一种图像校正的偏振光罗盘系统的研究

导航传感器在探险、应急、精确制导武器、船舶、航空器导航和定位系统中起着关键作用。针对偏振光罗盘在倾斜状态下误差较大的问题,提出了一种利用加速度计和陀螺仪对偏振光罗盘进行图像校正的方法,设计了一种基于陀螺仪校正的偏振光罗盘系统。首先利用加速度计和陀螺仪计算得到载体姿态角,对偏振光图像数据进行图像校正,采用Stokes矢量法解算大气偏振模式分布,进而提取导航特征点,最后对特征点进行拟合解算出航向角信息。并且进行了静态和动态测试实验,实验结果表明,该算法能够有效的对偏振光罗盘姿态变化引起的误差进行补偿,可以将偏振光罗盘的航向角测量误差控制在1.86°之内,平均误差为0.09°。     

智能式压力试验机课题研究

混凝土弹性模量是铁路梁场生产过程的重要控制指标,试验结果直接影响梁场生产进度与桥梁质量,而影响该项试验结果准确性的主要因素为试验设备智能化水平及操作过程信息化程度。通过本课题的研究,可提高弹模试验数据的准确性、及时性、完整性,减少弹模试验中试验人员数量,有效缩短弹模试验时间,实现弹性模量试验数据上传至工程管理平台,通过大数据对试验结果进行分析总结,为铁路领域梁体质量控制提供技术保障。     

以“互联网+”促进学前教育信息化建设

云计算、大数据、人工智能等技术的快速发展，有力地促进了“互联网+教育”的普及和应用。学前教育作为“互联网+”的重要应用领域，可以充分地利用人工智能、物联网、大数据等技术，实现学前教育硬件设备的互联互通，利用大数据搜索与推荐学前教育资源，基于人工智能开发可视化、虚拟化的学前教育课程，培养儿童的学习积极性和主动性，让学生养成一种良好的学习习惯，具有重要作用和意义。     

Facebook User Research Using a Probability‐Based Sample and Behavioral Data

Research on Facebook users is often based on small convenience samples and on usage data collected through survey self‐reports. The current research contributes to Facebook user research, as it is based on a large, nationally representative, probability‐based, U.S. sample with Internet usage data collected from meters. Results revealed that 50% of sample members are recent Facebook users. However, within this group, there is wide variation in amount of usage between heavy, medium, and light users. Finally, based on a multivariate analysis, Facebook users are significantly more likely to be women, teens, whites, and adults with at least a high school diploma. These demographic patterns apply to heavy, medium, and light Facebook users.     

RWO-Sampling: A random walk over-sampling approach to imbalanced data classification

This study investigates how to alleviate the class imbalance problems for constructing unbiased classifiers when instances in one class are more than that in another. Since keeping the data distribution unchanged and expanding class boundaries after synthetic samples have been added influence the classification performance greatly, we take into account the above two factors, and propose a Random Walk Over-Sampling approach (RWO-Sampling) to balancing different class samples by creating synthetic samples through randomly walking from the real data. When some conditions are satisfied, it can be proved that, both the expected average and the standard deviation of the generated samples equal to that of the original minority class data. RWO-Sampling also expands the minority class boundary after synthetic samples have been generated. In this work, we perform a broad experimental evaluation, and experimental results show that, RWO-Sampling statistically does much better than alternative methods on imbalanced data sets when implementing common baseline algorithms.     

电动汽车动力锂电池模型参数辨识

针对纯电动汽车上动力锂电池等效模型参数辨识的问题,以某纯电动汽车的由87个单体串联的84 Ah的镍钴锰三元锂电池组为研究对象,基于市区行驶的电池数据,选用了二阶RC电池等效模型,辨识了等效模型的参数。基于整体电池数据,选取出了8段在连续的12个(及12个以上)采样周期内相邻两个采样点的电流变化绝对值超过0.2 C的电池数据段,分别对初始开路电压最大和最小的单体进行了参数辨识。以最小二乘法对电流连续变化最长的一段电池数据段作为参数辨识的结果,并结合整体电池数据对辨识得的结果进行了参数的验证。研究结果表明,初始开路电压最大单体的绝对误差平均值为3.62%,初始开路电压最小单体的绝对误差平均值为3.24%,满足工程要求,可运用于工程实践中。     

Highly predictive support vector machine (SVM) models for anthrax toxin lethal factor (LF) inhibitors

Anthrax is a highly lethal, acute infectious disease caused by the rod-shaped, Gram-positive bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF), a zinc metalloprotease secreted by the bacilli, plays a key role in anthrax pathogenesis and is chiefly responsible for anthrax-related toxemia and host death, partly via inactivation of mitogen-activated protein kinase kinase (MAPKK) enzymes and consequent disruption of key cellular signaling pathways. Antibiotics such as fluoroquinolones are capable of clearing the bacilli but have no effect on LF-mediated toxemia; LF itself therefore remains the preferred target for toxin inactivation. However, currently no LF inhibitor is available on the market as a therapeutic, partly due to the insufficiency of existing LF inhibitor scaffolds in terms of efficacy, selectivity, and toxicity. In the current work, we present novel support vector machine (SVM) models with high prediction accuracy that are designed to rapidly identify potential novel, structurally diverse LF inhibitor chemical matter from compound libraries. These SVM models were trained and validated using 508 compounds with published LF biological activity data and 847 inactive compounds deposited in the Pub Chem BioAssay database. One model, M1, demonstrated particularly favorable selectivity toward highly active compounds by correctly predicting 39 (95.12%) out of 41 nanomolar-level LF inhibitors, 46 (93.88%) out of 49 inactives, and 844 (99.65%) out of 847 Pub Chem inactives in external, unbiased test sets. These models are expected to facilitate the prediction of LF inhibitory activity for existing molecules, as well as identification of novel potential LF inhibitors from large datasets.