适用于长距离输电网络的通用电压崩溃指标(英文)

首先提出一个广义电压崩溃指标(Generalized Voltage Collapse Index,缩写为GVCI),定义为含有大容抗的长距离辐射型输电系统(架空线或海底电缆)距离最大稳定极限负荷的当前负荷裕度.GVCI为瞬时值,可用于保护和控制装置的整定,通过相量测量单元(PMU)所提供的局部测量值实现控制和保护功能.用一个简单的两母线双回线系统对所提出的GVCI指标的有效性进行了测试,通过扩展GVCI,用解析方式分析有载调压变压器(OLTC)的运行对电压崩溃的影响;并将GVCI与分析电压崩溃时常用的L指标进行了比较,讨论GVCI在地理位置偏远的风力发电场的可能应用.

Common Bean Germplasm Diversity Study for Cold Tolerance in Ethiopia

Limited tolerance of common beans (Phaseolus vulgaris L.) to cold temperatures hinders an additional harvest during the small rainy season crop cycle (February to May) in the Ethiopian highlands that comprise two-thirds of the country. Therefore, identification of cold tolerant common bean genotypes is of paramount importance for the region. Field screening of 99 common bean genotypes originally procured from CIAT (International Center for Tropical Agriculture) was carried out for nine different plant traits associated with crop growth and yield at two locations differing in climates: Dire Dawa-higher daily maximum and minimum temperatures and absence of near zero chilling temperatures from February to May;Haramaya-lower daily maximum and minimum temperatures and occasionally near zero chilling temperatures during this period. The analysis of variance (ANOVA) showed the existence of significant variation among genotypes for the parameters measured. Principal component analysis (PCA) was carried out to assess the variation and correlation among genotypes for the traits and group them based on their performance at the two locations. The combination of first three principal components explained more than 50% of the genotypic variations. Principal component analysis was also able to discriminate the performance of genotypes between the two locations. It was grouped into at least 17 genotypes that were specific to Haramaya highland location. The results also revealed significant variation in performance among the 17 genotypes. These genotypes are specific to Ethiopian highlands and prominent resources for in-situ conservation of germplasms.

A Feature Subset Selection Technique for High Dimensional Data Using Symmetric Uncertainty

With the abundance of exceptionally High Dimensional data, feature selection has become an essential element in the Data Mining process. In this paper, we investigate the problem of efficient feature selection for classification on High Dimensional datasets. We present a novel filter based approach for feature selection that sorts out the features based on a score and then we measure the performance of four different Data Mining classification algorithms on the resulting data. In the proposed approach, we partition the sorted feature and search the important feature in forward manner as well as in reversed manner, while starting from first and last feature simultaneously in the sorted list. The proposed approach is highly scalable and effective as it parallelizes over both attribute and tuples simultaneously allowing us to evaluate many of potential features for High Dimensional datasets. The newly proposed framework for feature selection is experimentally shown to be very valuable with real and synthetic High Dimensional datasets which improve the precision of selected features. We have also tested it to measure classification accuracy against various feature selection process.

Removal of Fluoride from Groundwater by Carbonised <i>Punica granatum</i>Carbon (“CPGC”) Bio-Adsorbent

This study applies the development and application of low cost, Punica granatum bio-adsorbent for the removal of fluoride in groundwater. The batch adsorption study was carried out to analyze the defluoridation by contact time variation, adsorbent dose, adsorbate concentration, adsorbent particle size and presence of co-anions at neutral pH. The analysis of the isotherm equilibrium data using the Langmuir and Freundlich equations by linear methods showed that the data fitted better with Freundlich model (R2 > 0.980). Prepared adsorbent showed enhanced removal of fluoride concentration by 78.1% at equilibrium contact time of 75 minutes. Carbonised Punica granatum Carbon (CPGC) seeds showed a high affinity for fluoride ions compared with other conventional adsorbents. Therefore, it can be considered as a potentially “good”, low-cost bio-adsorbent for de-fluoridation of water compared to other bio-adsorbent.

巴拉特·辛格作品欣赏

无论是这个世界还是我们的生命,自产生之初,就面临着诸多自然及人为灾害。最近这些年,我们目睹了太多的灾难,四川、海地地震,南亚苏娜米海啸,以及如海湾战争、“9·11事件”这类人类自己制造的不幸,不计其数的生命因此离我们远去。所有这些驱使着我站入祈祷者的行列,为世界和平而祈祷。这也是我近期作品的主题。在这一作品中我想展示我们人类站在一起,为我们共同拥有的这一世界的和平而祈祷。

Kinematic Modeling for Biped Robot Gait Trajectory Using Machine Learning Techniques

This paper presents the predictive models for biped robot trajectory generation.Predictive models are parametrizing as a continuous function of joint angle trajectories.In a previous work,the authors had collected the human locomotion dataset at RAMAN Lab,MNIT,Jaipur,India.The MNIT gait dataset consists of walking data on a plane surface of 120 human subjects from different age groups and genders.Thirty-two machine learning models(linear,support vector,k-nearest neighbor,ensemble,probabilistic,and deep learning)trained using the collected dataset.In addition,two types of mapping,(a)one-to-one and(b)many-to-one,are presented for each model.These mapping models act as a reference policy for the control of joints and prediction of state for the next time instant in advance if the onboard sensor fails.Results show that the deep learning and probabilistic learning models perform better for both types of mappings.Also,the probabilistic model outperforms the deep learning-based models in terms of maximum error,because the probabilistic model effectively deals with the prediction uncertainty.In addition,many-to-one outperforms the one-to-one mapping because it captures the correlation between knee,hip,and ankle trajectories.Therefore,this study suggests a many-to-one mapping using the probabilistic model for biped robot trajectory generation.