一种新型回路电阻测试仪的研究与设计

针对传统低压回路电阻测试仪只能在被测设备停电时使用的一大限制,本文提出并设计了一种新型低压回路电阻测试仪。该仪器可在低压设备运行状态下对其回路电阻进行测量,在保证测量精度的前提下减少了设备停电率,大大提高了工作效率。     

Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Synthesis,structure, CO oxidation,and H2 production activities of CaCu3−xMnxTi4−xMnxO12 (x = 0, 0.5, and 1.0)

Synthesis of nanocrystalline pristine and Mn-doped calcium copper titanate quadruple perovskites, CaCu_3?xMn_xTi_4?xMn_xO₁₂ (x = 0, 0.5, and 1.0) by modified citrate solution combustion method has been reported. Powder X-ray diffraction patterns attest the phase purity of the perovskite materials. Average particle sizes of all the materials obtained from the Scherrer's formula are in the range of 55–70 nm. The specific surface areas for all the perovskites obtained from BET isotherms are found to be low as expected for the condensed oxide systems and fall in the range of 13–17 m² g^?1. Transmission electron microscopy studies show a reduction in particle size of CaCu₃Ti₄O₁₂ with increase in Mn doping. Ca and Ti are present in +2 and +4 oxidation states in all the materials as demonstrated by X-ray photoelectron spectroscopy analyses. Cu²⁺ gets reduced in CaCu₃Ti₄O₁₂ with higher Mn content. Mn is observed to be present only in +3 oxidation state. All the materials have been examined to be active in CO oxidation as well as H₂ production from methanol steam reforming. CaCu₃Ti₄O₁₂ with ~14 at.% Mn is found to show best catalytic activities among these materials. A comprehensive analysis of the catalytic activities of these perovskites toward CO oxidation and H₂ production from MSR reveal the cooperative activity of copper-manganese in the doped perovskites and it is more effective at lower manganese content.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

最大塑性功原理在烧结体上限分析中的应用

最大塑性功原理又称第二塑性变分原理，在致密体塑性变形分析叶l是运用能量法进行变形力能计算的基础。本文从分析Drucker公设的亢要条件出发，证明了烧结体塑性变形最大塑性功原理的存在，片在该原理的荩础卜，推导出塑性变形的上限功率表达式，并以平面挤压条件下的上限法应用作为实例加以说明。     

指纹考勤管理软件的研究与实现

针对企、事业单位原有的考勤系统，如打卡钟、磁卡、IC卡等考勤方式存在代打卡、卡丢失等不足和缺陷的实际，结合指纹识别技术，建立一套考勤、薪资、人事管理于一体的软件系统。给出了系统的体系结构和功能实现，能使企业考勤管理更加简便快捷、客观公正。并提出了进一步的发展方向和展望。     

高分辨率阵列感应测井仪在河南油田的应用

文章介绍了美国Baker-Atlas公司研制生产的高分辨率阵列感应成像测井仪（HDIL）,简述了该仪器的工作原理和技术性能指标及主要应用范围,并介绍了高分辨率阵列感应测井（HDIL）资料在河南南阳油田的具体应用实例.