The bipartite edge frustration of graphs under subdivided edges and their related sums

The bipartite edge frustration of a graph G, denoted by φ(G), is the smallest number of edges that have to be deleted from G to obtain a bipartite spanning subgraph of G. This topological index is related to the well-known Max-cut problem, and has important applications in computing stability of fullerenes. In this paper, the bipartite edge frustration of an infinite family of fullerenes is computed. Moreover, this quantity for four classes of graphs arising from a given graph under different types of edge subdivisions is investigated.     

Model-based PI–fuzzy control of four-wheeled omni-directional mobile robots

The purpose of this study is to suggest and examine a PI–fuzzy path planner and associated low-level control system for a linear discrete dynamic model of omni-directional mobile robots to obtain optimal inputs for drivers. Velocity and acceleration filtering is also implemented in the path planner to satisfy planning prerequisites and prevent slippage. Regulated drivers’ rotational velocities and torques greatly affect the ability of these robots to perform trajectory planner tasks. These regulated values are examined in this research by setting up an optimal controller. Introducing optimal controllers such as linear quadratic tracking for multi-input–multi-output control systems in acceleration and deceleration is one of the essential subjects for motion control of omni-directional mobile robots. The main topics presented and discussed in this article are improvements in the presented discrete-time linear quadratic tracking approach such as the low-level controller and combined PI–fuzzy path planner with appropriate speed monitoring algorithm such as the high-level one in conditions both with and without external disturbance. The low-level tracking controller presented in this article provides an optimal solution to minimize the differences between the reference trajectory and the system output. The efficiency of this approach is also compared with that of previous PID controllers which employ kinematic modeling. Utilizing the new approach in trajectory-planning controller design results in more precise and appropriate outputs for the motion of four-wheeled omni-directional mobile robots, and the modeling and experimental results confirm this issue.     

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S_w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.     

Secure network coding for wireless mesh networks: Threats, challenges, and directions

In recent years, network coding has emerged as a new communication paradigm that can significantly improve the efficiency of network protocols by requiring intermediate nodes to mix packets before forwarding them. Recently, several real-world systems have been proposed to leverage network coding in wireless networks. Although the theoretical foundations of network coding are well understood, a real-world system needs to solve a plethora of practical aspects before network coding can meet its promised potential. These practical design choices expose network coding systems to a wide range of attacks.We identify two general frameworks (inter-flow and intra-flow) that encompass several network coding-based systems proposed in wireless networks. Our systematic analysis of the components of these frameworks reveals vulnerabilities to a wide range of attacks, which may severely degrade system performance. Then, we identify security goals and design challenges in achieving security for network coding systems. Adequate understanding of both the threats and challenges is essential to effectively design secure practical network coding systems. Our paper should be viewed as a cautionary note pointing out the frailty of current network coding-based wireless systems and a general guideline in the effort of achieving security for network coding systems.     

Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machine, cutting tool and tool access direction (TAD) for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine, cutting tool, and TAD for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. A number of case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.     

An efficient classifier to diagnose of schizophrenia based on the EEG signals

In this paper, electroencephalogram (EEG) signals of 13 schizophrenic patients and 18 age-matched control participants are analyzed with the objective of classifying the two groups. For each case, multi-channels (22 electrodes) scalp EEG is recorded. Several features including autoregressive (AR) model parameters, band power and fractal dimension are extracted from the recorded signals. Leave-one (participant)-out cross validation is used to have an accurate estimation for the separability of the two groups. Boosted version of Direct Linear Discriminant Analysis (BDLDA) is selected as an efficient classifier which applied on the extracted features. To have comparison, classifiers such as standard LDA, Adaboost, support vector machine (SVM), and fuzzy SVM (FSVM) are applied on the features. Results show that the BDLDA is more discriminative than others such that their classification rates are reported 87.51%, 85.36% and 85.41% for the BDLDA, LDA, Adaboost, respectively. Results of SVM and FSVM classifiers were lower than 50% accuracy because they are more sensitive to outlier instances. In order to determine robustness of the suggested classifier, noises with different amplitudes are added to the test feature vectors and robustness of the BDLDA was higher than the other compared classifiers.     

Analysis and forecasting of IPO underpricing

Probability of withdrawal is a feature of initial public offering (IPOs), which can be an important parameter in decisions of investors and issuers. Considering the probability of offering withdrawal facilitates more precise estimation of underpricing. In this paper, the effective factors on probability of IPO withdrawal and underpricing in Tehran Stock Exchange have been characterized using regression, and then neural network is applied to estimate the probability of IPO withdrawal and underpricing. To evaluate the performance of our applied method, fuzzy regression is employed and compared with neural network. According to the obtained empirical results, neural network demonstrates better accuracy than fuzzy regression. The results indicate that there is a meaningful relationship between underpricing and probability of withdrawal, and the probability of IPO withdrawal plays an important role in precise evaluation of underpricing.     

Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Learning from data streams is a challenging task which demands a learning algorithm with several high quality features. In addition to space complexity and speed requirements needed for processing the huge volume of data which arrives at high speed, the learning algorithm must have a good balance between stability and plasticity. This paper presents a new approach to induce incremental decision trees on streaming data. In this approach, the internal nodes contain trainable split tests. In contrast with traditional decision trees in which a single attribute is selected as the split test, each internal node of the proposed approach contains a trainable function based on multiple attributes, which not only provides the flexibility needed in the stream context, but also improves stability. Based on this approach, we propose evolving fuzzy min–max decision tree (EFMMDT) learning algorithm in which each internal node of the decision tree contains an evolving fuzzy min–max neural network. EFMMDT splits the instance space non-linearly based on multiple attributes which results in much smaller and shallower decision trees. The extensive experiments reveal that the proposed algorithm achieves much better precision in comparison with the state-of-the-art decision tree learning algorithms on the benchmark data streams, especially in the presence of concept drift.