Improved isochronous mass spectrometry with tune measurement

In conventional isochronous mass spectrometry(IMS)performed on a storage ring,the precision of mass measurements for short-lived nuclei depends on the accurate determination of the revolution times(T)of stored ions.However,the resolution of T inevitably deteriorates due to the magnetic rigidity spread of the ions,limiting the mass-resolving power.In this study,we used the betatron tunes Q(the number of betatron oscillations per revolution)of the ions and established a correlation between T and Q.From this correlation,T was transformed to correspond to a fixed Q with higher resolution.Using these transformed T values,the masses of ^(63)Ge,^(65)As,^(67)Se,and ^(71)Kr agreed well with the mass values measured using the newly developed IMS(Bρ-IMS).We also studied the systematics of Coulomb displacement energies(CDEs)and found that anomalous staggering in CDEs was eliminated using new mass values.This method of T transformation is highly effective for conventional IMS equipped with a single time-of-flight detector.

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.

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.

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.

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.

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.

基于领域大语言模型的古籍分词研究

[目的/意义]文章以古籍自动分词为切入点,引入“荀子”系列大语言模型,对大语言模型在古籍文本分词任务上的表现进行了探讨。[方法/过程]文章基于《左传》分词语料,进行了数据清洗和整理,构建了指令数据集,在此基础上,从数据集中抽取了1 000条作为测试数据,并分别使用500、1 000、2 000、5 000条数据作为训练数据进行指令微调,并测试其性能。[结果/结论]实验结果表明,只需要少量的数据,大语言模型就可以有较为理想的表现,在微调数据量达到5 000条数据时,Xunzi-Qwen-7B模型表现出了最优性能,F1值达到84.54%。

基于机器学习的数据库系统参数优化方法综述

参数优化是影响数据库性能和适应性的关键技术,合理的参数配置对于保障数据库系统的高效运行至关重要,但由于参数较多且参数间具有强关联性,传统参数优化方法难以在高维连续的参数空间中寻找最优配置,机器学习的发展为解决这一难题带来新的机遇。通过总结和分析相关工作,将已有工作按照发展时间和特性分为专家决策、静态规则、启发式算法、传统机器学习方法和深度强化学习方法。对数据库参数优化问题进行定义,并说明启发式算法在参数优化问题上的局限性。介绍基于传统机器学习的参数优化方法,包括随机森林、支持向量机、决策树等,描述机器学习方法解决参数优化问题的一般流程并给出一般实现。由于需要大量带标注的数据,传统机器学习模型在适应性和调优能力等方面存在不足。侧重介绍深度强化学习模型的工作原理,定义参数优化问题与深度强化学习模型的映射关系,比较基于深度强化学习的相关工作对数据库性能提升、模型训练时间和涉及的技术,描述基于深度神经网络构建和训练智能体的具体流程。最后,总结已有工作的特点,对当前机器学习在数据库参数优化方面的研究热点和发展方向进行展望,指出多粒度调优、自适应算法和自运维是未来的研究趋势。

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.

领域大语言模型下的古籍词性标注应用研究

[目的/意义]大语言模型的发展为古籍文本挖掘带来了新的思路,将大语言模型与古籍数字化、智能化相结合是新时代古籍工作的必经之路。[方法/过程]文章使用《左传》词性标注语料,通过数据清洗和预处理,构建了一批高质量的词性标注指令数据,在此基础上,分别使用500、1 000、2 000、5 000条数据对大语言模型进行指令微调,并在另外1 000条数据上进行性能测试。[结果/结论]实验结果表明,“荀子”系列模型在古籍文本词性标注任务上性能优于通用领域模型,在微调数据量达到5 000时,Xunzi-Baichuan2-7B模型表现出了最优性能,F1值达到81.67%。

LCC-MMC混合三端直流输电系统送端交流故障下的不间断运行协调控制策略

为实现基于电网换相换流器与模块化多电平换流器(LCC-MMC)的混合三端直流输电系统送端交流故障下的直流低电压穿越,提出兼顾传输容量与响应速度的自适应电压协调控制策略及有功功率分配策略。在维持故障期间功率续传的前提下,定量分析了模块化多电平换流器(MMC)的降压值以减少传输功率的绝对值损失量,并设计MMC根据本地直流电流偏差快速减投子模块总数的降压方式;考虑到半桥型MMC的调制比约束,设计正极MMC定量吸收无功功率与负极MMC动态调整交流电压参考值的换流站极间协同控制策略;同时,为抑制从站的过电流及避免送端严重交流故障时主站的潮流反转,提出各受端换流站有功功率自适应调整的控制方式。最后通过对输电系统送端交流电压跌落不同幅度时的故障穿越效果进行仿真分析,验证了所提控制策略的有效性。

基于PI参数的二阶线性自抗扰控制参数整定

线性自抗扰控制是一种不依赖于被控过程模型的控制方法,具有很好的工程应用潜力。目前,工业控制过程中大多采用PI控制器,为了确保由调试好的PI控制器平稳切换到线性自抗扰控制器,并使系统仍保持稳定状态,需要合理设置线性自抗扰控制器的参数。为此,在分析二阶线性自抗扰控制器的二自由度等效结构的基础上,推导出其反馈控制器与PID控制器的对应关系,给出一种基于现有PI控制参数直接获取二阶线性自抗扰控制初始参数的方法。最后,在MATLAB/Simulink平台上采用若干典型传递函数和双容水箱液位控制系统进行仿真研究,仿真结果验证了所提方法的有效性。

基于RoBERTa和T5的两阶段医学术语标准化

医学术语标准化作为消除实体歧义性的重要手段,被广泛应用于知识图谱的构建过程之中.针对医学领域涉及大量的专业术语和复杂的表述方式,传统匹配模型往往难以达到较高的准确率的问题,提出语义召回加精准排序的两阶段模型来提升医学术语标准化效果.首先在语义召回阶段基于改进的有监督对比学习和RoBERTa-wwm提出语义表征模型CL-BERT,通过CL-BERT生成实体的语义表征向量,根据向量之间的余弦相似度进行召回并得到标准词候选集,其次在精准排序阶段使用T5结合prompt tuning构建语义精准匹配模型,并将FGM对抗训练应用到模型训练中,然后使用精准匹配模型对原词和标准词候选集分别进行精准排序得到最终标准词.采用ccks2019公开数据集进行实验,F1值达到了0.9206,实验结果表明所提出的两阶段模型具有较高的性能,为实现医学术语标准化提供了新思路.

基于多目标灰狼算法的漂浮式风电机组浮台内TMD参数优化

针对漂浮式风电机组浮台内调谐质量阻尼器(TMD)参数调优的问题,以5 MW Barge型漂浮式风电机组为研究对象,采用多目标灰狼算法(MOGWO)优化TMD参数配置。首先,基于欧拉-拉格朗日方程建立浮台内含TMD的漂浮式风电机组动力学模型,采用Levenberg-Marquardt(LM)法进行模型未知参数辨识;其次,同时考虑塔顶和塔基控制目标,采用MOGWO算法优化TMD的刚度和阻尼参数;最后,在不同工况下进行仿真分析。结果表明:相对于传统的单目标优化算法,使用MOGWO算法参数优化后的TMD对风电机组具有更好的振动抑制效果。

近断层脉冲型地震动下调谐黏滞质量阻尼器减震结构易损性分析

选择典型脉冲型近断层地震动记录分别对一安装有调谐黏滞质量阻尼器(tuned viscous mass damper,TVMD)、黏滞阻尼器(viscous damper, VD)和无阻尼器的6层3跨钢框架结构进行增量动力分析,进而基于增量动力分析结果得到3种结构不同损伤状态的易损性曲线。易损性分析结果对比表明:随着惯容单元的引入,减震结构可靠性显著提升;相较于相同阻尼的VD,TVMD可以更有效地提升结构的抗震性能,减小结构在不同损伤状态下的失效概率。随着质量比的增加,TVMD减震结构在不同损伤状态下的失效概率减小,TVMD对结构性能状态的提升不会随非线性的增加而减小,且在结构经历弹性、弹塑性直至倒塌的整个过程中减震控制性能发挥稳定。进一步选取3组不同脉冲周期的近断层地震动记录对三种结构进行时程分析,讨论TVMD在不同脉冲周期近断层地震动下的减震控制效果。结果表明,在近断层脉冲型地震动作用下,TVMD并不都会实现阻尼单元“阻尼增效”,阻尼器的性能发挥与地震动的特性有关。相较于VD,当T_1/T_p≥2时,TVMD可以更好地减少结构能量输入但其能量耗散效率低于VD,不能实现“阻尼增效”;当T_1/T_p <2时,TVMD可以减小结构残余位移,具有更优的减少能量输入和增大能量耗散效率的双重效应,体现了惯容的“阻尼增效”。