Modeling and Current Programmed Control of a Bidirectional Full Bridge DC-DC Converter

Modelling of bidirectional full bridge DC-DC converter as one of the most applicable converters has received significant attention. Mathematical modelling reduces the simulation time in comparison with detailed circuit response;moreover it is convenient for controller design purpose. Due to simple and effective methodology, average state space is the most common method among the modelling methods. In this paper a bidirectional full bridge converter is modelled by average state space and for each mode of operations a controller is designed. Attained mathematical model results are in a close agreement with detailed circuit simulation.

Three-Component Model for Bidirectional Reflection Distribution Function of Thermal Coating Surfaces

We present a bidirectional reflection distribution function （BRDF） model for thermal coating surfaces based on a three-component reflection assumption, in which the specular reflection is given according to the microfacet theory and Snell＇s law, the multiple reflection is considered Nth cosine distributed, and the volume scattering is uniformly distributed in reflection angles according to the experimental results. This model describes the reflection characteristics of thermal coating surfaces more completely and reasonably. Simulation and measurement results of two thermal coating samples SR107 and S781 are given to validate that this three-component model significantly improves the modeling accuracy for thermal coating surfaces compared with the existing BRDF models.

A Novel Bidirectional Interaction Model and Electric Energy Measuring Scheme of EVs for V2G with Distorted Power Loads

With the increasing demand for petroleum resources and environmental issues,new energy electric vehicles are increasingly being used.However,the large number of electric vehicles connected to the grid has brought new challenges to the operation of the grid.Firstly,A novel bidirectional interaction model is established based on modulation theory with nonlinear loads.Then,the electric energy measuring scheme of EVs for V2G is derived under the conditions of distorted power loads.The scheme is composed of fundamental electric energy,fundamental-distorted electric energy,distorted-fundamental electric energy and distorted electric energy.And the characteristics of each electric energy are analyzed.Finally,the correctness of the model and energy measurement method is verified by three simulation cases:the impact signals,the fluctuating signals,and the harmonic signals.

The Korteweg-de Vires equation for the bidirectional pedestrian flow model considering the next-nearest-neighbor effect

This paper focuses on a two-dimensional bidirectional pedestrian flow model which involves the next-nearest-neighbor effect. The stability condition and the Korteweg-de Vries （KdV） equation are derived to describe the density wave of pedestrian congestion by linear stability and nonlinear analysis. Through theoretical analysis, the soliton solution is obtained.

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.

Bottleneck effects on the bidirectional crowd dynamics

The bottleneck effect on bidirectional crowd dynamics is of great theoretical and practical significance, especially for the designing of corridors in public places, such as subway stations or airports. Based on the famous social force model, this paper investigates the bottleneck effects on the free flow dynamics and breakdown phenomenon under different scenarios, in which different corridor shapes and inflow ratios are considered simultaneously. Numerical simulation finds an interesting self-organization phenomenon in the bidirectional flow, a typical characteristic of such a phenomenon is called lane formation, and the existence of which is independent of the corridor＇s shape and inflow rate. However, the pattern of the lane formed by pedestrian flow is related to the corridor＇s shape, and the free flow efficiency has close relationship with the inflow rate. Specifically, breakdown phenomenon occurs when inflows from both sides of the corridor are large enough, which mostly originates from the bottleneck and then gradually spreads to the other regions. Simulation results further indicate that the leaving efficiency becomes low as breakdown occurs, and the degree of congestion is proportional to the magnitude of inflow. The findings presented in this paper match well with some of our daily observations, hence it is possible to use them to provide us with theoretical suggestions in design of infrastructures.

Bayonet-corpus:a trajectory prediction method based on bayonet context and bidirectional GRU

Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.

Isogeometric analysis for free vibration of bidirectional functionally graded plates in the fluid medium

This paper for first time proposes an isogeometric analysis (IGA) for free vibration response of bi-directional functionally graded (BDFG) rectangular plates in the fluid medium. Material properties of the BDFG plate change in both the thickness and length directions via power-law distributions and Mori-Tanaka model. The governing equation of motion of BDFG plate in the fluid-plate system is formulated basing on Hamilton's principle and the refined quasi three-dimensional (3D) plate theory with improved function f(z). The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to determine the added mass. The discrete system of equations is derived from the Galerkin weak form and numerically analyzed by IGA. The accuracy and reliability of the proposed solutions are verified by comparing the obtained results with those published in the literature. Moreover, the effects of the various parameters such as the interaction boundary condition, geometric parameter, submerged depth of plate, fluid density, fluid level, and the material volume control coefficients on the free vibration behavior of BDFG plate in the fluid medium are investigated in detail. Some major findings regarding the numerical results are withdrawn in conclusions.

Dynamic feature analysis in bidirectional pedestrian flows

Analysis of dynamic features of pedestrian flows is one of the most exciting topics in pedestrian dynamics. This paper focuses on the effect of homogeneity and heterogeneity in three parameters of the social force model, namely desired velocity, reaction time, and body size, on the moving dynamics of bidirectional pedestrian flows in the corridors. The speed and its deviation in free flows are investigated. Simulation results show that the homogeneous higher desired speed which is less than a critical threshold, shorter reaction time or smaller body size results in higher speed of flows. The free dynamics is more sensitive to the heterogeneity in desired speed than that in reaction time or in body size. In particular, an inner lane formation is observed in normal lanes. Furthermore, the breakdown probability and the start time of breakdown are focused on. This study reveals that the sizes of homogeneous desired speed, reaction time or body size play more important roles in affecting the breakdown than the heterogeneities in these three parameters do.

Nonlinear Analysis of Bidirectional Vortex-Induced Vibration of A Deepwater Steep Wave Riser Subjected to Oblique Currents

An improved three-dimensional(3D)time-domain couple model is established in this paper to simulate the bidirectional vortex-induced vibration(VIV)of a deepwater steep wave riser(SWR)subjected to oblique currents.In this model,the nonlinear motion equations of the riser are established in the global coordinate system based on the slender rod theory with the finite element method.Van der Pol equations are used to describe the lift forces induced by the x-and y-direction current components,respectively.The coupled equations at each time step are solved by a Newmark-βiterative scheme for the SWR VIV.The present model is verified by comparison with the published experimental results for a top-tension riser.Then,a series of simulations are executed to determine the influences of the oblique angle/velocity of the current,different top-end positions and the length of the buoyancy segment on the VIV displacement,oscillating frequency as well as hydrodynamic coefficients of the SWR.The results demonstrate that there exists a coupled resonant VIV corresponding to x-direction and y-direction,respectively.However,the effective frequency is almost identical between the vibrations at the hang-off segment along x and y directions.The addition of the buoyancy modules in the middle of the SWR has a beneficial impact on the lift force of three segments and simultaneously limits the VIV response,especially at the decline segment and the hang-off segments.Additionally,the incident current direction significantly affects the motion trajectory of the SWR which mainly includes the fusiform and rectangle shapes.

基于改进YOLOv5s的轻量级绝缘子缺失检测

针对现有绝缘子缺失检测模型计算复杂度高和小目标难以检测等问题,提出一种基于改进的YOLOv5s轻量级检测模型。首先,移除主干网络中的C3模块来减少模型的参数量。其次,在多尺度特征融合网络中引入卷积块注意力机制来提高复杂背景下模型的特征提取能力。同时,采用加权双向特征金字塔网络结构对特征进行双向跨尺度加权融合,提升网络在遮挡物、相似目标干扰下目标的检测性能。最后,选用SIoU损失函数提升网络的收敛速度和检测精度。实验结果表明,所提模型的平均精准率为96.8%,浮点运算数为2.8 GFLOPS,而原始YOLOv5s在保证97.4%的平均精准率下的浮点运算数为16.3 GFLOPS。相较于原始模型,所提模型对小目标、遮挡目标以及模糊等场景有着较强的鲁棒性,且在保证近似检测精度的同时极大减少了计算量。

基于BERT-BiLSTM-CRF模型的畜禽疫病文本分词研究

针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。

基于改进金豺算法的短期负荷预测

针对电力负荷序列波动性和预测精度不高的问题,提出一种基于变分模态分解、排列熵和改进金豺算法优化双向长短期记忆网络的预测模型。首先,利用变分模态分解重构原始负荷序列,再采用排列熵理论对分解后的子序列进行熵值重组;然后,利用改进金豺算法对双向长短期记忆网络的参数进行优化,并对每个子序列建立预测模型;最后,组合各模型结果得到最终预测值。实验结果表明,本文模型预测精度更高,与真实值拟合度更好。

基于层间融合滤波器与社交神经引文网络的推荐算法

推荐算法是一种用于解决信息过载问题的方法,引文推荐通过引文上下文能够自动匹配候选论文列表。现有基于神经引文网络模型在引文上下文数据预处理的过程中,存在文本噪声和上下文学习不充分的问题。为此,提出一种基于层间融合滤波器和社交神经引文网络的推荐算法FS-Rec。首先,利用具有层间融合滤波器的BERT模型预处理引文上下文,在频域内从所有频率中提取有意义的特征,缓解引文上下文数据的噪声,同时在频域中对多层信息进行融合,增强上下文表示学习的能力;然后,在引文作者嵌入中引入社交关系,与其他引文信息嵌入通过编码器获得表示,将这些表示与经过BERT预训练的引文上下文表示进行融合,得到最终表示;最后,根据最终表示生成引文文本预测。实验结果表明,相较于现有的上下文引文推荐模型,FS-Rec在2个基准数据集arXivCS和PubMed取得了更高的召回率和平均倒数排名(MMR),证明了模型的有效性。

基于用户性格和语义-结构特征的文本评论情感分类方法

由于传统文本评论情感分类方法通常忽略用户性格对于情感分类结果的影响,提出一种基于用户性格和语义-结构特征的文本评论情感分类方法(User Personality and Semantic-structural Features based Sentiment Classification Method for Text Comments,BF_Bi GAC).依据大五人格模型能够有效表达用户性格的优势,通过计算不同维度性格得分,从评论文本中获取用户性格特征.利用双向门控循环单元(Bidirectional Gated Recurrent Unit,Bi GRU)和卷积神经网络(Convolutional Neural Network,CNN)可以有效提取文本上下文语义特征和局部结构特征的优势,提出一种基于Bi GRU、CNN和双层注意力机制的文本语义-结构特征获取方法.为区分不同类型特征的影响,引入混合注意力层实现对用户性格特征和文本语义-结构特征的有效融合,以此获得最终的文本向量表达.在IMDB、Yelp-2、Yelp-5及Ekman四个评论数据集上的对比实验结果表明,BF_Bi GAC在分类准确率(Accuracy)和加权macro F_(1)值(F_(w))上均获得较好表现,相对于拼接Bi GRU、CNN的情感分类方法(Sentiment Classification Method Concatenating Bi GRU and CNN,Bi G-RU_CNN)在Accuracy值上分别提升0.020、0.012、0.017及0.011,相对于拼接CNN、Bi GRU的情感分类方法(Sentiment Classification Method Concatenating CNN and Bi GRU,Conv Bi LSTM)F_(w)值上分别提升0.022、0.013、0.028及0.023;相对于预训练模型BERT和Ro BERTa,BF_Bi GAC在保证分类精度的情况下获得了较高的运行效率.

BTM-BERT模型在民航机务维修安全隐患自动分类中的应用

为界定民航机务维修安全隐患类别,实现安全隐患数据的自动分类,首先,利用构建的机务维修停用词库对安全隐患记录语料进行预处理。其次,运用词对主题模型(Biterm Topic Model,BTM)提取主题和关键词,确定了“员工未按规定对工作现场进行监管”等12类安全隐患。最后,根据BTM主题模型标注的数据集对算法进行微调,构建了基于变换器的双向编码(Bidirectional Encoder Representations from Transformers,BERT)算法的机务维修安全隐患记录自动分类模型,并与传统的分类算法进行对比。结果表明:所构建的模型可以实现民航机务维修安全隐患自动分类,其效果远高于传统机器学习支持向量机算法的效果,构建的分类模型的精确率、召回率和F 1较文本卷积神经网络算法分别提升了0.12、0.14和0.14,总体准确率达到了93%。

融合CNN与BiLSTM模型的短期电能负荷预测

针对卷积神经网络(CNN)在捕捉预测序列间历史相关性方面的不足以及在变量复杂情况下出现的无法精准提取预测关键信息的问题,提出一种将双向长短期记忆网络(BiLSTM)与卷积神经网络结合的CNN-BiLSTM模型.首先,采用数据预处理方法保证数据的正确性和完整性,并对数据进行分析以探究多变量之间的相关性;其次,通过CNN与L1正则化对多维输入特征进行特征筛选,选取与预测相关的重要性特征向量;最后,使用BiLSTM对CNN输出的关键特征信息进行保存,形成向量与预测序列,并通过分析时序特征的潜在特点,提取用户的内在消费模式.实验比较了该模型与其他时序模型在不同时间分辨率下的预测效果,实验结果表明,CNN-BiLSTM模型在不同的回望时间间隔下表现出了最佳的预测性能,能够实现更好的短期负荷预测.

融合距离阈值和双向TCN的时空注意力行人轨迹预测模型

为解决因缺乏部分行人建模思想、缺少时间维度的全局视野和忽略行人交互模式多样性,而导致交互建模不充分、低预测精度等问题,提出基于Social-STGCNN(social spatio-temporal graph convolutional neural network)的改进模型STG-DTBTA(spatio-temporal graph distance threshold Bi-TCN attention)。首先,构建PPM(partial pedestrian module)模块,对不满足距离阈值等约束条件的行人交互连接剪枝以去噪。其次,引入时空注意力机制,空间注意力动态分配交互权重,并设置多个注意力头以处理交互多样性问题;时间注意力捕捉时序数据的时间依赖关系。最后,采用双向TCN增加全局视野以捕捉轨迹数据中的动态模式和趋势,并采用门控机制融合双向特征。在ETH和UCY数据集上的实验结果表明,与Social-STGCNN相比,STG-DTBTA在维持参数量与推理时间接近的情况下,ADE平均降低8%,FDE平均降低16%。STG-DTBTA具有良好的交互建模能力、模型性能和预测效果。

利用BERT和覆盖率机制改进的HiNT文本检索模型

为有效提升文本语义检索的准确度,本文针对当前文本检索模型衡量查询和文档的相关性时不能很好地解决文本歧义和一词多义等问题,提出一种基于改进的分层神经匹配模型(hierarchical neural matching model,HiNT)。该模型先对文档的各个段提取关键主题词,然后用基于变换器的双向编码器(bidirectional encoder representations from transformers,BERT)模型将其编码为多个稠密的语义向量,再利用引入覆盖率机制的局部匹配层进行处理,使模型可以根据文档的局部段级别粒度和全局文档级别粒度进行相关性计算,提高检索的准确率。本文提出的模型在MS MARCO和webtext2019zh数据集上与多个检索模型进行对比,取得了最优结果,验证了本文提出模型的有效性。

数字经济、战略性新兴企业进入与新质生产力培育

新质生产力是由生产要素创新性配置、技术要素革命性突破、产业结构转型性升级孕育出的当代新型先进生产力。近年来,我国战略性新兴产业快速发展,充分发挥了培育新质生产力的引擎作用。本文基于我国2010~2022年城市面板数据,采用双向固定效应模型,深入研究数字经济如何促进战略性新兴企业进入、进而孕育新质生产力。实证结果表明,数字经济能够通过人才集聚效应、产业结构合理化赋能功能和关键数字技术创新吸引战略性新兴企业进入。进一步研究发现,在东部地区、吸引外来投资较多和基础设施建设较为完善的城市中,这种影响更为显著。最后,经济后果检验表明数字经济促进战略性新兴企业进入同时有助于实现新质生产力的培育。本文研究结论为推动战略性新兴产业和新质生产力发展提供了启示,从而有助于持续的经济增长和创新。