and multi modal analysis of the dynamical equations of rotor system, the calculation method for transient excitation forces is proposed. The transient excitation forces are calculated by the transient responses and modal parameters at several special positions of the rotor model. Then the “feature points” are introduced in the identification procedure and the unbalance parameters are identified by selecting the “feature points” of the calculated excitation forces. The accuracy of the proposed method is demonstrated by numerical simulations and experiments of the rotor system. Identified results of the proposed method are presented for different unbalance parameters and rotor models. Effects of different random noise levels to the displacement signals, varieties in damping and support stiffness are also presented to verify the adaptability and efficiency of the proposed method. The results show that the unbalance parameters estimations are robust with respect to the above influence factors.

Rolling Element Bearing Fault Diagnosis for Complex Equipment based on MFMD and BP Neural Network

A new fault feature extraction method for rolling element bearing is put forward in this paper based on modified Fourier mode decomposition (MFMD) and multi-scale permutation entropy, and the fault pattern recognition is studied by combining BP neural network. First, introduce frequency band entropy (FBE) to optimize the boundary frequency search and effective modal component selection of Fourier decomposition. Then, use MFMD to adaptively decompose the original vibration signal to several Fourier intrinsic mode functions (FIMFs) and select effective FIMFs. Next, use multi-scale permutation entropy to quantify the fault features. Finally, input the fault feature vector into the BP neural network for fault diagnosis model training and testing. The method proposed in this paper is applied to the rolling element bearing simulation data and the public bearing fault test data set of Case Western Reserve University to verify the feasibility of the method. The results show that the method proposed in this paper has a high identification accuracy for different types of faults, reaching over 98%. The feasibility and superiority of the rolling element bearing fault diagnosis method based on MFMD and BP neural network are verified, and technical support is provided for the health state assessment of rolling element bearings.

Actuator layout design with gain-oriented modal selection for sectional offset reflector antennas

Actuator layout design with gain-oriented modal selection for sectional offset reflector antennas

Robust distributed modal regression for massive data

Modal regression is a good alternative of the mean regression and likelihood based methods, because of its robustness and high efficiency. A robust communication-efficient distributed modal regression for the distributed massive data is proposed in this paper. Specifically, the global modal regression objective function is approximated by a surrogate one at the first machine, which relates to the local datasets only through gradients. Then the resulting estimator can be obtained at the first machine and other machines only need to calculate the gradients, which can significantly reduce the communication cost. Under mild conditions, the asymptotical properties are established, which show that the proposed estimator is statistically as efficient as the global modal regression estimator. What is more, as a specific application, a penalized robust communication-efficient distributed modal regression variable selection procedure is developed. Simulation results and real data analysis are also included to validate our method.

Kajian Permintaan Perjalanan Penumpang dalam Rangka Penyediaan Prasarana Sarana Transportasi Umum di Bali

Tujuan: Untuk mengembangkan kebijakan transportasi yang lebih komprehensif di Bali, penelitian ini perlu dilakukan.
 Metode Penelitian: Pengumpulan data dilakukan dengan melakukan survei berbasis kuesioner.
 Temuan: Perjalanan distribusi internal menunjukkan produksi perjalanan tertinggi dari Denpasar dan Kuta, sekitar 24,93% dan 11,75%, sedangkan atraksi perjalanan terbesar juga ke Denpasar dan Kuta, yaitu 13,76% dan 16,73% dari total jumlah perjalanan orang per hari. Distribusi perjalanan eksternal menunjukkan bahwa sebagian besar orang yang keluar masuk Bali melalui Bandara Ngurah Rai dan Pelabuhan Gilimanuk. Pemilihan modal menunjukkan bahwa yang paling banyak digunakan adalah sepeda motor 85,40%, mobil 10,27%, truk 3,60% dan angkutan umum 0,73%. Perkiraan generasi perjalanan dan daya tarik memiliki tingkat pertumbuhan 5,812% / tahun.
 Implikasi: Perlu diantisipasi untuk mengembangkan moda alternatif baru berdasarkan teknologi dari Denpasar ke semua kabupaten, seperti Kereta / LRT / ART dan transportasi feri, yang disepakati oleh 83,56% dan 89,33% dari orang-orang.

Study of a multi-ring cavity based single-frequency fiber laser and associated thermal effects

• A concise criterion for length selection is proposed for multi-ring fiber cavities. • Influence of thermal tampering on modal selection is analyzed and verified. • Stable single-frequency Yb-doped fiber lasers of multi-ring cavities are achieved. In this paper we present a detailed study of compound ring resonator based fiber lasers for single-frequency (SF) generation and associated thermal effects. A concise criterion to select a single longitudinal mode (SLM) by the multi-ring cavity (MRC) scheme is proposed and confirmed with experiments on fiber lasers with zero, one, and two sub-ring cavities. The results demonstrate the necessity of using at least two sub-rings of tight length differential to ensure stable SF laser emission by the MRC scheme. Subsequent studies of temperature variation upon the two sub-rings reveal that the robustness of SF emission status can be further secured by a controlled thermal tampering process. Stable SF emission, centered at 1064.2 nm with a 3-dB linewidth of <10 kHz, are thus achieved by Yb-doped all-fiber lasers with the MRC scheme.