A limit characterization for the number of spanning trees of graphs

In this paper we propose a limit characterization of the behaviour of classes of graphs with respect to their number of spanning trees. Let {G_n} be a sequence of graphs G₀,G₁,G₂,… that belong to a particular class. We consider graphs of the form K_n−G_n that result from the complete graph K_n after removing a set of edges that span G_n. We study the spanning tree behaviour of the sequence {K_n−G_n} when n→∞ and the number of edges of G_n scales according to n. More specifically, we define the spanning tree indicator ({G_n}), a quantity that characterizes the spanning tree behaviour of {K_n−G_n}. We derive closed formulas for the spanning tree indicators for certain well-known classes of graphs. Finally, we demonstrate that the indicator can be used to compare the spanning tree behaviour of different classes of graphs (even when their members never happen to have the same number of edges).     

使用计算机生成树木笔绘图形方法的分析

首先对使用计算机生成树木笔绘图形的方法进行了综合介绍,并分析了它具有的优点,及适用的研究对象。进而介绍和讨论了常规的树木图形生成方法,着重阐述了使用计算机自动绘制树木笔绘图形的方法,步骤,关键概念,以及它的应用。最后指出了该方法中存在的不足之处和今后需继续深入研究的方向。     

Intelligent condition monitoring using fuzzy inductive learning

Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.     

Geographical characterisation of honeys according to their mineral content and antioxidant activity using a chemometric approach

In this article, discrimination models are presented, relating the origin of honey samples to several variables, being the concentrations of different cations and anions in the honey samples measured by ion chromatography, and parameters that measure/reflect the antioxidant activity of the honey samples. The unsupervised method, principal component analysis, and supervised discrimination methods, such as linear and quadratic discriminant analysis, and classification and regression trees (CART), were applied to evaluate the existence of data patterns and the relationship between geographical origin and the measured parameters. The model with the best predictive ability (%CCR_TEST = 66.67%), the best overall % specificity (80%) and the best overall % sensitivity (67%) was found to be CART. It was proven that the mineral content and parameters analysed can provide enough information for the geographical characterisation and discrimination of honey.     

Assessment of resistance spot welding quality based on ultrasonic testing and tree-based techniques

Classification and regression tree (CART) and random forest techniques were proposed as pattern recognition tools for classification of ultrasonic oscillograms of resistance spot welding (RSW) joints. The results showed that CART models produced an acceptable error rate with high interpretability. These features may be used to understand and control the decision processes, instruct other human operators, compare margins of safety or modify them depending on the criticality of the industrial process. Compared with CART trees, random forests reduced the error rate at the cost of decreasing decision interpretability. The use of the agreement of the forest was proposed as a measure to reduce the workload of human operators, who would only have to focus on the analysis of ultrasonic oscillograms that are difficult to interpret.     

Decision trees aided scheduling for firm power capacity provision by virtual power plants

The incorporation of Distributed Generation (DG) under the Virtual Power Plant (VPP) concept allows the market integration of several and largely dispersed electric power sources. One matter of concern for the VPP owner and operator is to follow the hourly schedule regardless of the stochastic nature of some of its sources or any unpredicted generation outages. This study presents a Decision Tree (DT) based methodology that prepares for the dispatching of power equivalent to the possible loss of the highest injection of one of the sources of the VPP (according to day-ahead hourly schedule) to the rest of its sources, on an hour-ahead horizon. This allows VPP operators to provide firm capacity and participate effectively in the energy market.     

Tree ensemble construction using a GRASP-based heuristic and annealed randomness

Two new methods for tree ensemble construction are presented: G-Forest and GAR-Forest. In a similar way to Random Forest, the tree construction process entails a degree of randomness.The same strategy used in the GRASP metaheuristic for generating random and adaptive solutions is used at each node of the trees. The source of diversity of the ensemble is the randomness of the solution generation method of GRASP. A further key feature of the tree construction method for GAR-Forest is a decreasing level of randomness during the process of constructing the tree: maximum randomness at the root and minimum randomness at the leaves. The method is therefore named “GAR”, GRASP with annealed randomness.The results conclusively demonstrate that G-Forest and GAR-Forest outperform Bagging, AdaBoost, MultiBoost, Random Forest and Random Subspaces. The results are even more convincing in the presence of noise, demonstrating the robustness of the method.The relationship between base classifier accuracy and their diversity is analysed by application of kappa-error diagrams and a variant of these called kappa-error relative movement diagrams.     

Proteomic Analysis of Embryogenesis and the Acquisition of Seed Dormancy in Norway Maple (Acer platanoides L.)

The proteome of zygotic embryos of Acer platanoides L. was analyzed via high-resolution 2D-SDS-PAGE and MS/MS in order to: (1) identify significant physiological processes associated with embryo development; and (2) identify changes in the proteome of the embryo associated with the acquisition of seed dormancy. Seventeen spots were identified as associated with morphogenesis at 10 to 13 weeks after flowering (WAF). Thirty-three spots were associated with maturation of the embryo at 14 to 22 WAF. The greatest changes in protein abundance occurred at 22 WAF, when seeds become fully mature. Overall, the stage of morphogenesis was characterized by changes in the abundance of proteins (tubulins and actin) associated with the growth and development of the embryo. Enzymes related to energy supply were especially elevated, most likely due to the energy demand associated with rapid growth and cell division. The stage of maturation is crucial to the establishment of seed dormancy and is associated with a higher abundance of proteins involved in genetic information processing, energy and carbon metabolism and cellular and antioxidant processes. Results indicated that a glycine-rich RNA-binding protein and proteasome proteins may be directly involved in dormancy acquisition control, and future studies are warranted to verify this association.     

Machine learning ensembles for wind power prediction

For a sustainable integration of wind power into the electricity grid, a precise prediction method is required. In this work, we investigate the use of machine learning ensembles for wind power prediction. We first analyze homogeneous ensemble regressors that make use of a single base algorithm and compare decision trees to k-nearest neighbors and support vector regression. As next step, we construct heterogeneous ensembles that make use of multiple base algorithms and benefit from a gain of diversity among the weak predictors. In the experimental evaluation, we show that a combination of decision trees and support vector regression outperforms state-of-the-art predictors (improvements of up to 37% compared to support vector regression) as well as homogeneous ensembles while requiring a shorter runtime (speed-ups from 1.60× to 8.78×). Furthermore, we show the heterogeneous ensemble prediction can be improved when using high-dimensional patterns by increasing the number of past steps considered and hereby the spatio-temporal information available by the measurements of the nearby turbines. The experiments are based on a large wind time series data set from simulations and real measurements.     

Comparing attack trees and misuse cases in an industrial setting

The last decade has seen an increasing focus on addressing security already during the earliest stages of system development, such as requirements determination. Attack trees and misuse cases are established techniques for representing security threats along with their potential mitigations. Previous work has compared attack trees and misuse cases in two experiments with students. The present paper instead presents an experiment where industrial practitioners perform the experimental tasks in their workplace. The industrial experiment confirms a central finding from the student experiments: that attack trees tend to help identifying more threats than misuse cases. It also presents a new result: that misuse cases tend to encourage identification of threats associated with earlier development stages than attack trees. The two techniques should therefore be considered complementary and should be used together in practical requirements work.