Vibration attenuation performance of wind turbine tower using a prestressed tuned mass damper under seismic excitation

With the rapid development of large megawatt wind turbines,the operation environment of wind turbine towers(WTTs)has become increasingly complex.In particular,seismic excitation can create a resonance response and cause excessive vibration of the WTT.To investigate the vibration attenuation performance of the WTT under seismic excitations,a novel passive vibration control device,called a prestressed tuned mass damper(PS-TMD),is presented in this study.First,a mathematical model is established based on structural dynamics under seismic excitation.Then,the mathematical analytical expression of the dynamic coefficient is deduced,and the parameter design method is obtained by system tuning optimization.Next,based on a theoretical analysis and parameter design,the numerical results showed that the PS-TMD was able to effectively mitigate the resonance under the harmonic basal acceleration.Finally,the time-history analysis method is used to verify the effectiveness of the traditional pendulum tuned mass damper(PTMD)and the novel PS-TMD device,and the results indicate that the vibration attenuation performance of the PS-TMD is better than the PTMD.In addition,the PS-TMD avoids the nonlinear effect due to the large oscillation angle,and has the potential to dissipate hysteretic energy under seismic excitation.

Parameters Optimization and Performance Evaluation of the Tuned Inerter Damper for the Seismic Protection of Adjacent Building Structures

In order to improve the seismic performance of adjacent buildings,two types of tuned inerter damper(TID)damping systems for adjacent buildings are proposed,which are composed of springs,inerter devices and dampers in serial or in parallel.The dynamic equations of TID adjacent building damping systems were derived,and the H2 norm criterion was used to optimize and adjust them,so that the system had the optimum damping performance under white noise random excitation.Taking TID frequency ratio and damping ratio as optimization parameters,the optimum analytical solutions of the displacement frequency response of the undamped structure under white noise excitation were obtained.The results showed that compared with the classic TMD,TID could obtain a better damping effect in the adjacent buildings.Comparing the TIDs composed of serial or parallel,it was found that the parallel TIDs had more significant advantages in controlling the peak displacement frequency response,while the H2 norm of the displacement frequency response of the damping system under the coupling of serial TID was smaller.Taking the adjacent building composed of two ten-story frame structures as an example,the displacement and energy collection time history analysis of the adjacent building coupled with the optimum design parameter TIDs were carried out.It was found that TID had a better damping effect in the full-time range compared with the classic TMD.This paper also studied the potential power of TID in adjacent buildings,which can be converted into available power resources during earthquakes.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

超高层Tuned Mass Damper防震支撑系统技术研究应用

超高层建筑是现代城市建设的重要标志之一,其高度已经超过了传统建筑的极限。然而,随着建筑高度不断增加,地震的破坏力也越来越强,超高层建筑面临着更加严峻的安全挑战。因此,研究超高层建筑防震支撑系统技术非常重要,Tuned Mass Damper(TMD)是一种被广泛研究和应用的超高层建筑防震支撑系统技术,TMD最初是在20世纪60年代提出的,最早应用于桥梁上,后来,TMD被引入到建筑领域,并得到广泛的应用。通过精确调节质量、阻尼和弹性等参数来削弱地震引起的建筑物减震效应,从而减少了建筑物因地震造成的损害和崩塌的风险.

Variable stiffness tuned particle dampers for vibration control of cantilever boring bars

This research proposes a novel type of variable stiffness tuned particle damper(TPD)for reducing vibrations in boring bars.The TPD integrates the developments of particle damping and dynamical vibration absorber,whose frequency tuning principle is established through an equivalent theoretical model.Based on the multiphase flow theory of gas-solid,it is effective to obtain the equivalent damping and stiffness of the particle damping.The dynamic equations of the coupled system,consisting of a boring bar with the TPD,are built by Hamilton’s principle.The vibration suppression of the TPD is assessed by calculating the amplitude responses of the boring bar both with and without the TPD by the Newmark-beta algorithm.Moreover,an improvement is proposed to the existing gas-solid flow theory,and a comparative analysis of introducing the stiffness term on the damping effect is presented.The parameters of the TPD are optimized by the genetic algorithm,and the results indicate that the optimized TPD effectively reduces the peak response of the boring bar system.

A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue.The breakdown of coronary cardiovascular disease is one of the principal sources of death all over theworld.Cardiovascular deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained professionals.Since heart diseases happen without apparent signs,high-level detection is desirable.This paper proposed a robust and tuned random forest model using the randomized grid search technique to predictCAD.The proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between them.Nowadays,the healthcare industry has a lot of data but needs to gain more knowledge.Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham datasets.The datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection technique.Secondly,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two datasets.Finally,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)model.The randomized grid search selected the best parameters and got the ideal CAD analysis.The proposed framework was evaluated and compared with traditional classifiers.Our proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

Novel Double-Damped Tuned AC Filters in HVDC Systems

This paper presents a performance analysis of novel doubledampedtuned alternating current (AC) filters in high voltage direct current(HVDC) systems. The proposed double-damped tuned AC filters offer theadvantages of improved performance of HVDC systems in terms of betterpower quality, high power factor, and lower total harmonic distortion (THD).The system under analysis consists of an 878 km long HVDC transmissionline connecting converter stations at Matiari and Lahore, two major cities inPakistan. The main focus of this research is to design a novel AC filter usingthe equivalent impedance method of two single-tuned and double-dampedtuned AC filters. Additionally, the impact of the damping resistor on the ACchannel is examined. TheTHDof theHVDCsystem with and without currentAC filters was also compared in this research and a double-damped tuned ACfilter was proposed. The results of the simulation represent that the proposeddouble-damped tuned AC filter is far smaller in size, offers better powerquality, and has a much lower THD compared to the AC filters currently inplace in the converter station. The simulation analysis was carried out utilizingpower systems computer-aided design (PSCAD) software.

Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

The text classification process has been extensively investigated in various languages,especially English.Text classification models are vital in several Natural Language Processing(NLP)applications.The Arabic language has a lot of significance.For instance,it is the fourth mostly-used language on the internet and the sixth official language of theUnitedNations.However,there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic language.In general,researchers face two challenges in the Arabic text classification process:low accuracy and high dimensionality of the features.In this study,an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning(AATC-HTHDL)model is proposed.The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text.The first step in the proposed model is to pre-process the input data to transform it into a useful format.The Term Frequency-Inverse Document Frequency(TF-IDF)model is applied to extract the feature vectors.Next,the Convolutional Neural Network with Recurrent Neural Network(CRNN)model is utilized to classify the Arabic text.In the final stage,the Crow Search Algorithm(CSA)is applied to fine-tune the CRNN model’s hyperparameters,showing the work’s novelty.The proposed AATCHTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.

Energy Efficient Hyperparameter Tuned Deep Neural Network to Improve Accuracy of Near-Threshold Processor

When it comes to decreasing margins and increasing energy effi-ciency in near-threshold and sub-threshold processors,timing error resilience may be viewed as a potentially lucrative alternative to examine.On the other hand,the currently employed approaches have certain restrictions,including high levels of design complexity,severe time constraints on error consolidation and propagation,and uncontaminated architectural registers(ARs).The design of near-threshold circuits,often known as NT circuits,is becoming the approach of choice for the construction of energy-efficient digital circuits.As a result of the exponentially decreased driving current,there was a reduction in performance,which was one of the downsides.Numerous studies have advised the use of NT techniques to chip multiprocessors as a means to preserve outstanding energy efficiency while minimising performance loss.Over the past several years,there has been a clear growth in interest in the development of artificial intelligence hardware with low energy consumption(AI).This has resulted in both large corporations and start-ups producing items that compete on the basis of varying degrees of performance and energy use.This technology’s ultimate goal was to provide levels of efficiency and performance that could not be achieved with graphics processing units or general-purpose CPUs.To achieve this objective,the technology was created to integrate several processing units into a single chip.To accomplish this purpose,the hardware was designed with a number of unique properties.In this study,an Energy Effi-cient Hyperparameter Tuned Deep Neural Network(EEHPT-DNN)model for Variation-Tolerant Near-Threshold Processor was developed.In order to improve the energy efficiency of artificial intelligence(AI),the EEHPT-DNN model employs several AI techniques.The notion focuses mostly on the repercussions of embedded technologies positioned at the network’s edge.The presented model employs a deep stacked sparse autoencoder(DSSAE)model with the objective of creating a variation-tolerant NT processor.The time-consuming method of modifying hyperparameters through trial and error is substituted with the marine predators optimization algorithm(MPO).This method is utilised to modify the hyperparameters associated with the DSSAE model.To validate that the proposed EEHPT-DNN model has a higher degree of functionality,a full simulation study is conducted,and the results are analysed from a variety of perspectives.This was completed so that the enhanced performance could be evaluated and analysed.According to the results of the study that compared numerous DL models,the EEHPT-DNN model performed significantly better than the other models.

产生“Tuned”模板的Bayesian Networks方法

介绍了Bayesian Networks(简称BNs)产生“Tuned”模板新方法的基本原理以及BNs法与蚁群行为仿真技术和单纯形法组合的方法。通过实际航空影像的实验结果表明,新方法对纹理影像的识别率是令人满意的,同时还将新方法与遗传算法的结果作了对比,结果表明新方法是很有应用前景的。

Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

Tune调制对HIRFL-CSRm动力学孔径影响的模拟研究

通过引进包含tune调制的传输矩阵,模拟计算了由四极铁电源纹波所引起的tune调制对HIRFL-CSRm动力学孔径的影响.模拟计算中,对HIRFL-CSRm实际(lattice)跟踪1.0×106圈.从结果可以看出,动力学孔径随调制振幅的增大迅速减小,大体呈线性变化;动力学孔径随调制tune值的变化在研究范围内也有变化,变化的范围在0.049～0.089 m之间.

HIRFL-CSR Tune值测量系统

兰州重离子加速器冷却储存环(HIRFL-CSR)工作点(Tune)测量系统已投入调束运行。采用FFT测量法,用白噪声信号做激励源,经功率放大并通过同轴开关切换将激励信号加载到垂直或水平的对称极板上,通过位置检测器(BPM)采集束流信号。将BPM两极板感应到的束流信号分别经前置放大器放大,再通过相位相差180°的合路器将差信号送入实时频谱仪进行傅里叶分析,从而计算出Tune值。本文主要介绍HIRFL-CSR工作点测量原理、系统构成以及测量结果。

Seismic Response Reduction for Fixed Offshore Platform by Tuned Liquid Damper

An approach to seismic response reduction for offshore platforms by the use of the tuned liq-uid damper is presented in this paper.First of all,the effects of the tuned frequency ratio and excited fre-quency ratio on the seismic response reduction of the platform structure are investigated.Based on the re-sults,a mechanical model and equation of motion for the TLD-platform system are established.And theneffectiveness of the appraoch is verified by numerical calculation.

Suppression of Wave-Excited Vibration of Offshore Platform by Use of Tuned Liquid Dampers

This paper describes experimental and theoretical investigations of Tuned Liquid Damper (TLD) characteristics forsuppressing the wave-excited structural vibration. The structural model for the experiments is scaled according to a fullsize offshore platform by matching their dynamic properties. Rectangular TLDs of different sizes with partially filled liq-uid are examined. By observing the performance and behavior of TLDs through laboratory experiments, the study inves-tigates the influence of a number of parameters, including container size, container shape, frequency ratio, and incidentwave characteristics. In an analytical study, a mathematical model that describes the nonlinear behavior of liquid inTLD and the interaction of TLD and structure is prerequisite. The validity of the model is evaluated and simulating re-sults can reasonably match the corresponding experimental results.

Control performance comparison between tuned liquid damper and tuned liquid column damper using real-time hybrid simulation

Tuned liquid damper (TLD) and tuned liquid column damper (TLCD) are two types of passive control devices that are widely used in structural control. In this study, a real-time hybrid simulation (RTHS) technique is employed to investigate the diff erence in control performance between TLD and TLCD. A series of RTHSs is presented with the premise of the same liquid length, mass ratio, and structural parameters. Herein, TLD and TLCD are physically experimented, and controlled structures are numerically simulated. Then, parametric studies are performed to further evaluate the diff erent performance between TLD and TLCD. Experimental results demonstrate that TLD is more eff ective than TLCD under diff erent amplitude excitations.

Dynamic Performance Analysis of the Tuned Heave Plate System for Semi-Submersible Platform

Logistical supply is costly for the deepwater oil and gas exploitation, thereby it is necessary to develop a novel power supply solution to improve the offshore structure's self-holding capacity. The two-body point absorbers, as a renewable energy device, have achieved a rapid development. Heave plate is used to constrain the truss' s motion in the two-body point absorber, and the floater moves along the truss up and down. This two-body point absorber can be considered to be an essentially mass-spring-damper system. And it is well known that the heave plates have been widely used in the Spar platform to suppress the heave motions. So if the two-body point absorber can be modified to combine with offshore floating structures, this system can not only offer electric power to support operations or daily lives for the platform, but also control the large motions in the vertical plane. Following this concept, a novel tuned heave plate(THP) system is proposed for the conventional semi-submersible platform. In order to investigate the dynamic performances of the single THP, two experiments are conducted in this paper. First, the hydrodynamic coefficients of the heave plates are studied, and then the THP experiments are carried out to analyze its dynamic performance. It can be concluded that this THP is feasible and achieves the design objective.

Experimental study of vibration mitigation of mast arm signal structures with particle-thrust damping based tuned mass damper

Large amplitude vibration of mast arm structures due to wind loads are the primary contributing factor to the reduced fatigue life of signal support structures. To alleviate this problem of wind-induced in-plane vibration of mast arm signal structures, a particle-thrust damping based turned mass damper(PTD-TMD) device is adopted and its damping effect is characterized experimentally. The particle-thrust damping is a passive damping device that does not require electric power and is temperature independent. Based on the calibration test, an equivalent dynamic model of the PTD-TMD device is developed and used for numerical simulation study. The damping effects of this PTD-TMD device on signal support structures was investigated through both numerical analysis and laboratory testing of a 50-ft(15.24 m) mast arm structure including both free vibration and forced vibration tests. The experimental test and numerical study results show that vibration response behavior of mast arm signal support structures can be significantly reduced by installing the PTD-TMD that can increase the critical damping ratio of the mast arm signal structures to 4%. The stress range at the welded connection between the mast arm and traffic pole is also reduced.

Effect of inertia nonlinearity on dynamic response of an asymmetric building equipped with tuned mass dampers

This study investigates the effect of nonlinear inertia on the dynamic response of an asymmetric building equipped with Tuned Mass Dampers(TMDs).In the field of structural engineering,many researchers have developed models to study the behavior of nonlinear TMDs,but the effect of nonlinear inertia has not received as much attention for asymmetric buildings.To consider nonlinear inertia,the equations of motion are derived in a local rotary coordinates system.The displacements and rotations of the modeled building and TMDs are defined by five-degree-of-freedom(5-DOFs).The equations of motion are derived by using the Lagrangian method.Also in the proposed nonlinear model,the equations of motion are different from a conventional linear model.In order to compare the response of the proposed nonlinear model and a conventional linear model,numerical examples are presented and the response of the modeled buildings are derived under harmonic and earthquake excitations.It is shown that if the nonlinear inertia is considered,the response of the modeled structures changes and the conventional linear approach cannot adequately model the dynamic behavior of the asymmetric buildings which are equipped with TMDs.

Fuzzy-entropy based robust optimization criteria for tuned mass dampers

Tuned mass dampers (TMD) are well known as one of the most widely adopted devices in vibration control passive strategies. In the past few decades,many methods have been developed to find the optimal parameters of a TMD installed on a structure and subjected to a random base excitation process,but most of them are usually based on an implicit assumption that all of the structural parameters are deterministic. However,in many real cases this simplification is unacceptable,so robust optimal design criteria becomes aviable alternative to better support engineers in the design process. In Robust Design Optimization (RDO) approaches,indeed the solution must be able to not only minimize the performance but also to limitits variation induced by uncertainty. Most of the currently available RDO methods are based on a probabilistic description of the model uncertainty,even if in many cases they are not able to explicitly include the influence of all the possible sources of uncertainties. Therefore,in this study,a fuzzy version of the robust TMD design optimization problem is proposed. The consistency of the fuzzy approach is studied with respect to the available non-probabilistic formulations reported in the literature and an application to an example of a robust design of a linear TMD subjected to base random vibrations in the presence of fuzzy uncertainties. The results show that the proposed fuzzy-based approach is able to give a set of optimal solutions both in terms of structural efficiency and sensitivity to mechanical and environmental uncertainties.