Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

Transferable adversarial slow feature extraction network for few-shot quality prediction in coal-to-ethylene glycol process

In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time delays.The resulting few-shot learning scenarios present a hurdle to the efficient development of predictive models.To address this issue,our study introduces the transferable adversarial slow feature extraction network(TASF-Net),an innovative approach designed specifically for few-shot quality prediction in the CTEG process.TASF-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG process.Additionally,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step.A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG data.Adversarial learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and significantly.Furthermore,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target domain.The effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG dataset.Compared with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process.

Effects of projectile parameters on the momentum transfer and projectile melting during hypervelocity impact

The effects of projectile/target impedance matching and projectile shape on energy,momentum transfer and projectile melting during collisions are investigated by numerical simulation.By comparing the computation results with the experimental results,the correctness of the calculation and the statistical method of momentum transfer coefficient is verified.Different shapes of aluminum,copper and heavy tungsten alloy projectiles striking aluminum,basalt,and pumice target for impacts up to 10 km/s are simulated.The influence mechanism of the shape of the projectile and projectile/target density on the momentum transfer was obtained.With an increase in projectile density and length-diameter ratio,the energy transfer time between the projectile and targets is prolonged.The projectile decelerates slowly,resulting in a larger cratering depth.The energy consumed by the projectile in the excavation stage increased,resulting in lower mass-velocity of ejecta and momentum transfer coefficient.The numerical simulation results demonstrated that for different projectile/target combinations,the higher the wave impedance of the projectile,the higher the initial phase transition velocity and the smaller the mass of phase transition.The results can provide theoretical guidance for kinetic impactor design and material selection.

Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network

Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trainedmachine learning algorithms.In this study,a transfer learningenhanced convolutional neural network(CNN)was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge.The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy.First of all,a CNN algorithm for bridge weigh-in-motion(B-WIM)technology was proposed to identify the axle weight and the gross weight of the typical two-axle,three-axle,and five-axle vehicles as they crossed the bridge with different loading routes and speeds.Then,the pre-trained CNN model was transferred by fine-tuning to weigh themoving vehicle on another bridge.Finally,the identification accuracy and the amount of training data required were compared between the two CNN models.Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%.Moreover,the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model,showing its promising potentials in the actual applications.

Optimal and robust control of population transfer in asymmetric quantum-dot molecules

We present an optimal and robust quantum control method for efficient population transfer in asymmetric double quantum-dot molecules.We derive a long-duration control scheme that allows for highly efficient population transfer by accurately controlling the amplitude of a narrow-bandwidth pulse.To overcome fluctuations in control field parameters,we employ a frequency-domain quantum optimal control theory method to optimize the spectral phase of a single pulse with broad bandwidth while preserving the spectral amplitude.It is shown that this spectral-phase-only optimization approach can successfully identify robust and optimal control fields,leading to efficient population transfer to the target state while concurrently suppressing population transfer to undesired states.The method demonstrates resilience to fluctuations in control field parameters,making it a promising approach for reliable and efficient population transfer in practical applications.

Balancing electron transfer and intermediate adsorption ability of metallic Ni-Fe-RE-P bifunctional catalysts via 4f-2p-3d electron interaction for enhanced water splitting

Balancing electron transfer and intermediate adsorption ability of bifunctional catalysts via tailoring electronic structures is crucial for green hydrogen production,while it still remains challenging due to lacking efficient strategies.Herein,one efficient and universal strategy is developed to greatly regulate electronic structures of the metallic Ni-Fe-P catalysts via in-situ introducing the rare earth(RE)atoms(Ni-Fe-RE-P,RE=La,Ce,Pr,and Nd).Accordingly,the as-prepared optimal Ni-Fe-Ce-P/CC self-supported bifunctional electrodes exhibited superior electrocatalytic activity and excellent stability with the low overpotentials of 247 and 331 mV at 100 mA cm^(-2) for HER and OER,respectively.In the assembled electrolyzer,the Ni-Fe-Ce-P/CC as bifunctional electrodes displayed low operation potential of 1.49 V to achieve a current density of 10 mA cm^(-2),and the catalytic performance can be maintained for 100 h.Experimental results combined with density functional theory(DFT)calculation reveal that Ce doping leads to electron decentralization and crystal structure distortion,which can tailor the band structures and d-band center of Ni-Fe-P,further increasing conductivity and optimizing intermediate adsorption energy.Our work not only proposes a valuable strategy to regulate the electron transfer and intermediate adsorption of electrocatalysts via RE atoms doping,but also provides a deep under-standing of regulation mechanism of metallic electrocatalysts for enhanced water splitting.

Numerical and theoretical study of load transfer behavior during cascading pillar failure

To further study the load transfer mechanism of roofemulti-pillarefloor system during cascading pillar failure(CPF),numerical simulation and theoretical analysis were carried out to study the three CPF modes according to the previous experimental study on treble-pillar specimens,e.g.successive failure mode(SFM),domino failure mode(DFM)and compound failure mode(CFM).Based on the finite element code rock failure process analysis(RFPA^(2D)),numerical models of treble-pillar specimen with different mechanical properties were established to reproduce and verify the experimental results of the three CPF modes.Numerical results show that the elastic rebound of roofefloor system induced by pillar instability causes dynamic disturbance to adjacent pillars,resulting in sudden load increases and sudden jump displacement of adjacent pillars.The phenomena of load transfer in the roofemulti-pillarefloor system,as well as the induced accelerated damage behavior in adjacent pillars,were discovered and studied.In addition,based on the catastrophe theory and the proposed mechanical model of treble-pillar specimen edisc spring group system,a potential function that characterizes the evolution characteristics of roof emulti-pillarefloor system was established.The analytical expressions of sudden jump and energy release of treble-pillar specimenedisc spring group system of the three CPF modes were derived according to the potential function.The numerical and theoretical results show good agreement with the experimental results.This study further reveals the physical essence of load transfer during CPF of roof emulti-pillarefloor system,which provides references for mine design,construction and disaster prevention.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

A Simplified Method for the Stress Analysis of Underground Transfer Structures Crossing Multiple Subway Tunnels

According to the design specifications,the construction of extended piles involves traversing the tunnel’s upper region and extending to the underlying rock layer.To address this challenge,a subterranean transfer structure spanning multiple subway tunnels was proposed.Deliberating on the function of piles in the transfer structure as springs with axial and bending stiffness,and taking into account the force balance and deformation coordination conditions of beams and plates within the transfer structure,we established a simplified mechanical model that incorporates soil stratification by combining it with the Winkler elastic foundation beam model.The resolved established simplifiedmechanicalmodel employed finite difference technology and the Newton-Simpsonmethod,elucidating the mechanical mechanism of the transfer structure.The research findings suggest that the load carried by the upper structural columns can be transferred to the pile foundation beneath the beams through the transfer structure,subsequently reaching the deep soil layer and ensuring minimal impact on adjacent tunnels.The established simplified analysis method can be used for stress analysis of the transfer structure,concurrently considering soil stratification,pile foundation behavior,and plate action.The pile length,pile section size,and beam section size within the transfer structure should account for the characteristics of the upper load,ensuring an even distribution of the beam bending moment.

Impact of a Magnetic Dipole on Heat Transfer in Non-Conducting Magnetic Fluid Flow over a Stretching Cylinder

The thermal behavior of an electrically non-conducting magnetic liquid flowing over a stretching cylinder under the influence of a magnetic dipole is considered.The governing nonlinear differential equations are solved numerically using a finite element approach,which is properly validated through comparison with earlier results available in the literature.The results for the velocity and temperature fields are provided for different values of the Reynolds number,ferromagnetic response number,Prandtl number,and viscous dissipation parameter.The influence of some physical parameters on skin friction and heat transfer on the walls of the cylinder is also investigated.The applicability of this research to heat control in electronic devices is discussed to a certain extent.

Transfer film effects induced by 3D-printed polyether-ether-ketone with excellent tribological properties for joint prosthesis

Based on the building principle of additive manufacturing,printing orientation mainly determines the tribological properties of joint prostheses.In this study,we created a polyether-ether-ketone(PEEK)joint prosthesis using fused filament fabrication and investigated the effects of printing orientation on its tribological properties using a pin-on-plate tribometer in 25% newborn calf serum.An ultrahigh molecular weight polyethylene transfer film is formed on the surface of PEEK due to the mechanical capture of wear debris by the 3D-printed groove morphology,which is significantly impacted by the printing orientation of PEEK.When the printing orientation was parallel to the sliding direction of friction,the number and size of the transfer film increased due to higher steady stress.This transfer film protected the matrix and reduced the friction coefficient and wear rate of friction pairs by 39.13%and 74.33%,respectively.Furthermore,our findings provide a novel perspective regarding the role of printing orientation in designing knee prostheses,facilitating its practical applications.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Causal Analysis Between Rice Growth and Cadmium Accumulation and Transfer under Arbuscular Mycorrhizal Inoculation

Cadmium(Cd)contamination in rice has been a serious threat to human health.To investigate the effects of arbuscular mycorrhizal fungi(AMF)on the Cd translocation in rice,a controlled pot experiment was conducted.The results indicated that AMF significantly increased rice biomass,with an increase of up to 40.0%,particularly in root biomass by up to 68.4%.Notably,the number of prominent rice individuals also increased,and their plasticity was enhanced following AMF inoculation.AMF led to an increase in the net photosynthetic rate and antioxidant enzyme activity of rice.In the AMF treatment group,the Cd concentration in the rice roots was significantly higher(19.1%‒68.0%)compared with that in the control group.Conversely,the Cd concentration in the rice seeds was lower in the AMF treatment group,indicating that AMF facilitated the sequestration of Cd in rice roots and reduced Cd accumulation in the seeds.Path coefficients varied across different treatments,suggesting that AMF inoculation reduced the direct impact of soil Cd concentration on the total Cd accumulation in seeds.The translocation of Cd was consistently associated with simultaneous growth dilution and compensatory accumulation as a result of mycorrhizal effects.Our study quantitatively analyzed this process through path analysis and clarified the causal relationship between rice growth and Cd transfer under the influence of AMF.

Improving Heat Transfer in Parabolic Trough Solar Collectors by Magnetic Nanofluids

A parabolic trough solar collector(PTSC)converts solar radiation into thermal energy.However,low thermal efficiency of PTSC poses a hindrance to the deployment of solar thermal power plants.Thermal performance of PTSC is enhanced in this study by incorporating magnetic nanoparticles into the working fluid.The circular receiver pipe,with dimensions of 66 mm diameter,2 mm thickness,and 24 m length,is exposed to uniform temperature and velocity conditions.The working fluid,Therminol-66,is supplemented with Fe3O4 magnetic nanoparticles at concentrations ranging from 1%to 4%.The findings demonstrate that the inclusion of nanoparticles increases the convective heat transfer coefficient(HTC)of the PTSC,with higher nanoparticle volume fractions leading to greater heat transfer but increased pressure drop.The thermal enhancement factor(TEF)of the PTSC is positively affected by the volume fraction of nanoparticles,both with and without a magnetic field.Notably,the scenario with a 4%nanoparticle volume fraction and a magnetic field strength of 250 G exhibits the highest TEF,indicating superior thermal performance.These findings offer potential avenues for improving the efficiency of PTSCs in solar thermal plants by introducing magnetic nanoparticles into the working fluid.

Personality Trait Detection via Transfer Learning

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.

Near-field radiative heat transfer between nanoporous GaN films

Photon tunneling effects give rise to surface waves,amplifying radiative heat transfer in the near-field regime.Recent research has highlighted that the introduction of nanopores into materials creates additional pathways for heat transfer,leading to a substantial enhancement of near-field radiative heat transfer(NFRHT).Being a direct bandgap semiconductor,GaN has high thermal conductivity and stable resistance at high temperatures,and holds significant potential for applications in optoelectronic devices.Indeed,study of NFRHT between nanoporous GaN films is currently lacking,hence the physical mechanism for adding nanopores to GaN films remains to be discussed in the field of NFRHT.In this work,we delve into the NFRHT of GaN nanoporous films in terms of gap distance,GaN film thickness and the vacuum filling ratio.The results demonstrate a 27.2%increase in heat flux for a 10 nm gap when the nanoporous filling ratio is 0.5.Moreover,the spectral heat flux exhibits redshift with increase in the vacuum filling ratio.To be more precise,the peak of spectral heat flux moves fromω=1.31×10^(14)rad·s^(-1)toω=1.23×10^(14)rad·s^(-1)when the vacuum filling ratio changes from f=0.1 to f=0.5;this can be attributed to the excitation of surface phonon polaritons.The introduction of graphene into these configurations can highly enhance the NFRHT,and the spectral heat flux exhibits a blueshift with increase in the vacuum filling ratio,which can be explained by the excitation of surface plasmon polaritons.These findings offer theoretical insights that can guide the extensive utilization of porous structures in thermal control,management and thermal modulation.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Mass transfer process of peanut protein extracted by bis(2-ethylhexyl)sodium sulfosuccinate reverse micelles

The liquid-liquid extraction method using reverse micelles can simultaneously extract lipid and protein of oilseeds,which have become increasingly popular in recent years.However,there are few studies on mass transfer processes and models,which are helpful to better control the extraction process of oils and proteins.In this paper,mass transfer process of peanut protein extracted by bis(2-ethylhexyl)sodium sulfosuccinate(AOT)/isooctane reverse micelles was investigated.The effects of stirring speed(0,70,140,and 210 r/min),temperature of extraction(30,35,40,45,and 50℃),peanut flour particle size(0.355,0.450,0.600,and 0.900 mm)and solidliquid ratio(0.010,0.0125,0.015,0.0175,and 0.020 g/mL)on extraction rate were examined.The results showed that extraction rate increased with temperature rising,particle size reduction as well as solid-liquid ratio increase respectively,while little effect of stirring speed(P>0.05)was observed.The apparent activation energy of extraction process was calculated as 10.02 kJ/mol and Arrhenius constant(A)was 1.91 by Arrhenius equation.There was a linear relationship between reaction rate constant and the square of the inverse of initial particle radius(1/r_(0)^(2))(P<0.05).This phenomenon and this shrinking core model were anastomosed.In brief,the extraction process was controlled by the diffusion of protein from the virgin zone interface of particle through the reacted zone and it was in line with the first order reaction.Mass transfer kinetics of peanut protein extracted by reverse micelles was established and it was verified by experimental results.The results provide an important theoretical guidance for industrial production of peanut protein separation and purification.

Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy.World Health Organization(WHO)and many others advised controlling Corona Virus Disease in 2019.The limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to unfold.Among all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)droplets.All countries made masks mandatory to prevent infection.For such enforcement,automatic and effective face detection systems are crucial.This study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without masks.To contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or organization.The paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection localization.The experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced architecture.The proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing models.This outstanding work also can be used in video surveillance applications.

Transfer matrix method for free and forced vibrations of multi-level functionally graded material stepped beams with different boundary conditions

Functionally graded materials(FGMs)are a novel class of composite materials that have attracted significant attention in the field of engineering due to their unique mechanical properties.This study aims to explore the dynamic behaviors of an FGM stepped beam with different boundary conditions based on an efficient solving method.Under the assumptions of the Euler-Bernoulli beam theory,the governing differential equations of an individual FGM beam are derived with Hamilton’s principle and decoupled via the separation-of-variable approach.Then,the free and forced vibrations of the FGM stepped beam are solved with the transfer matrix method(TMM).Two models,i.e.,a three-level FGM stepped beam and a five-level FGM stepped beam,are considered,and their natural frequencies and mode shapes are presented.To demonstrate the validity of the method in this paper,the simulation results by ABAQUS are also given.On this basis,the detailed parametric analyses on the frequencies and dynamic responses of the three-level FGM stepped beam are carried out.The results show the accuracy and efficiency of the TMM.