基于GA-ELM的锂离子电池RUL间接预测方法

针对锂离子电池在线剩余寿命预测时容量难以直接测量及预测精度不高等问题,提出一种间接预测方法。首先,分析电池寿命状态特征参数,选取等压降放电时间作为锂电池间接健康因子;其次,引入遗传算法优化极限学习机模型参数,建立锂电池剩余使用寿命间接预测模型;最后,基于NASA锂电池实验数据和自主实验数据验证该预测方法的正确性和有效性。实验结果表明,相较高斯过程回归方法和极限学习机方法,该方法准确有效、测试速度快,并且预测结果输出稳定,精度较高。     

军车驾驶员的速度预测和动作协调能力的实验研究

目的通过分析两项分测验中不同水平军车驾驶员的成绩,验证海军不同水平军车驾驶员的速度预期能力与动作协调能力的差异,以预防交通事故发生。方法选取90名军车驾驶员为被试,以他们在两项分测验中的反应时为指标,考察了不同水平驾驶员对不同运动速度的目标的预期反应能力和动作协调能力。结果 (1)当目标物体运动速度为每秒80、110、和140像素时,汽车驾驶员会提前预测,并且优秀驾驶员、一般驾驶员和较差驾驶员判断的误差达到显著性差异水平;(2)当目标物体运动速度为每秒170、和200像素时,汽车驾驶员会推迟预测,并且优秀驾驶员、一般驾驶员和较差驾驶员判断的误差达到显著性差异水平;(3)目标刺激在三组运动,三组静止条件下,优秀驾驶员、一般驾驶员和较差驾驶员的判断反应时差异显著。结论 (1)汽车驾驶员在对目标对象速度预测中,速度较慢的运动目标大多被试提前预测,速度较快的运动目标大多被试则推迟预测,差异非常显著;(2)追碰静止的目标的反应时比追碰运动目标的反应时要短,二者的差异非常显著。     

文化遗产在严肃游戏中的设计与应用

目的通过分析文化遗产在严肃游戏中应用的目的、玩法以及范围,探讨在面向未来的文化遗产教育中,严肃游戏可能发挥的潜力和方向。方法利用严肃游戏的G/P/S分类模型,进行国内外相关文献和案例的梳理,结合案例分析对文化遗产在严肃游戏中的应用现状、难点和趋势进行概述。结论文化遗产在严肃游戏中的设计与应用可以分为4个方向:文化展示和传播、文化意识和行为的改变、传统技艺的辅助性获取以及社会文化情景的构建。其游戏玩法和应用范围呈现出多样化、复杂化和跨界拓展的趋势。其中,将文化传承与其它教育目标相结合,在设计游戏时考虑学习者社会文化情景的构建是通过游戏学习文化遗产的一个新趋势。     

基于机器学习的电喷印精度预测方法研究

目的 节省电流体喷射打印精度预测的时间和解决电流体工艺参数的选择问题,达到提高电流体打印的质量和效率的目的。方法 为了对电流体喷射打印精度进行预测,提出有限元模型与机器学习相结合的方法。基于线性回归、支持向量回归和神经网络等机器学习算法建立4种参数与射流直径的关系模型。结果 算法结果表明:支持向量回归和神经网络预测模型的决定系数R²能达到0.9以上,表示模型可信度高;支持向量回归和神经网络预测模型指标都比线性回归预测模型的小。结论 机器学习算法可对电喷印打印精度进行有效预测,预测效率提高了十几倍,节省了精度预测的时间。     

基于CSO-SVM的数控机床主轴热误差建模

针对数控机床多热源所致的温升与主轴热误差之间复杂的非线性关系问题,提出一种鸡群优化（chicken swarm optimization, CSO）算法与支持向量机（support vector machines, SVM）相结合的主轴热误差预测模型（以下简称热误差模型）。以某精密数控机床的主轴单元为研究对象,采用五点法对其在空转状态下的轴向热变形进行测量,并借助热电偶传感器对机床的4个关键温度测点的温度进行采集。以SVM为理论基础,随机选取75%的数据样本进行训练,进而构建主轴热误差模型。其中,利用CSO算法优化SVM模型的惩罚参数c和核参数g,以提升热误差模型的预测能力及鲁棒性。以余下的25%的样本作为测试数据集,对所得热误差模型进行验证。利用CSO-SVM模型对不同工况下主轴的热误差进行预测,并将预测结果与测量结果进行对比。结果表明：当主轴转速为3 000 r/min时,CSO-SVM模型的平均预测精度高达97.32%,相较于多元线性回归模型和基于粒子群优化的SVM模型分别提升了6.53%和4.68%;当主轴转速为2 000, 4 000 r/min时,CSO-SVM模型的平均预测精度分别为92.53%、91.82%,表明该模型具有较高的预测能力和良好的鲁棒性。CSO-SVM模型具有较强的实用性和工程应用价值。     

Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). We also constructed ensemble models such as bagging (Random Forest) and boosting like AdaBoost and Extreme Gradient Boosting (XGBoost) to evaluate the performance of prediction models. The proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the disease. The combined ML models used majority voting to select the best outcomes among the models. The efficacy of the proposed and other models was evaluated for effective diabetes prediction. The investigation concluded that the proposed model, after hyperparameter tuning, outperformed other learning models with an accuracy of 84.42%, a precision of 85.12%, a recall rate of 65.40%, and a specificity of 94.11%.     

浅海海底单参数模型分析与研究

介绍了单参数模型,提出以小掠射角下反射损失随掠射角的变化率Fd B作为描述海底性质的单参数,并分析研究了该海底单参数模型的适用性,分析得到,当频率不低于数十赫兹且海底沉积物类型属于高声速海底时,单参数模型可以用来描述海底性质。还研究了海水声速对于海底单参数模型描述水下声场的适用性的影响,结果表明,当海水声速剖面可以近似等效为均匀水层时,可用海底单参数模型对水下声场进行预报应用。     

艇尾实尺桨空化初始航速和高频噪声谱的工程预报

为了实现艇尾实尺桨空化初始航速和高频噪声谱的工程预报,采用模型桨空化多相流模拟和实桨高频噪声谱半经验公式预报相结合的方法,预报了SUBOFF潜艇标称伴流下的实尺度7叶大侧斜桨的空化初始航速和高频噪声谱曲线以及特定频率1kHz处的谱源级。空化模拟和空化初生的较高预报精度由E779A桨的空化形态、空化面积和初生空化数的预报给予了校验。半经验公式的适用性由USS212型潜艇和Agosta-80潜艇的螺旋桨噪声预报给予了校验,精度适中。计算结果表明:7叶桨在水深16.8 m时空化初始航速为12.8 kn,6 kn航速下1 kHz处谱级为101.7 dB,较相同水深下Agosta-80潜艇临界航速高2.6 kn、谱级低0.3 dB,表明噪声性能更优。较好地建立了艇尾实尺桨空化初始航速和高频噪声谱预报的工程应用方法。     

Assessment of shear capacity of adhesive anchors for structures using neural network based model

In this study, an artificial neural network (NN) based explicit formulation for predicting the edge breakout shear capacity of single adhesive anchors post-installed into concrete member was proposed. To this aim, a comprehensive experimental database of 98 specimens tested in shear was used to train and test NN model as well as to assess the accuracy of the existing equations given by American Concrete Institute and prestressed/precast concrete Institute. Moreover, the proposed NN model was compared with another existing model which had been derived from gene expression programming by the authors in a previous study. The prediction parameters utilized for derivation of the model were anchor diameter, type of anchor, edge distance, embedment depth, clear clearance of the anchor, type of chemical adhesive, method of injection of the chemical, and compressive strength of the concrete. The proposed model yielded correlation coefficients of 0.983 and 0.984 for training and testing data sets, respectively. It was found that the predictions obtained from NN agreed well with experimental observations, yielding approximately 5 % mean absolute percent error. Moreover, in comparison to the existing models, the proposed NN model had all of the predicted values in ±20 % error bands while the others estimated up to %160 error.     

Bayesian uncertainty analysis with applications to turbulence modeling

In this paper, we apply Bayesian uncertainty quantification techniques to the processes of calibrating complex mathematical models and predicting quantities of interest (QoI's) with such models. These techniques also enable the systematic comparison of competing model classes. The processes of calibration and comparison constitute the building blocks of a larger validation process, the goal of which is to accept or reject a given mathematical model for the prediction of a particular QoI for a particular scenario. In this work, we take the first step in this process by applying the methodology to the analysis of the Spalart-Allmaras turbulence model in the context of incompressible, boundary layer flows. Three competing model classes based on the Spalart-Allmaras model are formulated, calibrated against experimental data, and used to issue a prediction with quantified uncertainty. The model classes are compared in terms of their posterior probabilities and their prediction of QoI's. The model posterior probability represents the relative plausibility of a model class given the data. Thus, it incorporates the model's ability to fit experimental observations. Alternatively, comparing models using the predicted QoI connects the process to the needs of decision makers that use the results of the model. We show that by using both the model plausibility and predicted QoI, one has the opportunity to reject some model classes after calibration, before subjecting the remaining classes to additional validation challenges.     

基于混沌时间序列的多步预测方法研究

为更好地实现智能稳定平台隔离扰动的性能,本文针对平台载体运动姿态的预测问题展开研究.在对平台载体横滚运动时间序列进行混沌特性判定的基础上,使用加权一阶局域法对载体运动姿态进行多步预测.针对预测过程中误差累积的问题,提出在姿态预测的同时对误差序列进行预测,并通过误差预测值实时修正姿态预测值.文中给出了两种误差补偿的多步预测方法,通过试验水池的实测数据仿真分析表明,误差补偿的加权一阶局域多步预测方法提高了预测精度,抑制了误差的累积;进一步通过相对均方误差等指标进行评价,指出预测方法中LPC法误差补偿的多步预测效果最优,具有一定的实用价值.     

穿越古长城的隧道开挖爆破振动参数预测模型

以山西省某穿越古长城的高速公路隧道爆破开挖工程为实例,根据支持向量机学习原理,建立支持向量机预测模型,以孔径、孔深、孔距、排距、单段最大装药量、总装药量和爆源距作为模型的输入参数,分别预测质点的径向、切向和垂直方向的爆破峰值振动速度及频率,并将预测值与实测值进行对比,以检验模型的精确度。结果表明,支持向量机预测模型对爆破峰值振动速度与频率的预测具有收敛快、精度高等特点,平均误差分别为11.04%、10.16%。利用该模型可以较准确地对爆破振动参数进行预测,在后续的爆破施工作业中,结合预测结果可以更好地对古长城采取有效的保护措施。     

基于改进土壤承压模型的履带车辆行驶振动特性仿真研究

计算履带车辆在软土路面上行驶时振动特性的基础在于研究土壤承压特性,然而目前以贝克公式为代表的土壤承压模型缺乏考虑剪应力因素、加卸载因素和加载速率因素。通过物理试验结合仿真试验的手段在贝克公式基础上,综合考虑这三种因素影响,建立了改进土壤承压模型。建立了某型履带车辆的动力学模型,基于改进土壤承压模型进行了履带车辆行驶振动特性仿真研究,研究结果显示,与贝克公式相对比,基于改进土壤承压模型计算得到的车体冲击加速度峰值以及平均值要小,而履带板的沉陷量要大。研究结论可以为预测履带车辆在软土路面上行驶时的下陷量、行驶阻力、振动特性以及牵引特性等性能提供参考。     

基于BSA-RELM的纯电动汽车锂离子电池SOC估计

提出一种基于鸟群算法优化鲁棒极限学习机的锂离子电池荷电状态估计算法。鲁棒极限学习机克服了极限学习机不能处理异常值的缺点,提高了网络的预测准确率。利用鸟群算法优化鲁棒极限学习机的隐层节点数和调节因子等参数,解决隐层节点数和调节因子等参数难以确定的问题,可进一步提高网络的收敛速度,且利于寻找全局最优值。利用ADVISOR软件采集影响电池荷电状态的主要参数:电流、电压、温度和内阻等进行建模和测试。仿真结果表明,采用鸟群算法优化鲁棒极限学习机比BPNN、RBFNN和FNN的估计误差更小,具有更高的预测精度。     

Comparison of three computational models for predicting pressurization characteristics of cryogenic tank during discharge

In order to select an effective approach to predict the pressurization characteristics of cryogenic tank during rocket launching, three computational models, defined as 0-D, 1-D and CFD models, are used to obtain the pressure evolution and thermal performance of a cryogenic tank during pressurized discharge period. Several pressurization cases are computed by all of the three models to evaluate their predictive abilities and effects, respectively. The comparative study shows that for the case with a diffuser-type injector at the tank inlet, the consistent results by the three models are obtained in the most of period, except that 1-D model has a peak departure prediction of pressure value at the beginning of process. All of the three models can be used to predict the pressurization performance, and their predictive abilities could be validated with one another. The CFD model is the unique suitable model to display the pressurization performance including physical distribution in radial direction especially for the system with no-diffuser-type injector. Based on the analysis, the application selection of three models for different cases is accomplished. The 0-D model is the priority selection for a simple pressure prediction of tank ullage, even for the situation that severe temperature distribution exists in the ullage range. The 1-D model is the optimal selection as considering both the convenience and the time consumption for the constant-pressure cases. But it is not recommended in a constant-inlet flux cases for its distinct predicting deviation at the beginning of the process. When the detailed distributions within the tank are concerned, the CFD model is the unique selection. The results of this paper may be beneficial to the model selection and optimization analysis of a pressurization system.     

Quantification of human error in maintenance using graph theory and matrix approach

Assessment of human error in maintenance requires identification of the contributing factors that lead to human error(s). These factors are called human error inducing factors (HEIFs), which take into consideration both the active and latent error contributing aspects related to man, machine and environment. A systems approach of the Graph Theory is applied in this paper for quantifying human error in maintenance activities that models the identified factors and their interactions/interrelationships in terms of human error digraph. The nodes in the digraph represent the HEIFs and the edges represent their interrelationships. The digraph is converted into an equivalent matrix and an expression based on this is developed, which is characteristic of the human error in maintenance. This expression is used to evaluate a human error index by substituting the numerical value of the factors and their interrelations. The index is a measure of the human error potential involved in the maintenance of systems. A higher value of index indicates that the error likelihood is more for the associated tasks, and more efforts are required to make the system less prone to human error. The proposed methodology is illustrated using a case study. The approach is anticipated to play a significant role in identifying sources of human errors and predicting their impact; and will help to integrate human factors during design stage with the objective of reducing human error in maintenance. Copyright © 2011 John Wiley & Sons, Ltd.     

基于神经网络的电弧增材制造铝合金力学性能预测

目的 预测不同工艺参数下电弧增材制造铝合金的力学性能。方法 通过实验建立了电弧增材制造6061铝合金及Ti C增强6061铝合金力学性能的数据集,并建立了一种以焊接电流、焊接速度、脉冲频率、TiC颗粒含量为输入,以屈服强度和抗拉强度为输出的神经网预测模型,对比了反向传播神经网络(BP)、粒子群算法优化BP神经网络(PSO-BP)、遗传算法优化BP神经网络(GA-BP)3种预测模型的精度。结果 与BP模型和PSO-BP模型相比,GA-BP预测模型具有更好的预测精度。其中,GA-BP模型预测6061铝合金屈服强度最佳结果的相关系数(R)为0.965,决定系数(R²)为0.93,平均绝对误差(Mean Absolute Error,MAE)为2.35,均方根误差(Root Mean Square Error,RMSE)为2.67;预测Ti C增强的6061铝合金抗拉强度最佳结果的R=1,R²高达0.99,MAE为0.46,RMSE为0.49,GA-BP具有良好的预测精度。结论 BP、PSO-BP、GA-BP 3种神经网络模型可以用来预测电弧增材制造...     

穿越古长城的隧道开挖爆破振动参数预测模型

以山西省某穿越古长城的高速公路隧道爆破开挖工程为实例,根据支持向量机学习原理,建立支持向量机预测模型,以孔径、孔深、孔距、排距、单段最大装药量、总装药量和爆源距作为模型的输入参数,分别预测质点的径向、切向和垂直方向的爆破峰值振动速度及频率,并将预测值与实测值进行对比,以检验模型的精确度。结果表明,支持向量机预测模型对爆破峰值振动速度与频率的预测具有收敛快、精度高等特点,平均误差分别为11.04%、10.16%。利用该模型可以较准确地对爆破振动参数进行预测,在后续的爆破施工作业中,结合预测结果可以更好地对古长城采取有效的保护措施。     

基于背应力功的疲劳能量模型及其在轮槽构件寿命预测中的应用

基于局部应力-应变法与疲劳损伤能耗结构,以疲劳过程中背应力塑性功累积为基础,建立了一种新的缺口构件疲劳寿命预测能量模型,并将其应用于某型汽轮机轮槽结构件的疲劳寿命预测.通过与试验结果相比较,初步验证了模型的预测精度(预测寿命与试验寿命误差小于20％).此外,还进一步将上述能量模型与传统疲劳寿命预测能量方法进行了比较.结...     

基于改进狮群算法的汽轮机热耗率模型预测

针对汽轮机热消耗率模型难以精准预测的问题,提出一种基于改进的狮群算法和快速学习网综合建模的方法。首先,针对传统狮群算法易早熟收敛以及在迭代后期寻优速度缓慢导致算法陷入局部最优的缺陷,通过引入禁忌搜索、非线性扰动因子以及黄金正弦策略进行改进;其次,对改进后的狮群算法进行数值验证,结果证明其具有更高的收敛精度和收敛速度;最后,采用某热电厂汽轮机的运行数据建立汽轮机热消耗率预测模型,并将改进狮群算法优化的快速学习网对其进行热耗率预测,将实验结果与其他优化策略进行对比验证,实验结果表明,基于改进狮群算法的快速学习网预测模型具有更高的泛化能力,提高了汽轮机热耗率的预测精度。