A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Improvement of Giant Magnetoimpedance and Sensitivity in Co68.5-xFe4WxSi16.5B11 (x = 0.8, 2) Ribbons Sandwiched in PVA/Fe3O4 Nanocomposite Films

Journal of Superconductivity and Novel Magnetism - In this work, giant magnetoimpedance (GMI); magnetic hysteresis loop; and sensitivity of Co68.5Fe4Si16.5B11 (S), Co67.7Fe4W0.8Si16.5B11 (W1), and...     

Observer‐based adaptive optimal output containment control problem of linear heterogeneous Multiagent systems with relative output measurements

This paper develops a relative output‐feedback–based solution to the containment control of linear heterogeneous multiagent systems. A distributed optimal control protocol is presented for the followers to not only assure that their outputs fall into the convex hull of the leaders' output but also optimizes their transient performance. The proposed optimal solution is composed of a feedback part, depending of the followers' state, and a feed‐forward part, depending on the convex hull of the leaders' state. To comply with most real‐world applications, the feedback and feed‐forward states are assumed to be unavailable and are estimated using two distributed observers. That is, a distributed observer is designed to measure each agent's states using only its relative output measurements and the information that it receives by its neighbors. Another adaptive distributed observer is designed, which uses exchange of information between followers over a communication network to estimate the convex hull of the leaders' state. The proposed observer relaxes the restrictive requirement of having access to the complete knowledge of the leaders' dynamics by all the followers. An off‐policy reinforcement learning algorithm on an actor‐critic structure is next developed to solve the optimal containment control problem online, using relative output measurements and without requiring the leaders' dynamics. Finally, the theoretical results are verified by numerical simulations.     

Numerical simulation of a falling droplet surrounding by air under electric field using VOF method: A CFD study

This is a numerical study of a falling droplet surrounding by air under the electric field modeled with finite volume method by means of CFD. The VOF method has been employed to model the two-phase flow of the present study. Various capillary numbers are investigated to analyze the effects of electric field intensity on the falling droplet deformation. Also, the effects of electric potential on the heat transfer coefficient have been examined. The obtained results showed that by applying the electric field at a capillary number of 0.2 the droplet tends to retain its primitive shape as time goes by, with a subtle deformation to an oblate form. Intensifying the electric field to a capillary number of 0.8 droplet deformation is almost insignificant with time progressing; however, further enhancement in capillary number to 2 causes the droplet to deform as a prolate shape and higher values of this number intensify the prolate form deformation of the droplet and result in pinch-off phenomenon. Ultimately, it is showed that as the electric potential augments the heat transfer coefficient increases in which for electric potential values higher than 2400 V the heat transfer coefficient enhances significantly.     

Experimental and numerical investigation of bridge pier scour estimation using ANFIS and teaching–learning-based optimization methods

Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments. The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study, the adaptive network-based fuzzy inference system (ANFIS) and teaching–learning-based optimization (TLBO) method were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18, Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.

     

An evolutionary game theory–based security model in vehicular ad hoc networks

The word population is growing on a daily basis; consequently, the growth of commute and transport, developing efficient and intelligent transportation systems (ITS), has become one of the most popular requirements and the most significant attempts in modern urban areas containing large population. A key component of intelligent transportation systems is a vehicular ad hoc network. Devising internet‐based practical programs such as awareness of climatic conditions, geographical location, practical programs like on‐line payment services in the vehicular ad hoc network has led to safer driving, prevention of deadly accidents, transportation improvement, more welfare and convenience for passengers, and even offering more commercial opportunities. The special features of vehicular ad hoc network, such as intense activity, constantly‐changing topology, the vehicles' high speed, etc, will lead to challenges in gaining security. Therefore, providing vehicular ad hoc networks with security is of extreme importance in terms of users' anonymity, identification, and data privacy. In this paper, a security model is presented using a method based on evolutionary games. This method, in every vehicle, is applied as a node in the network while interacting with other vehicles; it aims at distinguishing some common attacks and defending against attackers. In this method, defending (honest) vehicles and attacking vehicles take part in an asymmetrical game; each vehicle aims at gaining the most utility and achieving its goals. The proposed method is simulated using various scenarios. The simulation results reveal that the proposed method is efficient and it reaches equilibrium and convergence at the end of the game in each scenario.     

Preparation of Au nanoparticles by Q switched laser ablation and their application in 4-nitrophenol reduction

Clean Technologies and Environmental Policy - This work reports the fabrication of Au nanoparticles (NPs) by laser ablation of a gold metal plate immersed in water in the absence of stabilizing...     

Non-isothermal aging of a high-Zn-containing Al–Zn–Mg–Cu alloy: microstructure and mechanical properties

The non-isothermal aging behaviour of a newly developed Al–Zn–Mg–Cu alloy containing 17?wt-% Zn was investigated. Hardness and shear punch tests demonstrated that during non-isothermal aging, the mechanical properties of the alloy first increased and then decreased. The best properties were obtained in a sample which was non-isothermally aged upto 250°C with heating rate of 20°C?min^?1, due to the presence of η′/η (MgZn₂) phases. This was confirmed by differential scanning calorimetery. After homogenisation, residual eutectic phases remained at triple junctions or in a spherical form. During aging, these phases transformed into rodlike S (Al₂CuMg)-phase at 400°C, with sizes ranging from 50 to 250?nm. The precipitation sequence in this high-Zn alloy was similar to that for conventional Al–Zn–Mg–Cu alloys.