Cost prediction method based on an improved fuzzy model

Scientific cost prediction is the foundation for formulating a business plan, which helps enterprises to determine the target costs and to control the operating costs. In today’s world, quantitative methods are crucial for prediction purposes in cost management. On the basis of studying some typical methods of cost prediction, we propose a cost prediction method based on an improved fuzzy model. Firstly, owning to the inefficiency of existing fuzzifier, such that it is difficult to determine the number of fuzzy rules, we present an improved fuzzy c-means (FCM) fuzzifier to solve this problem referring to “granulation–degranulation” strategy and sample error function. Secondly, a fuzzy model based on the improved FCM fuzzifier is constructed through combining Takagi–Sugen–Kang fuzzy inference system. The algorithm of the improved fuzzy model is also realized by this study. Finally, through a series of experimental analyses for steel number product and steel class product of certain machinery enterprise, the results have demonstrated that the proposed method has the better prediction accuracy, which can be used effectively and practically in real-world cost prediction.     

Mathematical modeling of a vehicle crash test based on elasto-plastic unloading scenarios of spring-mass models

This paper investigates the usability of spring which exhibit nonlinear force-deflection characteristic in the area of mathematical modeling of vehicle crash. We present a method which allows us to obtain parameters of the spring-mass model basing on the full-scale experimental data analysis. Since vehicle collision is a dynamic event, it involves such phenomena as rebound and energy dissipation. Three different spring unloading scenarios (elastic, plastic, and elasto-plastic) are covered and their suitability for vehicle collision simulation is evaluated. Subsequently we assess which of those models fits the best to the real car’s behavior not only in terms of kinematic responses but also in terms of energy distribution.     

Collaborative fuzzy clustering

In this study, we introduce a new clustering architecture in which several subsets of patterns can be processed together with an objective of finding a structure that is common to all of them. To reveal this structure, the clustering algorithms operating on the separate subsets of data collaborate by exchanging information about local partition matrices. In this sense, the required communication links are established at the level of information granules (more specifically, fuzzy sets forming the partition matrices) rather than patterns that are directly available in the databases. We discuss how this form of collaboration helps meet requirements of data confidentiality. A detailed clustering algorithm is developed on a basis of the standard FCM method and illustrated by means of numeric examples.     

The effects of feeding conjugated linoleic acid (CLA) on rat growth performance,serum lipoproteins and subsequent lipid composition of selected rat tissues

Four groups of 12 albino rats (six males and six females), at 4 weeks of age, were fed for 60 days a semi‐synthetic diet containing graded amounts of conjugated linoleic acid (CLA; 0.0, 0.5, 1.0 and 1.5%) to determine its effect on growth performance and serum lipoprotein profiles and its incorporation into abdominal and muscle lipids. Rats fed CLA responded by significantly improved body mass gains; however, this effect was observed only with the 1.0% CLA‐supplemented diet. It was also true for feed conversion efficiency. The changes in serum total cholesterol (TC) and HDL cholesterol concentrations were insignificant and showed an opposite pattern. However, the resulting HDL cholesterol/TC ratio was significantly (P ≤ 0.01) increased. At the same time, serum triacylglycerol (TAG) concentrations were significantly (P ≤ 0.01) elevated, thus counteracting the potential antiatherogenicity of the improved HDL cholesterol/TC ratio. Feeding CLA to rats resulted in its substantial incorporation into both abdominal and muscle lipids. Generally, tissue lipids were enriched with CLA at the expense of long‐chain polyunsaturated fatty acids, eg linoleate and arachidonate. This effect is likely to correspond with reduction of arachidonate‐derived eicosanoid synthesis. These data together suggest that CLA has a potent effect on lipid transport and metabolism in rats. Also, they confirm the potential of nutritional methods to provide CLA‐enriched functional animal products, especially meat, for human consumption. © 2000 Society of Chemical Industry     

Sentiment Analysis as a Source of Gaining Competitive Advantage on the Electricity Markets

The electricity retail markets are evolving toward more competitive and customer-oriented. The deployment of smart meters and a wealth of new technologies create customers' eagerness for taking control of their electricity consumption. By being better-informed about the energy usage, people are encouraged to switch deals among existing suppliers or move to a new energy provider. Moreover, as customers are more socially interconnected, the Internet portals and social media become a place for discussion, comparison, and evaluation of the available offers. Unfortunately, in case of the energy sector there is a lack of understanding that such information, when taken into account and properly analyzed, can be a completely new and a powerful source of competitive advantage.In the paper, we introduce a solution that the use of quasi real-time automated sentiment analysis on the energy suppliers and the relevant aspects of their offers may enable energy companies to adapt quickly to changing circumstances, prevent potential customer churn, and harness new business opportunities.     

The effect of mining face’s direction on the observed seismic activity

International Journal of Coal Science & Technology - A major natural hazard associated with LGOM (Legnica-Glogow Copper Mining) mining is the dynamic phenomena occurrence, physically observed...     

Analysis and design of rank-based classifiers

In this paper, we propose a method to predict the presence or absence of correct classification results in classification problems with many classes and the output of the classifier is provided in the form of a ranking list. This problem differs from the “traditional” classification tasks encountered in pattern recognition. While the original problem of forming a ranking of the most likely classes can be solved by running several classification methods, the analysis presented here is moved one step further. The main objective is to analyse (classify) the provided rankings (an ordered list of rankings of a fixed length) and decide whether the “true” class is present on this list. With this regard, a two-class classification problem is formulated where the underlying feature space is built through a characterization of the ranking lists. Experimental results obtained for synthetic data as well as real world face identification data are presented.     

Canonical structures of DC-AC thyristor converters

Realizability conditions of periodic waveforms in passive switched circuits are presented. Formulae for an identification of state matrices in switched circuits are derived. As input data, time functions of excitation and response are chosen. Six general transformer structures of 1-port thyristor converters have been obtained as a result of the synthesis method. The synthesis methods presented can be applied to create new thyristor and transistor arrangements, particularly thyristor compensators.