Load Curtailment Strategy by Near in Bulk Power Systems Reliability Evaluation

In order to improve the existing method of electric network reliability evaluation, the basic concepts, for example, the flow through the failure events, the sent ends and received ends of these failure events, the load curtailment region of the received ends and the degree of the load curtailment, are defined. With these concepts, the connotation of LCSN (Load Curtail Strategy by Near) is enriched and algorithms of the load curtailment region are conducted. The theory foundation for further exploring LCSN is created. The results show that the algorithm can quickly and effectively make reliability assessment of power systems.     

Dairy cattle behavior classifications based on decision tree learning using 3‐axis neck‐mounted accelerometers

Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three‐axis neck‐mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross‐validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three‐axis accelerometers mounted to a collar.     

The Classification of Grassland Types Based on Object-Based Image Analysis with Multisource Data

The spatial distribution of different grassland types is important for effectively analyzing spatial patterns, obtaining key vegetation parameters using remote sensing (e.g., biomass, leaf area index, net primary production), and using and protecting grasslands. Existing classifications of grasslands by remote sensing are mostly divided according to the fractional vegetation cover or biomass, but classifications according to grassland types are scarce. In this study, we focused on the classification of different grassland types using remote sensing based on object-based image analysis (OBIA) with multitemporal images in combination with a 30-m digital elevation model (DEM) and the normalized difference vegetation index (NDVI). The grasslands were located in Hulunber, Inner Mongolia, and an autonomous region of China. The support vector machine (SVM) and random forest (RF) machine learning classifiers were selected for the classification. The results revealed the following: 1) It is feasible to generally extract different grassland types on the basis of OBIA with multisource data; the overall classification accuracy and Kappa value exceeded 90% and 0.9, respectively, using the SVM and RF machine learning classifiers, and the classification accuracy of the different grassland types ranged from 61.64% to 98.71%; 2) Multitemporal images and auxiliary data (DEM and NDVI) improved the separability of different grassland types. The information in the growing season was conducive for distinguishing temperate meadow steppe from temperate steppe and was favorable for extracting lowland meadow and swamp in the nongrowing season. The DEM and NDVI also effectively reduced the number of image segmentation objects and improved the segmentation effects; 3) Spectral and textural features were more important than geometric features in this study. A few main variables played a major role in the classification, while a large number of variables had either no significant effect or a negative effect on the classification results when the optimal feature subset was determined. This study provides a scientific basis and reference for the classification of various grassland types by remote sensing, including the data selection, image segmentation, feature selection, classifier selection, and parameter settings.     

动物分子生物学课程教学改革探索

动物分子生物学是动物科学和动物医学等专业的专业基础课之一。随着课程知识的不断更新,尤其是数据库时代的到来,之前的教学模式和教学内容已跟不上现代教学需求。针对以动物科学等专业为授课对象的动物分子生物学课程存在的问题,从优化教学内容、增加教学方式和多元考核方式进行教学改革和探索。通过课程教学改革,增加课程的吸引力和学生学习的积极性,提高教学质量,为培养优秀的动物科学等专业的创新型科研人才奠定基础。     

USE OF CVS FOR REFINING MID-SEASON FERTILIZATION NITROGEN-RATES IN WINTER WHEAT

The GreenSeeker? optical sensor is used as a management decision aid in many crops across the world. This sensor measures the normalized difference vegetative index (NDVI). It has been observed that when the by-plot coefficients of variation (CV) from the GreenSeeker? sensor NDVI readings in winter wheat (Triticum aestivum L.) was incorporated into the midseason calculation of the response index (RI), RI at harvest was better predicted than with NDVI alone. This study further evaluated the use of CV's collected from both small 1.48 m² and large 17.0 m² areas. Trials consisted of 3 seeding rates by 4 nitrogen rates. Results from this study showed that CV from NDVI readings and plant population were negatively correlated. Improved prediction of RI by utilizing CV was not found. It was however observed that when CV's were less than 5.0 and NDVI values were greater than 0.80 the RI was less than 1.2.     

图书馆员职业倦怠工作的探讨

职业倦怠已经成为图书馆职业的隐形杀手，这种不健康的心态严重影响和谐图书馆的建设，是馆员与读者、同事之间交流沟通的最大障碍。分析了职业倦怠在工作中的具体表象、形成的原因以及对工作的影响，从心理介入和终身学习等角度提出了解决的对策。     

柴油机SCR载体SOF沉积量估算模型与参数辨识

针对柴油机选择性催化还原(Selective Catalytic Reduction,SCR)载体由于可溶性有机物(Soluble Organic Fraction,SOF)沉积导致SCR的NO_x转化效率低的问题,该研究首先建立了SOF沉积量估算模型,并进行SOF原始排放与SCR载体的SOF捕集效率试验研究;然后利用Matlab/Simulink软件工具建立SOF沉积量估算模型,包括SOF原始排放模块、SCR载体对SOF捕集模块、SOF热解模块;最后基于多目标遗传算法,进行SOF瞬态修正脉谱优化和热解参数辨识,并探索惩罚函数的应用规律,使得4组SOF低温沉积量平均估算误差为2.42%,12组高温热解平均估算误差为4.03%。该研究可为解决柴油机SCR载体由于SOF沉积导致NO_x转化效率低的问题提供技术参考。     

基于IPSO-BP模型的粮食产量预测方法研究

针对粮食产量预测的复杂性,以基本微粒群算法(PSO)为基础,引入繁殖和变异机制,提出一种改进的微粒群算法(IPSO)优化BP神经网络的连接权值和阈值。综合考虑影响粮食产量的有关因素,构建出IPSO-BP的粮食产量预测模型,并以江苏省1978—2018年的粮食产量及影响其产量的10个因素作为数据集进行仿真试验。试验结果表明,与基本PSO-BP神经网络模型及BP神经网络模型相比,基于IPSO-BP神经网络模型获得的2016年、2017年、2018年粮食产量预测结果最优相对误差分别为0.24%、0.25%、0.06%,平均相对误差0.76%、0.67%、0.38%,该模型的预测精度及稳定性大幅提高。     

边缘检测算法比较分析

阐述了数字图像处理中常用的经典边缘检测各类算子的算法原理、计算方法,包括一阶经典检测微分算子、二阶经典检测微分算子,在明晰原理方法的基础上对比分析各个算子的优劣之处,分析各个算子的效率、精确程度,同时分析各个算子各自擅长处理的图像类型。通过对边缘检测算子的比较分析,可以较全面地了解经典边缘检测算子的不同适用情况,为实际应用准确选择最合适的算子。     

基于设计型学习的《信息技术》教学改革与实践——以盐城纺织职业技术学院为例

介绍了设计型学习的内涵和特征,在此基础上,用具体的案例详细介绍该方法进行教学的过程。通过在盐城纺织职业技术学院的教学实践,证明这种学习方法可以提高学生解决实际问题的能力和自主设计、自主学习的能力。