Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

“Here Comes the New”:Individual and Collective Forgetting in Toni Morrison’s Jazz

This article examines the problem of individual and collective attempts at forgetting the traumatic past in Toni Morrison’s sixth novel Jazz(1992).More specifically,it emphasizes by selected examples psychological and social aspects of willful amnesia which can lend itself useful in helping traumatized(country)individuals to repress painful remembrances,heal mental wounds and build a new identity in a memory-free modern city.Analyzing Jazz’s narrative featuring Joe and Violet Trace,with a particular focus put on the expectations and experiences connected with their migration to and life in the City,the article explores via Paul Connerton’s ruminations on cultural forgetting in modern times-delineated in his book How Modernity Forgets(2009)-the mechanisms of intentional amnesia used in the process of recovering from personal and social traumas resulting from more recent(migration and urban life)and more time-distant(slavery and racism)ordeals.

An Unforgettable Trip Into an Intangible Cultural Heritage at Quanzhou

Quanzhou,as the only starting point of the Maritime Silk Road recognized by the United Nations,was praised as"the most prosperous city in the world"by Marco Polo.On July 25th,2021,China's"Quanzhou:Emporium of the World in Song-Yuan China"was added to the UNESCO World Heritage List as a cultural site,bringing the total number of the country's UNESCO World Heritage sites to 56.The twentytwo world heritage sites in Quanzhou.

A traffic flow cellular automaton model to considering drivers' learning and forgetting behaviour

It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers＇ memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers＇ intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.

Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under non-persistent excitation.The proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is received.Theoretical proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite bound.Moreover,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed algorithm.Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

RLS channel estimation with adaptive forgetting factor in space-time coded MIMO-OFDM systems

Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.

Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors

The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they need.At this stage,there are a lot of intelligent tutoring systems.In these systems,students􀆳learning actions are tracked in real⁃time,and there are a lot of available data.From these data,personalized education that suits each student can be mined.To improve the quality of education,some models for predicting students􀆳next practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep learning.However,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the knowledge.Although some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning sequences.The main contribution of this paper is in two aspects.One is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political combinations.The other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting behavior.This paper proposes an adaptive algorithm for the original DKT model.By using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of students􀆳learning.The model proposed in this paper has been experimented on two public data sets to improve the original accuracy.

Forgetting In Creative Problem Solving for College Students

As more and more benefits of forgetting have been found in recent studies,whether forgetting could promote individuals ability of creative problem solving remains a controversial debate.This article discusses the effect of two types of forgetting,the retrieval-induced forgetting(RIF)and the forgetting during incubation,in benefiting creative problem solving by introducing and analysing the relevant experiments.The results reveal that retrieval-induced forgetting only works when previous mental fixations occurred and the promotion varies when solving different types of problems.The level of RIF is irrelevant to the performance in solving closed-ended creative problems and high level of RIF even impairs the creativity when solving open-ended problems.And forgetting during incubation cannot explain the incubation effect.The spreading activation of relevant information or the unconscious work is more likely to be the possible reasons.In conclusion,the current article brings about the discussions about the work conditions and effects of forgetting in creative problem solving.

Uncovering the Mist of Forced Forgetting: On Forgiveness in The Buried Giant

The Buried Giant by Kazuo Ishiguro begins with an elderly couple who start a quest for the past memory which disap pears under the spell of the she-dragon Querig.During,individual confrontations and collective revenges work together to disclose the dark secrets that have been withheld.At the same time,it probes into the problem of forgiveness:can forced forgetting enable individuals or collectives forget their dark history for either love or peace?Based on the analysis of the individual and collective memories embodied in the novel,the present paper by virtue of Paul Ricoeur’s theory of abuses of memory,especially forced forget ting exhumes Ishiguro’s critical attitude towards forced forgetting,which ignores the threatening elements of love and peace like vi olent revenge and betrayal.

The Enlightenment of Language Attrition and Forgetting to English Vocabulary Memory Strategies for College English Majors

This paper reviews the theory of language attrition,which refers to the loss or degradation of second language skills due to lack of using the second language for a certain period of time.Although our country attaches great importance to English language teaching,most of college English majors use English far less frequently than that of Chinese in real life,which makes them easily influenced by language attrition.Therefore,it is of great significance for college English majors to improve the efficiency of English vocabulary memory from the perspective of language attrition combined with Forgetting.This thesis consists of three parts.Chapter one is an analysis the concept of language attrition and Forgetting.Chapter two describes and analyzes the existing problems in current vocabulary memory among the college English majors via a questionnaire survey.The final chapter puts forward some corresponding countermeasures to help college English majors get rid of the influence of language attrition on vocabulary learning.

An Unforgettable Day

It was Sunday today.We decided to have a picnic on the top of the mountain near our school.How exciting1 we were!We set out early in the morning.After we got to the top,we were fascinated by the beautiful scenery.After we had a goodtime on the top of the mountain.

急性运动对负性情绪信息有意遗忘的影响

目的:探究急性运动是否可作为一种干预手段作用于大学生负性情绪信息的有意遗忘,以及情绪材料的性质和时间耦合对这一过程的影响。方法:将102名大学本科女生随机分为编码前运动组、编码后运动组和对照组,以10 min中等强度持续运动为运动干预方式,采用项目法有意遗忘范式,以低唤醒负性词、高唤醒负性词和中性词为记忆材料,比较不同组别被试在自由回忆正确数、再认的辨别力指数d’及相对应的有意遗忘效应值上的差异。结果:编码前运动组在再认任务中的有意遗忘效应值显著高于对照组,主要体现在编码前运动组忘记指令词语d’显著低于对照组,编码后运动组的自由回忆正确数呈现出低于编码前运动组和对照组的趋势。结论:编码前运动通过提升个体对信息的抑制控制能力促进有意遗忘的执行,编码后运动则通过干扰记忆巩固过程降低整体回忆表现。提示急性运动可提升个体主动抑制负性情绪信息记忆编码的能力,而在经历负性情绪事件后进行的急性运动能够帮助个体更快遗忘相关负性信息。

最小二乘算法优化及其在锂离子电池参数辨识中的应用

传统最小二乘法(LS)用于锂离子电池模型在线参数辨识精度低,通过带遗忘因子递推最小二乘算法能够有效地提高辨识精度,但固定的遗忘因子影响模型动态特性。遗忘因子的自适应处理能提高算法对动态系统的参数辨识能力,而目前的自适应方法容易忽略模型参数的稳定性,同时方法待定系数范围较大且难以确认。为了得到高精度且稳定性良好的模型参数,该文设计了一种精度和稳定性兼优且更简单的自适应遗忘因子递推最小二乘(AFFRLS)改进方法,并与其他AFFRLS、可变遗忘因子递推最小二乘(VFFRLS)进行仿真对比分析。结果表明,改进的AFFRLS能够在模型精度和参数稳定性取得更好的平衡,且对不同的在线工况具有良好的适用性。

基于主体遗忘行为的产学研协同创新最优网络结构

探究产学研协同创新中的知识扩散最优网络结构对提升创新效率具有重要意义。现有关知识扩散过程中的遗忘机制大多被视为指数衰减,且主要关注企业之间的知识扩散,鲜有学者将主体知识遗忘行为纳入产学研协同创新最优网络结构的研究中。本文在现有知识交互模型上加入了遗忘系数,基于仿真方法,探究了主体遗忘行为对产学研协同创新绩效的影响及不同条件下产学研协同创新绩效提升的最优网络结构。结果表明:主体遗忘行为降低了整体绩效,弱化了个体之间的知识差异;在不同的主体知识遗忘率下,最优知识网络结构是不同的。据此,从平均路径长和集聚度视角提出了优化网络结构提升协同创新绩效的建议。

不均衡小样本下多特征优化选择的生命体触电故障识别方法

针对现有的剩余电流保护装置无法有效识别触电事故的问题,该文提出了一种不均衡小样本下多特征优化选择的生命体触电故障识别方法。首先通过变分自编码器(VAE)对实验收集到的生命体触电小样本数据进行增殖以实现正负样本均衡;然后在时域上提取能够反映波形动态变化特性的23个特征量,并利用高斯核Fisher判别分析(GKFDA)与最大信息系数(MIC)法从中选择最优表达特征组;最后,提出基于遗忘因子的在线顺序极限学习机(FOS-ELM)算法实现生命体触电行为的鉴别。实验结果表明,所提方法利用不均衡小样本触电数据集就可以训练出一个优秀的分类模型,诊断准确率可达98.75%,诊断时间仅为1.33 ms。其优良的性能结合在线增量式学习分类器设计,使得模型具备新知识学习能力,具有极好的工程应用前景。

永磁同步电机多参数辨识研究

针对表贴式永磁同步电机(surface permanent magnet synchronous motor, SPMSM)在运行过程中参数时变问题,采用带遗忘因子的递推最小二乘法(forgetting factor recursive least squares, FFRLS)在线辨识永磁磁链ψ_f、定子电阻R_s和电感L_s。对SPMSM数学模型进行分析,结合空间矢量脉宽调制技术,实现矢量控制;分析不同参数发生变化对电机控制性能的影响,并建立矢量控制策略下FFRLS参数辨识和递推最小二乘法(recursive least squares, RLS)辨识的系统仿真模型,进行对比仿真分析。仿真结果表明,该算法能较好地进行辨识,辨识快速收敛,辨识精度高。

水平张量重力梯度仪垂向运动误差实时补偿方法

为减小垂向运动对水平张量重力梯度测量的影响,提出了一种水平张量重力梯度仪垂向运动误差实时补偿方法。首先,分析了载体垂向运动引起重力梯度动态测量误差的机理,建立了综合安装误差角的回归方程;其次,在递推公式中引入遗忘因子,提高状态估计的跟踪速度;再次,针对量测量易受载体运动干扰的问题,通过Sage-Husa自适应滤波的方法实时适应不同量级的动态干扰,对综合安装误差角进行实时估计;最后,利用得到的综合安装误差角估计结果,实现重力梯度垂向运动测量误差的实时补偿。船载实验数据处理结果表明,与传统补偿方法相比,所提方法可将重力梯度内符合精度由30E@1km提高至15E@1km。

青少年抑郁症患者主动遗忘能力与童年创伤和抑郁症状的关系

目的:比较青少年抑郁症患者与正常青少年在主动遗忘能力上的差异,并探讨该能力与童年创伤和抑郁症状的关系。方法:符合精神障碍诊断与统计手册第5版(DSM-5)抑郁症诊断标准的141例青少年患者和42例正常对照组参与研究。采用定向遗忘(DF)任务,以情绪图片为记忆材料,比较两组在再认成绩上的差异。DF效应指“记住”条件再认成绩高于“忘记”条件。并采用儿童期创伤问卷(CTQ-SF)和贝克抑郁量表(BDI-II)评估被试的童年创伤程度和抑郁症状。结果:与正常对照组不同,抑郁症组仅在正性材料中出现DF效应(P<0.001)。在抑郁症组中,负性材料DF效应值在CTQ-SF得分与BDI-II得分间起部分中介作用(效应值=0.10,95%可信区间为0.05~0.17,占总效应21.3%)。结论:青少年抑郁症患者的主动遗忘能力部分受损,他们对负性记忆的主动遗忘能力在童年创伤与抑郁症状间有部分中介效应。

持续学习的研究进展与趋势

随着深度学习技术的发展与应用,特别是资源受限场景和数据安全场景对序列任务和数据进行快速学习需求的增多,持续学习逐渐成为机器学习领域关注的一个新热点.不同于人类所具备的持续学习和迁移知识的能力,现有深度学习模型在序列学习过程中容易遭受灾难性遗忘的问题.因此,如何在动态、非平稳的序列任务及流式数据中不断学习新知识、同时保留旧知识是持续学习研究的核心.首先,通过对近年来持续学习国内外相关工作的调研与总结,将持续学习方法分为基于回放、基于约束、基于结构三大类,并对这3类方法做进一步的细分.具体而言,根据所使用的样本来源将基于回放的方法细分为采样回放、生成回放、伪样本回放3类;根据训练约束的来源将基于约束的方法细分为参数约束、梯度约束、数据约束3类;根据对于模型结构的使用方式将基于结构的方法细分为参数隔离、模型拓展2类.通过对比相关工作的创新点,对各类方法的优缺点进行总结.其次,对国内外研究现状进行分析.最后,针对持续学习与其他领域相结合的未来发展方向进行展望.

基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法

针对电池SOC与SOH估计结果相互影响,单独估计准确度不高的问题,该文提出了一种基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法。通过构建考虑老化和SOC的电池二阶RC等效电路模型,采用带遗忘因子的递推最小二乘法,在不同SOC和SOH的情况下,对电池的参数进行在线辨识,实现电池参数在线辨识与电池SOC和SOH估计的耦合。以锂离子电池自SOC=20%到恒流充电阶段结束所需时间为输入,电池SOH值为输出,训练GPR模型,实现电池SOH估计。将输出的SOH估计值与电池的额定容量相乘,得到电池的实际容量,更新二阶RC状态空间方程,采用扩展卡尔曼滤波算法对电池进行SOC估计,实现电池SOH估计和SOC估计之间的联合。采用牛津大学电池退化数据集和NASA随机使用电池数据集进行算法验证,结果表明,所提联合估计方法能够在电池的生命周期内较准确地跟随锂离子电池SOC和SOH的真实值。