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Mechanical, tribological and electrical properties of Cu-CNT composites fabricated by flake powder metallurgy method

Akbarpour M. R., Alipour S, Farvizi M., Kim H. S.

Archives of Civil and Mechanical Engineering

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2019

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Vol. 19, no. 3

694--706

EN

Cu-CNT composites were fabricated by a flake powder metallurgy method, and their microhardness, electrical conductivity, frictional and wear properties were investigated. Homogenous distribution of CNTs in fine-grained Cu matrix was obtained using this process. Microhardness increased with the addition of CNT vol% up to 8% to the Cu matrix, while the conductivity decreased to 79.2 IACS %. Results showed that CNTs play a major role in improving wear resistance by forming a CNT-rich film that acts as a solid lubricant layer. In the synthesized composites, Cu- 4 vol% CNT composite exhibited the best wear and friction properties. The dominant wear mechanisms for the Cu-CNT composites were plastic deformation, abrasion, and flake formation-spalling. Also, a newly modified correlation was proposed for the theoretical calculation of the friction coefficient of Cu-CNT composites consisting agglomerated CNTs.

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Noisy image segmentation using a self-organizing map network

Gorjizadeh S, Pasban S, Alipour S

Advances in Science and Technology. Research Journal

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2015

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Vol. 9, nr 26

118–-123

EN

Image segmentation is an essential step in image processing. Many image segmentation methods are available but most of these methods are not suitable for noisy images or they require priori knowledge, such as knowledge on the type of noise. In order to overcome these obstacles, a new image segmentation algorithm is proposed by using a self-organizing map (SOM) with some changes in its structure and training data. In this paper, we choose a pixel with its spatial neighbors and two statistical features, mean and median, computed based on a block of pixels as training data for each pixel. This approach helps SOM network recognize a model of noise, and consequently, segment noisy image as well by using spatial information and two statistical features. Moreover, a two cycle thresholding process is used at the end of learning phase to combine or remove extra segments. This way helps the proposed network to recognize the correct number of clusters/segments automatically. A performance evaluation of the proposed algorithm is carried out on different kinds of image, including medical data imagery and natural scene. The experimental results show that the proposed algoise in comparison with the well-known unsupervised algothms.

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A new method for localization of wireless sensor networks based on path planning of mobile robots

Ashoori E, Babagoli I, Alipour S

Advances in Science and Technology. Research Journal

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2015

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Vol. 9, nr 28

10--17

EN

With an explosive growth of wireless sensor networks (WSN), many of their features and applications have become important. Localization of sensor nodes is one of the most important problems in WSN whose accuracy has a very large impact on its performance. Global positioning system (GPS) is a well-known and powerful way which differentiates methods of its use on each node individually. But, because of high energy consuming and processing GPS, it is inappropriate for WSNs. Different algorithms are suggested to overcome the consumed cost of GPS by putting GPS on only some nodes instead of all nodes in the network for localization. So, for nodes localization, just a number of nodes exploit GPS and, they can help other nodes of network in localization via distribution of their coordinates. The use of a mobile robot to send signals to coordinate the target node localization is a good idea. The mobile robot should move in the right path and can localize node more accurately at lower cost. This paper proposes a new method to localize all nodes through some localized nodes based on graph theory in a tree and network topology. The proposed method provides better performance at the cost of accuracy and the number of nodes that can be made up of local consumption.