The Effect of Different Ratios of Malonic Acid to Plyvinylalcohol on Electrochemical and Mechanical Properties of Polyacrylonitrile Electrospun Separators in Lithium-Ion Batteries

The present study aimed to investigate the mechanical, thermal, and electrochemical properties of Polyacrylonitrile (PAN) electrospun separators in the presence of Polyvinylalcohol (PVA) hydrophilic materials and Malonic Acid (MA) crosslinker inside the lithium-ion batteries. The results showed that the M3 modified separator with the MA to PVA+MA (wt./wt.) optimum ratio of 37.5 % had the best performance in all tests. This separator had a value of 3.16 mS/cm in the ion conductivity test. Additionally, it had an electrolyte uptake of 1172 % (2.39 times more than the neat PAN separator) and thermal shrinkage of 7.4 % at 180 °C, where this value was 14.5 % for neat PAN separator at the same experimental condition. Furthermore, the acceptable performance in the battery performance tests was compared with other separators

APPLYING CHAOTIC IMPERIALIST COMPETITIVE ALGORITHM FOR MULTI-LEVEL IMAGE THRESHOLDING BASED ON KAPUR’S ENTROPY

Segmentation is one of the most important operations in image processing and computer vision. Normally, all image processing and computer vision applications are related to segmentation as a pre-processing phase. Image thresholding is one of the most useful methods for image segmentation. Various methods have been represented for image thresholding. One method is Kapur thresholding, which is based on maximizing entropy criterion. In this study, a new meta-heuristic algorithm based on imperialist competition algorithm was proposed for multi-level thresholding based on Kapur's entropy. Also, imperialist competitive algorithm is combined with chaotic functions to enhance search potency in problem space. The results of the proposed method have been compared with particle optimization algorithm and genetic algorithm. The findings revealed that the proposed method was superior to other methods

An ensemble deep learning model to enhance feature representation for entity detection

One of the main processes in most natural language processing (NLP), is named entity recognition (NER). In this regard, some machine learning techniques have been presented that traditionally use manual features. Also, in recent years, deep neural network-based models have been proposed that achieve higher accuracy without relying on huge computations for feature engineering. Thus, in this article, we employ a combination of two deep learning models to capture the properties of the input sentence, including: long short term memory (LSTM) and convolutional neural network (CNN). In this architecture, extracting local features along with global features, more information is acquired for more accurate classification. We evaluate the performance of this architecture on two datasets CoNLL2003 and ACE05; and demonstrate that by adding a word level CNN, useful local properties are extracted that enhance the accuracy of the performance. Finally, we compare the performance of our system with competitors and our superiority is reported

A Trust and Energy-Aware Based Routing Approach in Wireless Sensor Networks Using ODMA Algorithm

Rapid developments in radio technology have enabled the emergence of small sensor nodes capable of communicating in wireless sensor networks. Nodes in wireless sensor networks work together to transmit information using multipath routing. This partnership has made these types of networks vulnerable to many attacks. In order to determine the reliability of nodes in separating malicious nodes from other nodes, an intelligent trust management scheme must be used. In recent years, trust-based and energy-aware routing protocols have become important tools to increase wireless sensor networks security and performance. In this paper, a trust-based and energy-aware routing algorithm based on a new hybrid fitness function is proposed. This algorithm has two main aspects: one is the selection of secure nodes based on the tolerant constant and the other is to select the most suitable nodes from among the secure nodes to perform routing. The proposed algorithm uses a multipath routing technique with an intra-cluster and inter-cluster multi-hop communication mechanism. In addition, the optimal and secure route is selected based on a combined fitness function with parameters of Energy, Reliability, Quality of Service, Connectivity, Distance, Hop-Count and Network Traffic. The simulation is based on evaluation criteria such as throughput and detection rate in the presence of Denial-of-Service attack. The experimental results show that the evaluation criteria in the proposed algorithm have improved compared to other secure routing algorithms