Modal Model Research Articles

Estimation of the static bending modulus of elasticity in glulam elements by ultrasound and modal-updating NDT techniques

Glulam timber is gaining popularity as a structural element due to its cost-effectiveness, strength, and environmental benefits. However, being an organic material, it is susceptible to degradation. Therefore, nondestructive testing (NDT) methods are essential for assessing its structural health. In this study, two NDT approaches, ultrasound-based and modal updating, were used to predict the static bending modulus of elasticity (MOE) in Pinus Sylvestris and Spruce species. The results demonstrated that the modal updating and linear regression models outperformed the ultrasound technique in accurately determining the static MOE. Additionally, considering the influence of knots improved the accuracy of ultrasound predictions.

An innovative approach based on meta-learning for real-time modal fault diagnosis with small sample learning

The actual multimodal process data usually exhibit non-linear time correlation and non-Gaussian distribution accompanied by new modes. Existing fault diagnosis methods have difficulty adapting to the complex nature of new modalities and are unable to train models based on small samples. Therefore, this paper proposes a new modal fault diagnosis method based on meta-learning (ML) and neural architecture search (NAS), MetaNAS. Specifically, the best performing network model of the existing modal is first automatically obtained using NAS, and then, the fault diagnosis model design is learned from the NAS of the existing model using ML. Finally, when generating new modalities, the gradient is updated based on the learned design experience, i.e., new modal fault diagnosis models are quickly generated under small sample conditions. The effectiveness and feasibility of the proposed method are fully verified by the numerical system and simulation experiments of the Tennessee Eastman (TE) chemical process.

Experimental modal transferring of industrial robot with data augmentation through domain adaptation and transfer boosting

Pose-dependent modal properties of the robotic milling system determine the milling stability, which significantly affects the machining accuracy and surface quality. Experimental modal analysis is an effective way to obtain the actual modal properties, however, the full experimental modal measurement in the entire robot workspace is repetitive and time-consuming, and the modal properties of a measured workspace can hardly transfer to an unmeasured workspace because of domain discrepancy and data imbalance. In this paper, the mathematical formalization of modal properties transferring problem between different workspaces is recommended as heterogeneous transfer, and a transfer learning method of modal properties is proposed by integrating domain adaptation and transfer boosting to eliminate domain discrepancy and data imbalance. Firstly, a workspace is selected as the source workspace in which pose-dependent modal properties are obtained as the source data through a series of sufficient impact tests. For a new workspace, namely the target workspace, a few impact tests would be sufficient to obtain the modal properties as the target data. Then, both the inputs of source and target data are projected onto a reproducing kernel Hilbert space (RKHS) through transfer component analysis (TCA) to get the IN-Domain (IND) inputs. The source data is used to generate pseudo data with data augmentation through affine transformation, and the pseudo data is regarded as coarse data for the target workspace. Finally, the precise modal properties models are trained with TrAdaBoost.R2 in which the training data was obtained by mixing pseudo data and target data, and the unmeasured modal properties in the target space are predicted. The effectiveness of the proposed method is verified through experimental modal properties transferring between different workspaces and modal orders for the robotic milling system, and the comparisons with Gaussian process regression (GPR) demonstrate the outperformance of the proposed method.

Discrimination and Experimental Study of HSCCC Operational Modal based on LSCF

The dynamic characteristics of high-speed counter-current chromatography (HSCCC) have a significant impact on its separation efficiency and equipment stability. The accurate discrimination of the dynamic characteristic during the separation process constitutes the prerequisite for clarifying the separation mechanism and performance improvement. This study establishes the HSCCC operational modal discrimination model using the least-square complex frequency domain method (LSCF) and then derives the discrimination function based on the cross-power spectral density (CPSD) function and cross-half power spectral density (PSD) function. Then an operational modal acquisition and analysis system was built for the operational modal in three directions at six common speeds. Finally, the inherent frequencies and mode of vibration of the experimental modes were verified by the Modal Assurance Criterion (MAC), Mean Phase Deviation (MPD), Modal Phase Collinearity (MPC), and Mode Indicator Functions (MIF) in this study. The results prove the feasibility of the proposed method. It shows that the CPSD function is more applicable to the HSCCC dynamic performance analysis, which should be considered in the actual separation process analysis.

최빈값 추정을 위한 의사결정나무

Decision trees are one of the data mining techniques that make predictions by recursively partitioning data structures based on split rules. Since the analysis results can be understood through the tree structure, it has the advantage of having high interpretation power as well as predictive power. In addition, it is used in many fields because it is able to identify nonlinear relationships between response and predictor variables. However, if the purpose of it is to predict the mode of the response variable, there is a limitation in that the previously proposed decision tree cannot be applied. Thus, we develop a new form of the modal decision tree model by integrating the kernel density estimation methods into the decision tree model. The simulation is conducted with four models. The results are compared for each size of the data when the predictor variable and the response variable are linear and nonlinear relationship cases. When the data has a linear relationship, the performance of the modal desicion tree model proposed in this paper is comparative to that of the previously proposed modal linear regression (MODLR) model. When the data has a nonlinear relationship, the performance of the modal tree model is better.

FDD based modal identification of structures using least squares approach

In the conventional frequency domain modal identification procedures, modal parameters are attempted to be identified using power spectral density matrix decomposition. As it is well stated in the literature, the quality of estimated mode shapes significantly increases when an average PSD matrix over a selected frequency band is utilized in the identification procedure. In this context, this study aims at deriving a weighted average for the PSD matrix to increase the quality of the estimated mode shapes by using an FDD-based modal model. Then, the remaining modal parameters comprising modal frequency, damping ratio and spectral density of modal exciting force are calculated through a least squares equation. Moreover, the presented FDD based approach is also adapted to the solution of multiple setup problem. The proposed method for single and multiple setup cases is validated by making use of one numerical and two field data examples. The obtained results reveal that the presented frequency domain technique can successfully achieve high quality of modal estimates in the presence of measurement noise, and it can be easily implemented to field applications.

A hybrid estimation of carbon footprints for urban commuting transportation via path reconstruction

The transportation sector is a major source of carbon emissions, and it is of great significance to study the estimation method of carbon emissions from urban commuting traffic for energy conservation and emission reduction. In view of the difficulty of collecting detailed trip trajectory data, this paper first reconstructs the trip paths via an improved modal choice model and a modified path planning model based on the O-D trip matrix, taking seven single traffic modals and two combined modals into account. In order to estimate the carbon footprints with theoretical accuracy, the bottom-up method is adopted considering the trip modal, vehicle type, power source, vehicle occupancy, operation characteristics and traffic conditions. Meanwhile, faced with the converted carbon emissions from electric vehicles, factors like charging efficiency, vehicular load, regional power structure and transmission loss are further considered in the estimation function. A case study of Changzhou City has been performed to verify the feasibility of the proposed models, where the volume distribution of commuting trips is predicted upon a modified network traffic assignment by TransCAD, and the spatial distribution of carbon emission intensity has further expanded to the adjacent areas via ArcMap analysis tools. The total carbon emission and the average link emission intensity of daily commuting in the study area are about 14.7 × 105 kg/day and 870 kg/km respectively. The discussion results indicate that the CO2 emission of fuel-driven vehicles accounts for over 86%, and the equivalent carbon emission of electric vehicles accounts for about 14% under given modal choices. The correlations of carbon emissions to road levels and zone attributes get further revealed and discussed based on the estimation results.

A neuromarker for deficit syndrome in schizophrenia from a combination of structural and functional magnetic resonance imaging.

Deficit schizophrenia (DS), defined by primary and enduring negative symptoms, has been proposed as a promising homogeneous subtype of schizophrenia. It has been demonstrated that unimodal neuroimaging characteristics of DS were different from non-deficit schizophrenia (NDS), however, whether multimodal-based neuroimaging features could identify deficit syndrome remains to be determined. Functional and structural multimodal magnetic resonance imaging of DS, NDS and healthy controls were scanned. Voxel-based features of gray matter volume, fractional amplitude of low-frequency fluctuations, and regional homogeneity were extracted. The support vector machine classification models were constructed using these features separately and jointly. The most discriminative features were defined as the first 10% of features with the greatest weights. Moreover, relevance vector regression was applied to explore the predictive values of these top-weighted features in predicting negative symptoms. The multimodal classifier achieved a higher accuracy (75.48%) compared with the single modal model in distinguishing DS from NDS. The most predictive brain regions were mainly located in the default mode and visual networks, exhibiting differences between functional and structural features. Further, the identified discriminative features significantly predicted scores of diminished expressivity factor in DS but not NDS. The present study demonstrated that local properties of brain regions extracted from multimodal imaging data could distinguish DS from NDS with a machine learning-based approach and confirmed the relationship between distinctive features and the negative symptoms subdomain. These findings may improve the identification of potential neuroimaging signatures and improve the clinical assessment of the deficit syndrome.

Introduction to the synthese topical collection ‘modal modeling in science: modal epistemology meets philosophy of science’

Introduction to the synthese topical collection ‘modal modeling in science: modal epistemology meets philosophy of science’

Unified Object Detector for Different Modalities Based on Vision Transformers

Traditional systems typically require different models for processing different modalities, such as one model for RGB images and another for depth images. Recent research has demonstrated that a single model for one modality can be adapted for another using cross-modality transfer learning. In this paper, we extend this approach by combining cross/inter-modality transfer learning with a vision transformer to develop a unified detector that achieves superior performance across diverse modalities. Our research envisions an application scenario for robotics, where the unified system seamlessly switches between RGB cameras and depth sensors in varying lighting conditions. Importantly, the system requires no model architecture or weight updates to enable this smooth transition. Specifically, the system uses a depth sensor in low light conditions (night time) and both an RGB camera and a depth sensor or RGB camera only in well-lit environments. We evaluate our unified model on the SUN RGB-D dataset and demonstrate that it achieves a similar or better performance in terms of the mAP50 compared to state-of-the-art methods in the SUNRGBD16 category and a comparable performance in point-cloud-only mode. We also introduce a novel inter-modality mixing method that enables our model to achieve significantly better results than previous methods. We provide our code, including training/inference logs and model checkpoints, to facilitate reproducibility and further research.

Travel Demand Management in an Auto Dominated City: Can Travel Behaviour Be Nudged in the Kingdom of Saudi Arabia?

Car ownership and use in the Kingdom of Saudi Arabia (KSA) are very high due to the high income, the low fuel prices and the near absence of public transportation in the Kingdom. Currently, the Kingdom is going through a massive transformation and entering a new era of national reforms. One of the main aims of the national reforms is to reduce car dependency and enhance more sustainable options of travel such as public transportation in the KSA. In order to achieve such an aim, there are two hurdles: Firstly, there must be a provision of decent public transportation options, and secondly, there is a need to influence travel behaviour and encourage the shift from private cars to public transportation. For example, in Riyadh city, an impressive metro system is being constructed and will start operation in 2023. To influence travel behaviour, travel demand management measures (TDM), in particular pricing measures, can be adopted and implemented, in order to help and support achieving the target. The main aim of this paper, therefore, is to assess the attitudes of Saudi nationals towards—and willingness to accept—pricing measures and their possible impacts on their travel behaviour in the city of Riyadh. The methodology includes collecting data using an online survey on travel behaviour and attitudes in Riyadh and calibrating multinomial logit modal choice models. The participants in the survey were asked to report their support of the pricing measures for the objective of reducing congestion in the city, improving road safety or reducing travel time. The results show the highest support towards pricing measures for improving road safety, reducing travel times and, lastly, reducing congestion in the city.

Dynamic concurrent topology optimization and design for layer-wise graded structures

There are many requirements in engineering fields to improve the structural performance. Thus, the hierarchical structures have attracted lots of attention because of the appealing material efficiency and functionality, e.g., the bearing capacity and interfacial strength from large aerospace structures to small device facilities. However, the dynamic topology optimization design of these structures composed of multiple microstructures remains challenge by the connectivity issue between different microstructures and the extensive computational cost when handling the interval-frequency problem during multiscale concurrent design. In this work, the connectivity model is proposed for improving the manufacturability of hierarchical structure, which includes the connectable layer scheme between different microstructures and the enlarged filter domain. To break the bottleneck of computation cost induced by the simultaneous optimization of macro-microscopic design variables, the combined method of modal superposition and model order reduction is incorporated to efficiently compute the frequency response. Two response vectors are presupposed before decoupling sensitivity analysis. In addition, in order to implicitly ensure the bearing capacity of the structure, the stiffness maximization design is integrated into the response minimization problem when the frequency interval range is high. The layer-wise graded cantilever beam is used as an example to demonstrate the effectiveness and accuracy of proposed method in handling dynamic concurrent topology optimization problem. The manufacturability and systematic performance of hierarchical structures are significantly improved when they simultaneously experience the interval-frequency harmonic and static loads.

Modal Test and Model Modification of the Rotating Parts of a Self-propelled Antiaircraft Gun

The rotating parts of a self-propelled antiaircraft gun is taken as the research object, the single point excitation and multi-point response frequency response function method were used to test the experimental mode. At the same time, the finite element simulation model is established, and the frequency and mode of vibration are obtained. Combined with the modal test and analysis of the rotating parts, the stiffness of the connection between the rotating parts and the turret is selected as the optimization parameter. The numerical simulation model was optimized and modified by comparing the experimental and simulation modes and combining with the sensitivity analysis of the connection parameters, the correction variable with the higher sensitivity of the parameters to the results was selected. After optimization, the error between the first order calculation frequency and the test frequency is reduced from 8.61% to 1.13%, it is shown that the simulation model optimized by parameters can better reflect the physical characteristics of the real structure.

Kinds of modalities and modeling practices

Several recent accounts of modeling have focused on the modal dimension of scientific inquiry. More precisely, it has been suggested that there are specific models and modeling practices that are best understood as being geared towards possibilities, a view recently dubbed modal modeling. But modalities encompass much more than mere possibility claims. Besides possibilities, modal modeling can also be used to investigate contingencies, necessities or impossibilities. Although these modal concepts are logically connected to the notion of possibility, not all models are equal in their affordances for these richer modal inferences. This paper investigates the modal extent of selected models and argues that analyzing singular model-target pairings by themselves is typically not enough to explain their modal aptness or to identify the kinds of modalities they can be used to reason about. Furthermore, it is argued that some important concepts that are not explicitly modal - like biological robustness - can be understood modally through their relational nature to a background space of possibilities. In conclusion, it is suggested that the strategy of modal modeling is contrastive, situating particular possibilities in larger modal spaces and studying the structural relations within them.

A data-driven model for water quality prediction in Tai Lake, China, using secondary modal decomposition with multidimensional external features

Study regionTai Lake, the third largest freshwater lake in China, with a history of serious ecological pollution incidents. Study focusLake water quality prediction techniques are essential to ensure an early emergency response capability for sustainable water management. Herein, an effective data-driven ensemble model was developed for predicting lake dissolved oxygen (DO) based on meteorological factors, water quality indicators and spatial information. First, variation mode decomposition (VMD) was used to decompose data into multiple modal components and classify them into feature terms and self terms. The feature terms were combined with relevant external features for multivariate prediction by convolutional neural network (CNN) and a bi-directional long and short-term memory (BiLSTM) with attention mechanism (AT), as well as using the whale optimization algorithm (WOA) to optimize the model hyperparameters. The self terms form a secondary modal decomposition model. Finally, the groupings were linearly summed to obtain outcome. New hydrological insights for the region:The proposed model has the highest prediction accuracy in Tai Lake as well as the best prediction effect using 0.5 days as the period. This research also establishes a stepwise water temperature regulation mechanism, where the output of the target DO content value is achieved by changing the magnitude of water temperature and combining it with this prediction model, thereby strengthening the protection of water resources and the management of fishery production.

Cross-Length of Mode Shapes in Structural Dynamics: Concept and Applications

Modal mass is one of the modal parameters that are required to define the dynamic behavior of a structure when a modal model is used. In experimental modal analysis (EMA), modal masses are the least reliable modal parameter, while in operational modal analysis (OMA), modal masses cannot be estimated because the exciting forces are not measured. In this paper, the concept of cross-length between mode shapes is formulated for continuous and discrete systems, and important properties are derived from this definition. It is demonstrated that the cross-length is zero for all modes in constant mass density systems. For structures consisting of parts with different mass density, equations that relate the partial cross-lengths over the different volumes and the total masses of the different parts can be formulated for each mode, i.e., there is a relationship between the cross-length and the total mass of the parts with different mass density. The equations proposed in the paper have been validated through numerical simulations and experimental testing on two lab-scaled structures. This methodology can also be applied as a correlation technique, specifically to determine how the mass is distributed in the structure, as well as a technique to construct a proportional FRF in constant mass density systems.

Acoustic Dissipation and Modal Coupling in Server Computers

Interest in the interior acoustics of server computers is growing as noise generated by air movers (fans) is increasingly observed to impact the performance of hard disk drives (HDD) inside these computers. In a typical server, a backplane cavity is located between an array of air movers and an array of HDD such that the HDD may be within a fan diameter of the air movers. The possibility of functional degradation requires careful management of air mover noise with respect to HDD susceptibility to acoustic noise disturbances and motivates understanding the physical phenomena underlying the acoustics. A previous investigation demonstrated the importance of coherence between acoustic standing wave modes in the backplane cavity. In this paper server interior airborne acoustics are further investigated by focusing on acoustic dissipation and inter-modal coupling in the backplane cavity. Various dissipation models are developed and used in conjunction with a previously formulated modal cavity acoustic model to generate cavity transfer factors. The transfer factor predictions are compared to measured transfer factors to draw further insights into the physical phenomena governing the interior cavity acoustics.

Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection.

The use of medical images for colon cancer detection is considered an important problem. As the performance of data-driven methods relies heavily on the images generated by a medical method, there is a need to inform research organizations about the effective imaging modalities, when coupled with deep learning (DL), for detecting colon cancer. Unlike previous studies, this study aims to comprehensively report the performance behavior for detecting colon cancer using various imaging modalities coupled with different DL models in the transfer learning (TL) setting to report the best overall imaging modality and DL model for detecting colon cancer. Therefore, we utilized three imaging modalities, namely computed tomography, colonoscopy, and histology, using five DL architectures, including VGG16, VGG19, ResNet152V2, MobileNetV2, and DenseNet201. Next, we assessed the DL models on the NVIDIA GeForce RTX 3080 Laptop GPU (16GB GDDR6 VRAM) using 5400 processed images divided equally between normal colons and colons with cancer for each of the imaging modalities used. Comparing the imaging modalities when applied to the five DL models presented in this study and twenty-six ensemble DL models, the experimental results show that the colonoscopy imaging modality, when coupled with the DenseNet201 model under the TL setting, outperforms all the other models by generating the highest average performance result of 99.1% (99.1%, 99.8%, and 99.1%) based on the accuracy results (AUC, precision, and F1, respectively).

Initiation mechanism analysis of wheel polygonal wear on high-speed railway based on refined vibration model for ballastless track system

The wheel polygonal wear on high-speed railway exhibits constant high frequency excitation, which results in strong vibrations/noise and deteriorate railway systems. To get insight into the wheel polygonal wear mechanism, a refined vibration model of ballastless track comprising an improved fastener model and track-bed broadband modal model is proposed, and further its coupled dynamic model with the vehicle is established. The simulation scheme of wheel polygonal wear initiation and a wheel out-of-roundness growth rate index are proposed to measure the growth degree of wheel polygon at each order. The relevance between the wheel polygonal wear and wheel-rail coupled resonances is studied, and the time-varying characteristics of wheel-rail coupled vibration modes in a vehicle-discrete supporting track system are firstly clarified. On this basis, the initiation mechanism of wheel polygonal wear is revealed. The results suggest that the rail 3rd-order bending resonance between bogie wheels contribute significantly to wheel polygonal wear, which is attributed to the time-invariant modal shape of this rail 3rd-order bending vibration as well as the natural frequency consistent with the actual polygonal wear excitation. The effects of the fastener model/parameters and track-bed broadband vibrations on wheel polygonal wear are analyzed. The results show that the improved fastener model eliminates the error between the rail 3rd-bending modal frequency obtained based on the simple model and actual polygonal wear frequency. In addition, increasing the fastener damping and iron chair mass can significantly suppress wheel polygonal wear. Finally, the slight contribution of track-bed broadband vibrations to wheel polygonal wear is confirmed.

A Bayesian approach to global mode shape identification using modal assurance criterion-based discrepancy model

Modal identification of large-scale engineering structures may require multiple measurements obtained from different locations, due to the limitations in the instrumentation equipment. In such cases, the global values for modal frequencies and damping ratios can be estimated over the measurement setups by simply obtaining their statistical expected values. Estimation of global mode shape vectors, however, arises as a much more challenging problem since the local mode shapes confine at different degrees of freedom of the measured structure. To overcome this problem, a novel Bayesian methodology is presented in this study using a modal assurance criterion-based discrepancy model. By making use of this model, a novel solution procedure is presented to the existing literature, and mode shape normalization-based problems arising in the estimation of covariance matrix are solved. The computational efficiency of the proposed method is verified by two numerical and one real data example. The conducted investigations reveal that the presented methodology shows a good performance in terms of computational accuracy.