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1.
朱慧军  杨洪磊 《电子测试》2021,(2):57-58,50
对于机械设备的故障运行问题,技术人员应当深入研究机械设备的故障规律,并研究出运行趋势的预测方法,从传感器的检测时间间隔与使用数量等方面加以深入的研究。本文介绍了机械设备运行状态的故障预测方法,并将机械设备运行状态的故障预测方法总结为三个步骤,分别是数据获取、处理与设备寿命预测,结合这些内容,提出了关于机械设备故障运行的一些方法,旨在为相关技术人员提供参考依据。  相似文献   
2.
大中型煤炭企业具有与市场关联度高、风险损失大等特点,一旦出现信用风险,对企业以及社会的影响都是巨大的。为了能够准确识别煤炭企业的信用风险,本文以上市煤炭企业为研究对象,提出基于Filter-Wrapper两阶段特征选择的大中型煤炭企业信用风险评估模型。首先针对大中型煤炭企业的特点,在通用指标选择上结合煤炭企业风险因素提出两个新指标:抗风险能力、煤炭及加工产品业务销售毛利率;然后使用Filter-Wrapper两阶段特征选择算法用来筛选冗余特征,从而构建信用风险预测模型。实验表明所提出模型与筛选前相比具有更高的预测准确性,同时对信用风险违约样本识别率也更高,验证了模型与所提指标的有效性,对大中型煤炭企业的信用风险识别具有重要意义。  相似文献   
3.
Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.  相似文献   
4.
The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.  相似文献   
5.
针对采集的控制棒驱动机构(CRDM)振动信号中存在非平稳、强噪声失真信号,提出一种基于评价函数和误差反向传播(BP)网络的CRDM滚轮状态评估方法。信号经半软阈值去噪、局部均值分解(LMD)提取特征向量,特征向量组成的样本集经BP网络进行状态识别,引入评价函数对状态识别结果进行评价,依据评价结果进行失真样本剔除,保留新形成的样本集进行状态识别。结果表明,基于评价函数和BP网络的CRDM滚轮状态评估方法能有效对滚轮缺陷状态进行识别,解决了控制棒驱动机构滚轮状态难以进行准确识别的问题。   相似文献   
6.
在压水堆安全性分析中,需准确预测气液逆流极限(CCFL)工况下两相流动关系。本文采用水下淹没排气的实验方法,对相同管长不同管径垂直管的CCFL特性进行可视化实验,并对垂直管CCFL关联式模型进行分析,主要结论有:①在CCFL工况下垂直管内流型为环状流动;表观气速较大时,大管径管内液膜呈搅拌状,小管径管内液膜呈波动状;随表观气速减小,均转为液面光滑的自由降膜流动;②Wallis数模型过度关联了管径变化对垂直管CCFL特性的影响;Kutateladze数和Froude-Ohnesorge数模型也不能良好关联垂直管CCFL特性的管径效应;③提出了新的CCFL无量纲参数和相应的实验关联式,由此可使垂直管CCFL特性的管径效应得以统一表征,还可以关联物性参数变化的影响。   相似文献   
7.
目前大多数知识图谱表示学习只考虑实体和关系之间的结构知识,性能受存储知识的限制,造成知识库补全能力不稳定,而融入外部信息的知识表示方法大多只针对某一特定的外部模态信息建模,适用范围有限.因此,文中提出带有注意力模块的卷积神经网络模型.首先,考虑文本和图像两种外部模态信息,提出三种融合外部模态信息和实体的方案,获得实体的多模态表示.再通过结合通道注意力模块和空间注意力模块,增强卷积的表现力,提高知识表示的质量,提升模型的补全能力.在多个公开的多模态数据集上进行链路预测和三元组分类实验,结果表明文中模型性能较优.  相似文献   
8.
确定采空区顶板安全厚度对保证矿山作业安全具有重要意义。本文采用有限差分软件FLAC3D,基于尖点突变理论和强度折减法研究西石门铁矿采空区顶板安全系数与其厚度的函数关系,建立了顶板安全厚度判断方法。在此基础上,通过单因素试验研究了采空区纵深、跨度、高度、顶板粘聚力与抗拉强度对采空区顶板安全厚度的影响,结果表明:纵深、跨度、顶板粘聚力、抗拉强度与顶板安全厚度之间分别呈现线性、非线性正相关、非线性负相关、非线性负相关的变化关系;高度对顶板安全厚度的影响非常小。并建立了综合考虑采空区纵深、跨度、顶板粘聚力与抗拉强度4种因素的采空区顶板安全厚度预测模型,为确定采空区顶板安全厚度提供了一种新的研究方法。模型预测结果与某硫铁矿瞬变电磁勘探结果相吻合,验证了预测模型方法的科学性和有效性。  相似文献   
9.
The effect of the emergency perception of bystanders of cyberbullying victims on helping behaviors is often neglected in research on cyberbullying. In this study, we explored the influence of this cognitive factor on cyber-bystanders’ helping tendencies as well as elucidated possible underlying processes. The results of two studies were reported. In Study 1, 150 undergraduates read a true case of a girl experiencing cyberbullying. The results indicated that when the participants perceived the victim’s situation to be more critical (i.e., higher emergency perception), their helping tendencies were stronger, partly through increased state empathy followed by feelings of responsibility to help. In Study 2, we randomly assigned 300 undergraduates to two groups. The low emergency group read the same cyberbullying case as Study 1, whereas the cyberbullying case read by the high emergency group contained additional emergency information of the victim. The results indicated that the high emergency group expressed stronger helping tendencies than did the low emergency group. This effect was caused by a stronger perception that the victim was in an emergency situation, which not only strengthened the participants’ helping tendencies directly but also indirectly through increasing their state empathy and feelings of responsibility to help.  相似文献   
10.
Access to electricity is still a challenge in many parts of sub-Saharan Africa. In Nigeria, over 70% of the rural dwellers do not have access to electricity. The purpose of this paper is to examine the potential of a smart microgrid for off-grid rural electrification in Nigeria. A combination of design thinking and model-based design methodology is employed to select a suitable microgrid configuration and to develop a smart microgrid model. A system consisting of a solar photovoltaic array, battery energy storage and a diesel generator is selected, and the model is developed in Simulink. Demand data from 10 rural communities in Nigeria are used to validate the performance of the model and the potential for demand management is considered. The use of energy efficient light bulbs is found to reduce the peak electricity demand of the case study communities by 42 to 76%. Combining the proposed system with the use of LED bulbs makes the system to have 56 to 81% less net present cost than a system with a diesel generator alone and incandescent light bulbs. The proposed smart microgrid is found to be more suitable for off-grid rural electrification in Nigeria than diesel generators which are currently used for off-grid electrification in Nigeria.  相似文献   
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