Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

In the conventional technique,in the evaluation of the severity index,clustering and loading suffer from more iteration leading to more com-putational delay.Hence this research article identifies,a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects.The polynomial load modelling or ZIP(constant impedances(Z),Constant Current(I)and Constant active power(P))is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security.The process of finding the severity of the line using a Hybrid Line Stability Ranking Index(HLSRI)is used for assisting the concepts of machine learning with J48 algorithm,infers the superior affected lines by adopting the IEEE standards in concern to be compensated in maintaining the power system stability.The simulation is performed in the WEKA environment and deals with the supervisor learning in order based on severity to ensure the safety of power system.The Unified Power Flow Controller(UPFC),facts devices for the purpose of compensating the losses by maintaining the voltage characteristics.The finite element analysis findings are compared with the existing procedures and numerical equations for authentications.

An Efficient Algorithm for Processing Partialmax/min Queries in OLAP

In the process of analyzing the large-scale network security situation,the data we faced are always flooded and messy,and the information is difficult to obtain with respond to the query timely.Online analytical processing which use the Data-cube as a data source directly,calculated all or part of the Data-cube in advance,and it can reduce the query response time significantly.This paper considers a class of queries,called the Partial-MAX/MIN query.We introduce Rank Decision Tree(RD-Tree) and it’s searching algorithm for efficient processing of the partial-max/min queries.Through experiments,we show our approach has an efficient processing capability for partial-max/min queries.

Materials Selection Method Combined with Different MADM Methods

Materials selection is a multiple attribute decision making(MADM)problem.A lot of MADM methods are applicable to materials selection,and it may produce considerable differences between the results of materials selection.But it is unknown which MADM method is better.So it is desirable to decide reasonable final result of materials selection in consideration of the individual results from different MADM methods.In this paper,materials selection method combined with different MADM methods is proposed.The method is based on final ranks of alternative materials,where the final ranks are determined from the ranks of the alternative materials using different MADM methods.This method is applied to select optimal magnesium alloy material for automobile wheels.This method may be widely used to select optimal material in engineering practice.

Selection of Maize Genotypes Resistant to Pink Stem Borer and Sugarcane Borer

Maize is an important economic crop grown in Nigeria. Its production is dramatically affect by the pink stem borer--Sesamia caIamistis （Hampson, Noctuidae） and sugarcane borer--Eldana saccharina （Walker, Pyralidae） that are endemic in Southeastern Nigeria. In areas of stress, existing genotypes may marginally do well due to their inherent capabilities. Therefore, it is possible to find useful genes in such areas of stress, since such genes have been responsible for the survival of host crops over the years. Evaluation study was conducted for a range of agronomic characteristics and resistance attributes for 209 local maize collections from Southeastern Nigeria along with three improved check varieties. Field trials were conducted at three locations in a total of four environments in 2001. Highly significant genotypic variances as were noted in all the traits, are indicative of the magnitude of variation that exists among the genotypes, thus providing the opportunity of selection for desirable traits. Furthermore, four traits, namely, leaf feeding, ear damage, stalk lodging and yield were used from across the environments to construct a rank summation index （RSI）, which was used to rank the entries for resistance to stem borers. This RSI led to the identification of 11 genotypes which represents the best 5% of the 212 genotypes in resistance ability. Genotype AMA TZBR-WC1 （from International Institute of Tropical Agriculture （IITA）, Ibadan） had the best overall resistance levels, followed by genotypes SE NG-77 and SE NG-67 （from Umuahia North）, SE NG-62 （from Ikwuano）, SE NG-148 （from Ukwa West）, SE NG-106 （from Bende）, SE NG-119 （from Isiala Ngwa）, SE NG-33 （from Ikwuano） and SE NG-65 （from Umuahia North）.

Cloudy fuzzy inventory model under imperfect production process with demand dependent production rate

The aim of this article is an effort to initiate the cloudy fuzzy number in developing classical economic production lot-size model of an item produced in scrappy production process with fixed ordering cost and without shortages.Here,the market value of an item is cloudy fuzzy number and the production rate is demand dependent.In general,fuzziness of any parameter remains fixed over time,but in practice,fuzziness of parameter begins to reduce as time progresses because of collected experience and knowledge that motivates to take cloudy fuzzy number.The model is solved in a crisp,general fuzzy and cloudy fuzzy environment using Yager’s index method and De and Beg’s ranking index method and comparisons are made for all cases and better results obtained in the cloudy fuzzy model.The model is solved by dominance based Particle Swarm Optimization algorithm to obtain optimal decision and numerical examples and sensitivity analyses are presented to justify the notion.