LF+VD工艺生产X70管线钢洁净度研究

为了对“LF+VD”工艺生产X70管线钢过程中夹杂物的演变机理进行研究,进行了密集取样和分析,并对VD炉深真空工艺和钙处理工艺实施了优化。研究结果表明VD炉深真空过程采用前期强氩气搅拌保持13 min,后期弱氩气搅拌保持5 min可以促使大颗粒夹杂物充分上浮,同时VD炉破空后钙处理控制钙质量分数为0.0010%~0.0014%,可促使夹杂物充分变性达到低熔点区的目的,使得钢板夹杂物一检小于等于2.0级别合格率比例由96.3%提高至98.7%以上,钢水洁净度得到提高。

冶金机理与贝叶斯优化XGBoost融合的VD炉精炼终点钢液温度预测

在炼钢生产过程中,真空脱气精炼(VD)炉是生产高品质钢的重要设备之一,其精炼终点温度对钢液质量、生产效率和连铸顺行具有重要影响。为了实现对VD炉精炼终点钢液温度的精准控制,本文采用冶金机理和贝叶斯优化极端梯度提升(metallurgical mechanism–Bayesian optimization–extreme gradient boosting,MM–BO–XGBoost)相结合的方法建立钢液温度预测模型。首先,基于VD炉冶金机理解析,确定影响精炼终点钢液温度的主要因素;其次,使用3σ原则对实际生产数据进行预处理,剔除异常值,并采用皮尔逊相关性分析剔除对钢液温度影响较小的因素,从而确定模型的输入变量;再次,将冶金机理与XGBoost模型进行融合,对输入变量的初始特征重要性进行部分放大;最后,针对XGBoost模型的超参数寻优问题,采用贝叶斯优化(BO)对其进行超参数寻优,由此构建了MM–BO–XGBoost模型。在模型仿真过程中,对本文模型同时使用网格搜索和随机搜索进行超参数寻优,旨在对比和验证BO寻优的效果;此外,使用本文提供的数据对已有的冶金机理模型、多元线性回归模型和反向传播神经网络模型进行仿真,并与MM–BO–XGBoost模型进行性能对比。结果表明:本文提出的MM–BO–XGBoost模型的超参数优化效果最好;本文模型的预测VD炉终点钢液温度在±10℃和±15℃误差范围内的命中率分别为87.81%和96.42%,均高于其他对比模型,综合性能最优。本文构建的VD炉钢液精炼终点温度预测模型,对实现钢液温度精准控制、降低生产成本和提高VD炉精炼效率具有重要的现实意义。

VD精炼技术的研究进展

VD(Vacuum degassing)精炼在真空气氛下有强烈的渣-钢反应,具有脱气、脱硫以及控制非金属夹杂物等功能,是生产高品质洁净钢的重要精炼方式。介绍了VD精炼技术的发展历程和工业应用,详细阐述了在脱气、脱硫、夹杂物控制等方面的研究和生产应用情况,并对VD工艺提出了建议。VD精炼中真空脱气(O、N、H)的研究主要集中在优化工艺参数和提升设备性能方面,能将钢中氢质量分数控制在(1~2)×10^(-6),氮质量分数控制在(40~60)×10^(-6),且VD真空脱氮具有一定的优势。脱硫方面,VD精炼有良好的热力学和动力学条件,能将钢中硫含量控制在很低水平。夹杂物控制方面,通过优化炉渣成分和工艺参数,VD精炼可以减少夹杂物数量、对夹杂物进行改性。相关的研究结果对VD精炼的研究与生产具有一定的指导意义,为降低钢中杂质元素含量、控制非金属夹杂物、提高钢的洁净度提供了有益的参考。

Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the Information Content Criterion

The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy.

基于DBSVDD-RVR的多模态间歇过程质量变量在线软测量

现有的多模态间歇过程软测量未考虑过程数据的批次差异及过渡模态的复杂时变特性,影响了间歇过程模态识别的合理性及质量变量在线软测量的准确性。提出了一种基于双边界支持向量数据描述-相关向量回归(double boundary support vector data description-relevance vector regression,DBSVDD-RVR)的间歇过程质量变量在线软测量方法。依据间歇过程离线模态划分获得的各稳定及过渡模态历史数据,建立DBSVDD在线模态识别模型,并引入滑动窗,构建间歇过程在线模态识别策略,利用DBSVDD模型实现在线测量数据的模态识别;在此基础上,构建了基于超球体距离的数据相似度计算方法,选择过渡模态在线数据的相似建模数据集,建立过渡模态的即时学习RVR软测量模型,并依据历史数据建立各稳定模态的RVR软测量模型,实现间歇过程质量变量的在线软测量。青霉素发酵过程的实验结果表明,所提方法有效地提高了间歇过程模态识别的合理性和质量变量在线软测量的准确性。

Method for detector description conversion from DD4hep to Filmbox

DD4hep serves as a generic detector description toolkit recommended for offline software development in next-generation high-energy physics(HEP)experiments.Conversely,Filmbox(FBX)stands out as a widely used 3D modeling file format within the 3D software industry.In this paper,we introduce a novel method that can automatically convert complex HEP detector geometries from DD4hep description into 3D models in the FBX format.The feasibility of this method was dem-onstrated by its application to the DD4hep description of the Compact Linear Collider detector and several sub-detectors of the super Tau-Charm facility and circular electron-positron collider experiments.The automatic DD4hep–FBX detector conversion interface provides convenience for further development of applications,such as detector design,simulation,visualization,data monitoring,and outreach,in HEP experiments.

12 t钢包VD处理工艺实践

为保证新型合金钢对气体含量的要求,通过VD炉处理,将其中的气体元素含量控制在要求范围之内。针对VD实验过程中的Mn损、温降、溢渣等问题提出相对应的控制措施,并取得了不错的效果;对加Al量与VD脱氢率进行探究,结果表明,VD处理过程前加Al量越高,VD过程中脱氢率就越低。

Enhancing Image Description Generation through Deep Reinforcement Learning:Fusing Multiple Visual Features and Reward Mechanisms

Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.

Enhancing constitutive description and workability characterization of Mg alloy during hot deformation using machine learning-based Arrhenius-type model

Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy.However,the hot deformation of Mg alloy is highly sensitive to factors such as temperature,strain rate,and strain,leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy.To overcome the shortcomings of the conventional Arrhenius-type(AT)model,this study developed machine learning-based Arrhenius-type(ML-AT)models by combining the genetic algorithm(GA),particle swarm optimization(PSO),and artificial neural network(ANN).Results indicated that when describing the flow behavior of the AQ80 alloy,the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models.Moreover,an activation energy-processing(AEP)map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model.Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods,ultimately contributing to the precise determination of the optimum processing window.These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.

Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment

Cross-lingual image description,the task of generating image captions in a target language from images and descriptions in a source language,is addressed in this study through a novel approach that combines neural network models and semantic matching techniques.Experiments conducted on the Flickr8k and AraImg2k benchmark datasets,featuring images and descriptions in English and Arabic,showcase remarkable performance improvements over state-of-the-art methods.Our model,equipped with the Image&Cross-Language Semantic Matching module and the Target Language Domain Evaluation module,significantly enhances the semantic relevance of generated image descriptions.For English-to-Arabic and Arabic-to-English cross-language image descriptions,our approach achieves a CIDEr score for English and Arabic of 87.9%and 81.7%,respectively,emphasizing the substantial contributions of our methodology.Comparative analyses with previous works further affirm the superior performance of our approach,and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language.In summary,this study advances the field of cross-lingual image description,offering an effective solution for generating image captions across languages,with the potential to impact multilingual communication and accessibility.Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.

Unified Description of the Three Stable Particles in Self-Action Allows Determination of Their Relative Masses

The Dirac equation γμ(δμ-eAμ)Ψ=mc2Ψ describes the bound states of the electron under the action of external potentials, Aμ. We assumed that the fundamental form of the Dirac equation γμ(δμ-Sμ)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials Sμ. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.

基于图滤波器和SVDD算法的分布式光伏集群发电异常检测研究

由于电网企业不断加快数字化转型,利用北斗定位技术将自动获取区域内光伏计量装置经纬度这一关键技术参数。文章充分利用分布式光伏集群内光伏发电装机位置空间相关性,提出一种在弱监督下基于图滤波与支持向量数据描述(support vector data description,SVDD)的分布式光伏集群发电异常检测方法。首先建立分布式光伏集群发电图数据结构模型,通过加权邻接矩阵描述分布式光伏发电点空间耦合性,其次构造图高通滤波器将时域参数转化为频域参数,然后通过SVDD算法优化图滤波结果,进一步挖掘图高通滤波器阈值与输出功率数据之间的关系。结果表明,采用图滤波器和SVDD算法模型方法在分布式光伏发电异常检测精度上有显著提高。

基于SVDD和改进K-Means的变压器故障诊断模型

变压器状态对于智能配电房的安全稳定运行具有重要意义。为实现对变压器故障的准确诊断,在变压器油中溶解气体分析(DGA)的基础上,提出了一种联合使用支持向量数据描述(SVDD)和改进K-Means聚类的变压器故障诊断方法。首先利用SVDD构造闭合分类曲面实现“正常”和“故障”两类判断,然后对“故障”类样本进行K-Means聚类分析,自动将其划分为低能放电、中低温过热、高能放电、高温过热和局部放电5种故障类型,同时针对K-Means初始聚类中心选取难题,提出局部密度概念自动确定K-Means初始聚类中心,提升聚类性能。最后利用变压器故障真实数据开展实验,结果表明,相较于支持向量机(SVM)和BP神经网络模型,所提方法的故障诊断准确率分别提升9.8%和8%。

基于MKMCC-DSVDD的航空发动机异常检测方法

为解决传统航空发动机异常检测方法准确率和泛化性能较低的问题,提出一种混合核最大相关熵的深度支持向量数据描述(mixed kernel maximum correntropy criterion-deep support vector data description,MKMCC-DSVDD)方法。首先,采用合成少数类过采样技术扩充异常样本规模,提高对非均衡样本的泛化性能;其次,建立基于混合核改进的最大相关熵损失函数,可以在无须数据分布假设的前提下提升准确率;最后,构建基于MKMCC-DSVDD的航空发动机异常检测方法。在航空发动机气路系统和滑油系统异常检测实验中,所提方法平均曲线下的面积(area under curve,AUC)达到98.53%,表明其具有较高的实用性和泛化性能。

应用神经网络技术预报VD炉终点钢水温度

应用神经网络方法建立了VD炉钢水温度预报模型。用该模型在线连续预报的76炉次钢水中,预报温度与实际测量温度之差在±4℃和±5℃之内的炉次分别占67 1%和80 3%。分析了各工艺参数对终点温度的影响,据此对宝钢的生产实践提出了一些降低能耗的措施。

BOF-LF-VD-CC工艺生产车轮钢的N含量控制

根据马钢车轮钢生产工艺规程,通过在线工业实验,测定了车轮钢精炼过程中钢液N含量的变化规律。结果表明:在LF-VD-CC过程中钢液吸氮主要环节包括LF过程、钢包到中包过程和中包至结晶器过程。其中LF过程钢液吸N严重,平均吸N量达到28×10^(-6),最大吸N量为50×10^(-6)。讨论了LF过程钢液吸N原因和抑制吸N的工艺措施。VD过程中钢液脱氮效果明显,平均脱氮率可达到30%左右,平均脱氮量为21×10^(-6)。分析了影响VD过程脱N的主要工艺参数及各参数的优化控制值。

25-(OH)VD与2型糖尿病患者的糖尿病肾病相关性研究

目的探讨25-(OH)VD与2型糖尿病患者的糖尿病肾病相关性。方法收集937例未接受维生素D补充治疗的2型糖尿病患者,测定各临床指标以分析糖尿病肾病的相关因素。结果与正常尿微量白蛋白组患者相比,微量或大量尿白蛋白组患者的25-(OH)VD水平较低且具有显著统计学差异。相关性分析显示,用性别和BMI作为校正指数,年龄、糖尿病病程、高血压病程、收缩压、糖化血红蛋白、肌酐、尿素氮与尿微量白蛋白呈正相关,而25-(OH)VD水平与尿微量白蛋白呈负相关。结论 25-(OH)VD水平与2型糖尿病患者的尿微量白蛋白呈显著负相关。

LF/VD过程的系统工艺优化

从钢水纯净度、温度控制、成分控制方面,对LFVD过程进行了系统工艺优化,制定了LFVD系统工艺优化模型及标准化生产操作制度。

SG45VCM钢LF+VD精炼吹氮合金化研究

1 873 K下,Mo Si2电阻炉上用70 mm×100 mm Mg O坩埚,以SG45VCM钢为研究对象,对不同压力下（33、66、100 k Pa）气相渗氮过程及脱氮过程中氮的溶解行为进行了研究。分别建立了相应的热力学及动力学模型,并在实验室进行了验证。实验发现,氮分压越大溶解度越大,吹氮25 min后钢液中氮达到饱和,吹氩脱气40 min后,钢中氮的质量分数在100×10-6以下。工业试验表明,60 t LF＋VD精炼炉底吹氮气后钢中增氮效果稳定。在LF精炼底吹氩气压力1~1.5 MPa、流量300~400 Nl/min条件下吹氮20~30 min后切换成氩气,VD保真空5~10 min的冶炼工艺条件下,可不添加任何氮化合金,即可生产满足氮含量要求的产品。

以VDS为主化疗方案的近期疗效

作者报告以ＶＤＳ为主的化疗方案治疗多种恶性肿瘤３７例。结果总有效率为４０．５％，其中肺癌有效率为３１．３％，非何杰金氏恶性淋巴瘤有效率为８５．７％，食管癌有效率为５０．０％；初治病例有效率５０．０％，既往经ＶＣＲ治疗失败病例有效率为３８．５％。作者认为ＶＤＳ对多种恶性肿瘤，特别是对化疗较不敏感的肺腺癌、食道癌等有较好疗效，且与ＶＣＲ无完全交叉耐药性。其神经毒性低，但要警惕其骨髓毒性。