基于多态数据采集的电力巡检驾驶舱故障预警与辅助决策

采用Pearson法、大数据挖掘法进行驾驶舱故障预警电力巡检时,存在电力巡检数据遗漏问题,导致故障预警准确率低,为此提出一种基于多态数据采集的电力巡检驾驶舱故障预警与辅助决策方法。设计驾驶舱故障预警与辅助决策结构图,采集电力巡检驾驶舱的多态故障数据;以多态数据为基础,判断电力系统故障的发生情况;通过数据预处理进行故障筛选,并以此为基础进行电力系统故障预警;根据EEAC理论生成驾驶舱故障辅助决策方法。为验证该方法的预警效果,设计了相关对比实验。结果表明,当迭代次数为2 000时,该方法的故障预警准确率为90%,故障预警效果较好。

基于多态数据融合的断路器远程故障诊断智能模型

断路器是电力系统的重要组成部分,断路器故障会直接降低电力系统运行的稳定性。为了给断路器的检修提供有价值的参考,提出基于多态数据融合的断路器远程故障诊断智能模型。根据不同故障类型下断路器的运行特征,设置故障诊断标准。充分考虑断路器的组成结构和工作原理,利用传感器设备远程采集断路器的实时运行数据。利用多态数据融合技术处理初始采集数据,从振动、电流和电压3个方面提取数据特征。通过特征匹配,得出断路器的智能故障诊断结果。通过与传统模型的对比发现,优化设计模型的故障诊断正确率提高了4.1%,且时效性得到明显提升。

面向对象C++语言中多态数据结构的研究

多态是面向对象程序设计的重要机制。多态数据结构是多态性机制的一种表现形式。通过分析继承结合动态联编机制并利用支持运行时多态性的虚函数和抽象类的特征,得出构建多态数据结构的基本方法,并以多态数组和多态队列两个实例说明多态数据结构在面向对象程序设计C++语言中的具体实现。

基于多态和模板的数据结构算法设计

基于对面向对象的软件分析和设计技术的研究,作者在文中提出了用面向对象的方法描述数据结构,即在数据结构课程中把面象对象编程中的多态和模板等技术相结合,以获得极高层次上的具有可复用性的泛型组件,从而使教学和现代计算机技术紧密结合,增强了传统课程的实用性。

新型磁电阻探索与多态数据存储应用

随着现代信息存储与通信技术的快速发展,人们希望新型的电子器件能兼具低功耗、非易失、高密度等优异性能,甚至能实现信息的存储、处理与通信三位一体的功能,因而对材料和器件的研究提出了巨大的挑战.为满足上述需求,我们课题组一方面在多种新材料和器件中探索新的磁电阻效应,深入理解自旋相关输运的物理机理,进而获得有效调控自旋相关输运的新途径;另一方面,基于多物理效应调控材料和器件的磁电阻,进而获得可用于多态信息存储的自旋电子学原型器件.在新型磁电阻探索方面,本文将介绍非晶浓磁半导体的负磁电阻、单晶CoZnO磁性半导体硬带跃迁区的正磁电阻、非磁肖特基异质结中的新型整流磁电阻以及非对称势垒磁性隧道结中的隧穿整流磁电阻.在多态数据存储方面,本文将介绍氧化物异质结中可用电磁场调控的4电阻态、磁性异质结中基于剩磁调控的10电阻态以及磁性单层膜中基于自旋轨道矩效应的10电阻态.

基于多态性数据库的红豆杉提取物药效及不良反应的相关性

红豆杉提取物因为其功效被医疗行业所采用,但是对于红豆杉提取物临床存在的不良反应研究并不是很深入,因此提出基于多态性数据库的红豆杉提取物药效及不良反应的相关性研究。利用多态性数据对红豆杉提取物进行药效分析,设计制作微流控芯片从而测定红豆杉提取物药效,考察红豆杉提取物药效特性,并对红豆杉提取物存在的不良反应症状进行检测,分析不良反应存在的概率,从而利用多态性数据确定相关性。实验研究表明,红豆杉提取物药效、不良反应与细胞磷脂小分子的作用密切相关。

基于统一坐标系的多源数据入库方法设计

对空间地理信息数据的快速获取和使用是铁路工程建设科学决策的基础,然而,不同时期、不同方法获取的形态数据存在坐标系统、高程系统和数据格式不统一的问题,导致不同形态数据不能有效利用。针对上述难题,本文提出将多源多时期的DLG、DEM、卫星遥感影像等数据放置在基于CGCS2000椭球地理坐标系的数据库进行数据管理,通过基于ArcGIS Engine的二次开发处理系统解决对同一区域的不同数据的坐标转换、格式转换等一系列问题,并在工程实践进行相应的精度验证。研究成果可为铁路工程建设及智能化应用地理空间信息数据提供技术支持。

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Stock trend prediction method coupled with multilevel indicators

To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM s prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.

黄海西部降水pH值的年度及季节统计特征

采用内核密度估计方法对黄海西部降水pH值年度/季节特征进行了分析.结果显示,降水pH值年度内核密度估计图相似,呈典型双态分布,以低pH值峰为主.季节性分布特征春、夏、冬季相近,低pH值峰为主,秋季高pH值峰为主.该海域降水pH值特征是陆源输入与海源输入相互影响的结果.根据数据非正态分布特点,采用bootstrap模拟取样获得的降水pH代表值显示,黄海西部降水总体呈弱酸雨特征,年度与季节性pH代表值都呈弱酸性,且年度降水pH值变化范围要大于季节性变化,降水酸性依次为春季>冬季>秋季>夏季,季节性特征较显著.

随机介质背景下的空频TR-MUSIC成像方法

针对空空时间反转多信号分类(time reversal multiple signal classification,TR-MUSIC)抗噪性能差而难以实现对复杂随机介质影响下目标的聚焦成像,以及空空多态数据矩阵的获取较为复杂等问题,提出基于空频分解的时间反转成像新方法,即空频TR-MUSIC。该方法利用天线阵列采集的散射场回波信号建立空频多态数据矩阵,对该矩阵进行奇异值分解得到噪声子空间向量,从而实现对目标的成像。基于完全散射场数据的成像函数包含多个子矩阵的贡献,具有统计特性。仿真结果表明,无论是在自由空间中还是在随机介质背景下,空频TR-MUSIC的成像效果均优于传统的空空TR-MUSIC,具有较好的分辨率和定位精度。即使在信噪比为10 dB的高斯白噪声影响下,也能实现对目标的准确成像。

断路器远程离线单元故障检修方法研究

断路器用于切断电力系统异常部分与其他部分之间的联系。为了保证电力系统的安全性和可靠性,提出了断路器远程离线单元故障检修方法。传感器分别采集断路器多态数据,采用小波变换实施去噪。采用集合经验模态分解(EMD)方法,提取断路器多态数据特征向量。将提取到的断路器振动信号/声音信号能量熵作为输入向量,通过概率神经网络(PNN)实施故障单独检测,并结合D-S证据理论完成断路器远程离线单元故障检修。试验结果表明:采用该方法时,断路器1在误动故障上分配的概率最高,由此可以判断出断路器1存在误动故障。检修结果与实际结果一致。800个训练样本在180次迭代时收敛值小于0.0001,说明PNN训练结果满足实际应用需求。该方法具有一定的实际应用价值。

Botulinum toxin for chronic anal fissure after biliopancreatic diversion for morbid obesity

AIM: To study the effect of botulinum toxin in patients with chronic anal fissure after biliopancreatic diversion (BPD) for severe obesity. METHODS: Fifty-nine symptomatic adults with chronic anal fissure developed after BPD were enrolled in an open label study. The outcome was evaluated clinically and by comparing the pressure of the anal sphincters before and after treatment. All data were analyzed in univariate and multivariate analysis. RESULTS: Two months after treatment, 65.4% of the patients had a healing scar. Only one patient had mild incontinence to flatus that lasted 3 wk after treatment, but this disappeared spontaneously. In the multivariate analysis of the data, two registered months after the treatment, sex (P = 0.01), baseline resting anal pressure (P = 0.02) and resting anal pressure 2 mo after treatment (P < 0.0001) were significantly related to healing rate.CONCLUSION: Botulinum toxin, despite worse results than in non-obese individuals, appears the best alternative to surgery for this group of patients with a high risk of incontinence.

CYP1A1 Ile462Val polymorphism contributes to colorectal cancer risk:A meta-analysis

AIM:To study the relation between CYP1A1 Ile462Val polymorphism and colorectal cancer risk by meta-analysis. METHODS:A meta-analysis was performed to investigate the relation between CYP1A1 Ile462Val polymorphism and colorectal cancer risk by reviewing the related studies until September 2010.Data were extracted and analyzed.Crude odds ratio(OR) with 95% confidence interval(CI) was used to assess the strength of relation between CYP1A1 Ile462Val polymorphism and colorectal cancer risk. RESULTS:Thirteen published case-control studies including 5336 cases and 6226 controls were acquired. The pooled OR with 95%CI indicated that CYP1A1 Ile462Val polymorphism was significantly related with colorectal cancer risk(Val/Val vs Ile/Ile:OR=1.47,95%CI:1.16-1.86,P=0.002;dominant model:OR= 1.33,95%CI:1.01-1.75,P=0.04;recessive model:OR=1.49,95%CI:1.18-1.88,P=0.0009) .Subgroup ethnicity analysis showed that CYP1A1 Ile462Val polymorphism was also significantly related with colorectal cancer risk in Europeans(Ile/Val vs Ile/Ile:OR=1.22,95%CI:1.05-1.42,P=0.008;dominant model:OR= 1.24,95%CI:1.07-1.43,P=0.004) and Asians(Val/ Val vs Ile/Ile:OR=1.40,95%CI:1.07-1.82,P=0.01;recessive model:OR=1.46,95%CI:1.12-1.89,P= 0.005) . CONCLUSION:CYP1A1 Ile462Val may be an increased risk factor for colorectal cancer.

Adaptive multi-modal feature fusion for far and hard object detection

In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image information.Firstly,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection task.Meanwhile,semantema of each image feature map is enhanced by semantic information from all subsequent feature maps.Secondly,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of objects.Finally,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target objects.The experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,i.e.increasing the AP by 8.78%and 5.49%on medium and hard difficulty levels.Meanwhile,our method achieves greater detection performance compared with many mainstream multi-modal methods,i.e.outperforming the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.

Climate Variability in Niger： Potential Impacts on Vegetation Distribution and Productivity

Since 2003, the sites of the national environmental monitoring system （DNSE） of Niger, set up by the long term ecological monitoring observatories network （ROSELT） with the support of the Sahel and Sahara Observatory （OSS）, were used to collect ecological data with harmonized methods for spatio-temporal comparisons purpose. Floristic and phytoecological data were collected using the phytosociological methodology of Braun-Blanquet （1932）. Ecosystem vital attributes used included the specific diversity, alpha diversity, equidistribution, biological types and herbaceous phytomass. At the whole system scale, the analysis revealed that the specific diversity, the alpha diversity and the phytomass values were higher in less disturbed biotopes of the north soudanian and south sahelian bioclimates where the rainfall rate is relatively high. Regarding the north sahelian and saharian bioclimates, the topography may play a critical role in the redistribution of this phytodiversity. Besides, the distribution of the biological types showed the prevalence of therophytes （56.8 ± 11%） regardless of the bioclimate and, to a lesser extent, the perennial species （26.5 ± 7.3%）, the later group showing higher values for the north soudanian bioclimate.

Analysis and Prototyping of Multicellular DC-DC Transformer for Environmentally Friendly Data Centers

A multicellular DCX （dc-dc transformer） using unregulated cell converters has been proposed for the environmentally friendly data centers. The high speed cell converter with the switching frequency over MHz behaves as an ideal transformer, and this behavior solves the voltage imbalance issue in the multicellular converter topology. The analysis of the unregulated cell converter is conducted by using the state space averaging method, and the operation condition for the ideal transformer is specified. The behavior of the multicellular DCX using the high speed cell converters has been also analyzed, and the voltage imbalance issue among cell converters is discussed quantitatively. A prototype of a 19.2 kW 384 V-384 V multicellular DCX using sixty-four unregulated cell converters is fabricated and the validity of the analyses is verified.

基于C-AHP的铁路货运安全监测指标自动筛选系统设计

对于铁路货运安全监测指标管理,原有的指标筛选系统由于对监测指标没有分解研究,使得指标自动筛选结果的信息贡献率较低。因此,提出基于云模型理论和层次分析法(Cloud Model-Analytic Hierarchy Process, C-AHP)的铁路货运安全监测指标自动筛选系统设计。系统硬件部分,对智能仪器、工控机进行了设计。系统软件部分,首先依据货运安全监测原则建立起初步的安全监测指标体系。其次,基于C-AHP方法计算影响货运安全的因素指标权重。最后,通过指标权重排序完成安全监测指标自动筛选。搭建好系统测试环境后,选择两种筛选系统与设计系统进行对比实验,实验结果表明:在相同的筛选时间内,应用三种系统分别对同一影响因素,与不同影响因素下指标筛选,结果显示设计系统筛选结果的信息贡献率较高,证明了该系统自动筛选结果更具合理性。

Carbon Emission Evaluation in Jinan Western New District based on Multi-source Data Fusion

Carbon emissions caused by human activities are closely related to the process of urbanization,and urban land utilization,function vitality and traffic systems are three important factors that may influence the emission levels.For clarifying the space structure of a low-carbon eco-city,and combining the concept of"Combining Assessment with Construction"to track and contrast the construction of the low-carbon eco-city,this research selects quantifiable low-carbon eco-city spatial characteristics as indicators,and evaluates and analyzes the potential carbon emissions.Taking the Jinan Western New District as an example,diversity of construction land,travel carbon emission potential,and density and accessibility of adjacent road networks in the overall urban planning were measured.After the completion of the new urban area,the evaluation mainly reflected certain factors,such as the mixed degree of urban functions,the density of urban functions,the walking distance to bus stops and the density and number of bus stops.Dividing the levels and adding equal weights after index normalization,the carbon emission potential is evaluated at the two levels of the overall and fragmented areas.The results show that:(1)The low-carbon emission potential areas in the planning scheme basically reached the planned goals.(2)There is inconsistency between districts and indicators in the planning scheme.The diversity of construction land and the accessibility of the adjacent road network are relatively small;however,there is a large difference between the travel carbon emission potential and the road network accessibility.(3)Carbon emission potential after completion did not reach the planned expectation,and the low-carbon emission potential plots were concentrated in the Changqing Old City Area and Central Area of Dangjia Town Area.(4)The carbon emission indicators varied greatly in different areas,and there were serious imbalances in the density of public transportation lines and the mixed degree of urban functions.

Unseen head pose prediction using dense multivariate label distribution

Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.