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.

Common Cuckoo(Cuculus canorus)nestlings adapt their begging behavior to the host signal system

Common Cuckoos(Cuculus canorus)dependent on parental care for post-hatching demonstrate an intriguing ability to modify their begging vocalizations to ensure maximum care and resources from their interspecific foster parents.Here,we compared begging calls of the Common Cuckoo nestlings fed by four host species,the Grey Bushchat(Saxicola ferreus),Siberian Stonechat(Saxicola maurus),Daurian Redstart(Phoenicurus auroreus),and Oriental Magpie-robin(Copsychus saularis).Results showed that begging calls of the stonechat-,redstart-,and robin-cuckoo resemble those of host species'nestlings in various aspects like low frequency,high frequency,frequency bandwidth and peak frequency,while the bushchat-cuckoo chicks'begging calls were only comparable to their host species in terms of how long they lasted and their peak frequency.In addition,cuckoo nestlings raised in different host nests displayed significant variations in their begging calls in low and peak frequency.This study suggests that cuckoo nestlings do not mimic host species nestlings'begging calls throughout the nestling period,but may tune their begging calls according to host species,while begging calls vary with cuckoo and host species nestlings'ages.Future research should study the parents'reactions to these calls in different host species for a better understanding of the mechanisms underlying such adaptations.

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.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

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.

Research on MBSE Application for Radio Navigation System

The rapid development of concepts and technologies for civil aircraft navigation systems has put forward higher requirements for agile iteration and integrated design verification in the research and development(R&D)process.Traditional document based system engineering(DBSE)methods have gradually become inadequate.Model based system engineering(MBSE)is fully based on user′s needs and is carried out from top to bottom,in line with the concept of forward design.It is gradually being applied in the development of civil aircraft systems.This article focuses on the civil aircraft radio navigation system and proposes a complete system engineering solution based on models,from system design and development to validation.Guided by the Arcadia methodology,with Capella modeling tool,Simulink simulation tool,and system validation tool,the complete R&D process from design and development to testing and validation was achieved through model construction,code generation,and testing validation.A radio navigation station selection optimization method based on machine learning was proposed,and results had good signal quality and persistence.The verification result of Beijing⁃Shanghai flight route shows MBSE method practiced in this article can ensure the feasibility of the entire process of radio navigation system development,as well as the rationality of tuning and positioning result.By automatically generating code to form a universal functional module,an optimization method that integrates different radio navigation station selection strategies is achieved,providing new ideas for the development and design of radio navigation systems.

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.

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.

Investigation Responses of the Diagrid Structural System of High-rise Buildings Equipped with Tuned Mass Damper Using New Dynamic Method

Due to the shortage of land in cities and population growth,the significance of high rise buildings has risen.Controlling lateral displacement of structures under different loading such as an earthquake is an important issue for designers.One of the best systems is the diagrid method which is built with diagonal elements with no columns for manufacturing tall buildings.In this study,the effect of the distribution of the tuned mass damper(TMD)on the structural responses of diagrid tall buildings was investigated using a new dynamic method.So,a diagrid structural systems with variable height with TMDs was solved as an example of structure.The reason for the selection of the diagrid system was the formation of a stiffness matrix for the diagonal and angular elements.Therefore,the effect of TMDs distribution on the story drift,base shear and structural behaviour were studied.The obtained outcomes showed that the TMDs distribution does not significantly affect on improving the behaviour of the diagrid structural system during an earthquake.Furthermore,the new dynamic scheme represented in this study has good performance for analyzing different systems.Abbreviation:TMD-tuned mass damper;SATMD-semiactive-tuned mass dampers;MDOF-multiple degrees of freedom;m_(i)-mass of ith story of the building;c_(i)-damping coefficient of the ith story of the building;k_(i)-stiffness of ith story of the building;x_(i)-displacement of the ith story of the building;md-mass of damper;c_(d)-damping coefficient of the damper;k_(d)-stiffness of damper;x_(d)-displacement of TMD;M_(i)-generalized mass of the ith normal mode;C_(i)-generalized damping of the ith normal mode;K_(i)-generalized stiffness of the ith normal mode;K_(i)(t)-generalized load of the ith normal mode;Y_(i)(t)-generalized displacement of the ith normal mode;[M]-matrices of mass;[C]-matrices of damping;{P(t)}-consequence external forces;N_(i)(τ)-interpolation functions;[Ai]-mechanical properties of the structure.

Experimental Studies of the Practicability of a New Type Tuned Mass Damper System

Based on the principle of Tuned Mass Damper (TMD),the test of a new quake reduction system was investigated.The main structure of the system is connected with the top floor through Laminated Rubber Bearing (LRB) to make up a huge TMD system suspended structure. It was shown from the test that the new TMD quake reduction system can reduce the acceleration of the top floor by more than one quarter if the parameters are chosen efficiently.Since the good effectiveness and easy availability, this system has the practical value in earth quake engineering.

Research on Practicability and Feasibility for a New-Type Tuned Mass Damper System-TMD

Based on the principle of tuned mass damper (TMD). the method of using laminated rubber bearing (LRB) to connect TMD with structure is discussed in this paper. This is a new type of TMD system-suspended structure. To test the function of quake-reduction and the possibility of application, this paper explores the suspended top floor through shaking table test. In the model test, an electro-hydraulic shaking table was used. The main structure model was a four-story steel frame structure. The block to combat the structural quake was a concrete block. LRB was used to connect the block to the main structure. In order to analyze the efficiency of TMD, the fundamental frequencies of the main structure and block of TMD were measured separately first. Then. the frequencies of the main structure with the block and without the block were compared respectively under sine and imitative quake waves. The test shows that this new-typeTMD system is effective in combating the structural quake often reducing the acceleration of the top floor by more than 25 %. Because of the easy availability of the method, it is endowed with practical feasibility.

Location optimization for the tuned mass damper system on a long-span floor

The L4 roof of Beijing Olympic International Conference Center is a long-span floor with a tuned mass damper system. The locations of dampers in the layout are not optimal theoretically. This paper is about the location optimization of the 74 sets of dampers on the floor. The main content includes the establishment of a 2D dot-matrix model for the structure, the optimal location combination searched by a genetic algorithm, and the optimal results for five working conditions by calculating the total weight.

Left-Handedness with Three Zero-Absorption Windows Tuned by the Incoherent Pumping Field and Inter-Dot Tunnelings in a GaAs/AlGaAs Triple Quantum Dots System

Left-handedness with three zero-absorption windows is achieved in a triple-quantum-dot system. With the typ- ical parameters of a GaAs/AlGaAs heterostructure, the simultaneous negative relative electric permittivity and magnetic permeability are obtained by the adjustable incoherent pumping field and two inter-dot tunnelings. Furthermore, three zero-absorption windows in the left-handedness frequency bands are observed. The left- handedness with zero-absorption in the solid state heterostrueture may solve the challenges not only in the left-handed materials achieved by the photonic resonant scheme but also in the application of negative refractive materials with a large amount of absorption.

Analysis of Impact of Tuned Damper Floating Fastener on Track System

Ground-borne noise caused by track vibration became one of major problem affecting urban rail transit development. Resilient rail fasteners come to be a main measure of reducing ground-borne noise. Low stiffness of fastener contributes to ensuring the good effect of vibration isolation, but causing higher vibration of rails, while the tuned damper fastener can make it up. In this paper, the simulation of rail receptance is used to fit on site measured data, then the data are used to build a vehicle-track-foundation model to do the calculations, like vibration reduction of track systems and rails. According to the results, the tuned damper fastener can reduce the vibration of slab as well as rails.

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.

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.

Intelligent Deep Learning Enabled Wild Forest Fire Detection System

The latest advancements in computer vision and deep learning(DL)techniques pave the way to design novel tools for the detection and monitoring of forestfires.In this view,this paper presents an intelligent wild forestfire detec-tion and alarming system using deep learning(IWFFDA-DL)model.The pro-posed IWFFDA-DL technique aims to identify forestfires at earlier stages through integrated sensors.The proposed IWFFDA-DL system includes an Inte-grated sensor system(ISS)combining an array of sensors that acts as the major input source that helps to forecast thefire.Then,the attention based convolution neural network with bidirectional long short term memory(ACNN-BLSTM)model is applied to examine and identify the existence of danger.For hyperpara-meter tuning of the ACNN-BLSTM model,the bacterial foraging optimization(BFO)algorithm is employed and thereby enhances the detection performance.Finally,when thefire is detected,the Global System for Mobiles(GSM)modem transmits messages to the authorities to take required actions.An extensive set of simulations were performed and the results are investigated interms of several aspects.The obtained results highlight the betterment of the IWFFDA-DL techni-que interms of various measures.

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.

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.