Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

Utilization of biomarkers for the prognostic prediction of cardiac arrest survivors using a multi-modal approach

International guidelines for post-cardiac arrest care recommend using multi-modal strategies to avoid the withdrawal of life-sustaining therapy(WLST)in patients with the potential for neurological recovery.[1]However,a clear methodology for multi-modal approaches has yet to be developed.Neuron-specific enolase(NSE)is currently the only recommended biomarker,and the European Resuscitation Council(ERC)and the European SocietyofIntensiveCareMedicine(ESICM)have proposed a cutoff value of 60μg/L at 48 and/or 72 h after the return of spontaneous circulation(ROSC)as a multimodal prognostic tool for predicting poor neurological outcomes.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

The role of multimodality imaging in calcified valves with infective endocarditis

A 68-years old woman presented with ahistory of recurrent fever of 38-39°ac-companied by chills and weakness over the past month.Her physical examination was unremarkable except for an audible 3/6 ejection murmur at the 2nd right intercostal space.Her vital signs were normal with no fever at presentation.Laboratory tests showed elevated white blood count of 11,800cells/mm3 with a remarkable neutrophilia and elevated C-reactive protein of 14 mg/dL.Blood glucose,renal and liver function tests were all normal.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Modal Frequency Prediction of Chladni Patterns Using Machine Learning

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT,a lite and conversational variant of Generative Pretrained Transformer 4(GPT-4)developed by OpenAI,is one of the milestone Large Language Models(LLMs)with billions of parameters.LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks,which profoundly impact various fields.This paper mainly discusses the future applications of LLMs in dentistry.We introduce two primary LLM deployment methods in dentistry,including automated dental diagnosis and cross-modal dental diagnosis,and examine their potential applications.Especially,equipped with a cross-modal encoder,a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations.We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application.While LLMs offer significant potential benefits,the challenges,such as data privacy,data quality,and model bias,need further study.Overall,LLMs have the potential to revolutionize dental diagnosis and treatment,which indicates a promising avenue for clinical application and research in dentistry.

Development of a software platform for bridge modal and damage identification based on ambient excitation

Modal and damage identification based on ambient excitation can greatly improve the efficiency of high-speed railway bridge vibration detection.This paper first describes the basic principles of stochastic subspace identification,peak-picking,and frequency domain decomposition method in modal analysis based on ambient excitation,and the effectiveness of these three methods is verified through finite element calculation and numerical simulation,Then the damage element is added to the finite element model to simulate the crack,and the curvature mode difference and the curvature mode area difference square ratio are calculated by using the stochastic subspace identification results to verify their ability of damage identification and location.Finally,the above modal and damage identification techniques are integrated to develop a bridge modal and damage identification software platform.The final results show that all three modal identification methods can accurately identify the vibration frequency and mode shape,both damage identification methods can accurately identify and locate the damage,and the developed software platform is simple and efficient.

The effect of Typhoon Kalmaegi on the modal energy and period of internal waves near the Dongsha Islands(South China Sea)

The influence of Typhoon Kalmaegi on internal waves near the Dongsha Islands in the northeastern South China Sea was investigated using mooring observation data.We observed,for the first time,that the phenomenon of regular variation characteristics of the 14-d spring-neap cycle of diurnal internal tides(ITs)can be regulated by typhoons.The diurnal ITs lost the regular variation characteristics of the 14-d spring-neap cycle during the typhoon period owing to the weakening of diurnal coherent ITs,represented by O_(1)and K_(1),and the strengthening of diurnal incoherent ITs.Results of quantitative analysis showed that during the pre-typhoon period,timeaveraged modal kinetic energy(sum of Modes 1–5)of near-inertial internal waves(NIWs)and diurnal and semidiurnal ITs were 0.62 kJ/m^(2),5.66 kJ/m^(2),and 1.48 kJ/m^(2),respectively.However,during the typhoon period,the modal kinetic energy of NIWs increased 5.11 times,mainly due to the increase in high-mode kinetic energy.At the same time,the modal kinetic energy of diurnal and semidiurnal ITs was reduced by 68.9%and 20%,respectively,mainly due to the decrease in low-mode kinetic energy.The significantly reduced diurnal ITs during the typhoon period could be due to:(1)strong nonlinear interaction between diurnal ITs and NIWs,and(2)a higher proportion of high-mode diurnal ITs during the typhoon period,leading to more energy dissipation.

Accessibility Measures in Modal Choice Models: A Proof of Concept Applied to Freight Transport

Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their space.Mainly brain tumour masses occur due to malignant masses.These tissues must die so that new tissues are allowed to be born and take their place.Tumour segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance variation.Manually finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and radiologists.Radiologists could not work for large volume images simultaneously,and many errors occurred due to overwhelming image analysis.The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches.This research study proposed an automatic model for tumor segmentation in MRI images.The proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy array.In the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters.In the third step,the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour masses.Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and active.The proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-gradeglioma (LGG) volumes for the test set is 0.9950, which shows the proposedmodel has achieved significant results in segmenting the tumour in MRI usingdeep learning approaches. The proposed model is fully automatic that canimplement in clinics where human experts consumemaximumtime to identifythe tumorous region of the brain MRI. The proposed model can help in a wayit can proceed rapidly by treating the tumor segmentation in MRI.

Study of Axial Vibration of a Motor-Compressor System Using Operational Modal Analysis

A case study of excessive vibration on a motor-compressor system is presented in this paper.After barely two months of operation,the reciprocating compressor motor’s routine monitoring revealed excessive axial vibration amplitude.For this reason,the Operational Modal Analysis(OMA)was carried out in order to identify the pri-mary cause.According to the investigation,one of the harmonic components which was 18 times the motor’s running speed matched with a resonance frequency of 112 Hz.According to OMA study,the motor was vibrating in torsional motion because the compressor’s load had stimulated the entire motor-compressor unit at this reso-nance frequency.The analysis also demonstrates the bulging effect of the motor shaft’s axial vibration on the motor’s endplate.

Numerical Modelling of Double Slit Caisson Breakwater Integrated Oscillating Water Column Wave Energy Converter with Modal Superposition Method

Oscillating water column wave energy converter is a power generation device in which ocean waves excite the oscillation of the water surface in an air chamber, which generates fluctuations in air pressure and rotate air turbine generator(s). The oscillation of the fluid in the air chamber is a fluid oscillation phenomenon with a natural period, similar to fluid oscillation in a container such as sloshing. Previous research has shown that for an oscillating water column with a single air chamber submerged in water, the oscillation characteristics can be modeled as a one-degree-of-freedom oscillation system that takes only a single oscillation mode into account. However, a double-slit breakwater integrated oscillating water column wave energy converter using two water columns of the breakwater separated by slit walls, has been verified to have two resonance periods. In this study, the free oscillating motion of the oscillating water column wave energy converter using the double-slit breakwater is modeled by modal superposition method including the first-order and second-order modes of vertical motion of the two water surfaces. The result from the simulation is similar to the result of the free vibration experiment.

Mining Fine-Grain Face Forgery Cues with Fusion Modality

Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns.Despite the considerable progress in existing methods,we note that:Previous works overlooked finegrain forgery cues with high transferability.Such cues positively impact the model’s accuracy and generalizability.Moreover,single-modality often causes overfitting of the model,and Red-Green-Blue(RGB)modal-only is not conducive to extracting the more detailed forgery traces.We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues.First,we propose two functional modules to reveal and locate the deeper forged features.Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module,which can excavate the association between dualmodal space and channels and enhance the learning of subtle noise features.A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality.The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning.Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.

A Multilevel Hierarchical Parallel Algorithm for Large-Scale Finite Element Modal Analysis

The strict and high-standard requirements for the safety and stability ofmajor engineering systems make it a tough challenge for large-scale finite element modal analysis.At the same time,realizing the systematic analysis of the entire large structure of these engineering systems is extremely meaningful in practice.This article proposes a multilevel hierarchical parallel algorithm for large-scale finite element modal analysis to reduce the parallel computational efficiency loss when using heterogeneous multicore distributed storage computers in solving large-scale finite element modal analysis.Based on two-level partitioning and four-transformation strategies,the proposed algorithm not only improves the memory access rate through the sparsely distributed storage of a large amount of data but also reduces the solution time by reducing the scale of the generalized characteristic equation(GCEs).Moreover,a multilevel hierarchical parallelization approach is introduced during the computational procedure to enable the separation of the communication of inter-nodes,intra-nodes,heterogeneous core groups(HCGs),and inside HCGs through mapping computing tasks to various hardware layers.This method can efficiently achieve load balancing at different layers and significantly improve the communication rate through hierarchical communication.Therefore,it can enhance the efficiency of parallel computing of large-scale finite element modal analysis by fully exploiting the architecture characteristics of heterogeneous multicore clusters.Finally,typical numerical experiments were used to validate the correctness and efficiency of the proposedmethod.Then a parallel modal analysis example of the cross-river tunnel with over ten million degrees of freedom(DOFs)was performed,and ten-thousand core processors were applied to verify the feasibility of the algorithm.

Research on Breeze Vibration Law and Modal Identification Method of Conductor Considering Anti-Vibration Hammer Damage

In the harsh environment,the structural health of the anti-vibration hammer,which suffers from the coupled effects of corrosion and fatigue damage,is significantly reduced.As part of the conductor structure,the anti-vibration hammer is rigidly attached to the conductor,effectively suppressing conductor vibration.The conductor’s breeze vibration law and natural modal frequency are altered damage to the anti-vibration hammer structure.Through built a vibration experiment platform to simulate multiple faults such as anti-vibration hammer head drop off and position slippage,which to obtained the vibration acceleration signal of the conductor.The acceleration vibration signal is processed and analyzed in the time and frequency domains.The results are used to derive the breeze vibration law of the conductor under multiple faults and propose an anti-vibration hammer damage online monitoring technology.The results show that the vibration acceleration value and vibration intensity of the conductor are significantly increased after the anti-vibration hammer damage.The natural frequency increases for each order,with an absolute change ranging from 0.15 to 6.49 Hz.The anti-vibration hammer slipped due to a loose connection,the 1st natural frequency increases from 8.18 to 16.62 Hz.Therefore,in engineering applications,there can be no contact to determine the anti-vibration hammer damage situation by monitoring the modal natural frequency of the conductor.This is even a tiny damage that cannot be seen.This method will prevent the further expansion of the damage that can cause accidents.

Modal Parameter Identification Method of Jacket Platform Structure Based on AFDD and Optimized FBFFT

Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.

Modal and Thermal Analysis of a Modified Connecting Rod of an Internal Combustion Engine Using Finite Element Method

The connecting rod is one of the most important moving components in an internal combustion engine. The present work determined the possibility of using aluminium alloy 7075 material to design and manufacture a connecting rod for weight optimisation without losing the strength of the connecting rod. It considered modal and thermal analyses to investigate the suitability of the material for connecting rod design. The parameters that were considered under the modal analysis were: total deformation, and natural frequency, while the thermal analysis looked at the temperature distribution, total heat flux and directional heat flux of the four connecting rods made with titanium alloy, grey cast iron, structural steel and aluminium 7075 alloy respectively. The connecting rod was modelled using Autodesk inventor2017 software using the calculated parameters. The steady-state thermal analysis was used to determine the induced heat flux and directional heat flux. The study found that Aluminium 7075 alloy deformed more than the remaining three other materials but has superior qualities in terms of vibrational natural frequency, total heat flux and lightweight compared to structural steel, grey cast iron and titanium alloy.