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Institution

Taiyuan University of Science and Technology

EducationTaiyuan, China
About: Taiyuan University of Science and Technology is a education organization based out in Taiyuan, China. It is known for research contribution in the topics: Microstructure & Particle swarm optimization. The organization has 4849 authors who have published 4173 publications receiving 33598 citations.


Papers
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Journal ArticleDOI
TL;DR: A comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out, and the results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms.
Abstract: In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub ( https://github.com/ggw0122/Monarch-Butterfly-Optimization , C++/MATLAB) and MATLAB Central ( http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization , MATLAB).

778 citations

Journal ArticleDOI
TL;DR: In this article, a series of novel porous carbon materials with different dimensions have been prepared by various methods using biomass as the raw material, which is an important field in the fabrication of supercapacitor electrode materials.
Abstract: The exploration of renewable, cost-effective, and environmentally friendly electrode materials with high adsorption, fast ion/electron transport, and tunable surface chemistry is urgently needed for the development of next-generation biocompatible energy-storage devices. In recent years, biomass-derived carbon electrode materials for energy storage have attracted significant attention because of their widespread availability, renewable nature, and low cost. More importantly, their inherent uniform and precise biological structures can be utilized as templates for fabricating electrode materials with controlled and well-defined geometries. Meanwhile, the basic elements of biomass are carbon, sulfur, nitrogen, and phosphorus. The special naturally ordered hierarchical structures as well as abundant surface properties of biomass-derived carbon materials are compatible with electrochemical reaction processes such as ion transfer and diffusion. To date, a series of novel porous carbon materials with different dimensions have been prepared by various methods using biomass as the raw material, which is an important field in the fabrication of supercapacitor electrode materials. Herein, we summarized recently reported biomass-derived carbon materials with one-dimensional, two-dimensional, and three-dimensional structures and their applications as carbon-based electrode materials for supercapacitors. Finally, the current challenges and future perspectives of the carbon-based electrode materials with respect to the supercapacitor's performance were closely highlighted.

597 citations

Journal ArticleDOI
TL;DR: This work provides an update on the latest development of stabilized nZVI for various environmental cleanup uses, and overviews the evolution and environmental applications of stabilization, as well as revealing some critical knowledge gaps and research needs.

477 citations

Journal ArticleDOI
TL;DR: A novel method that used deep learning to improve the detection of malware variants using a convolutional neural network that could extract the features of the malware images automatically was proposed.
Abstract: With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.

444 citations

Journal ArticleDOI
TL;DR: A blockchain based multi-WSN authentication scheme for IoT is proposed and the analysis of security and performance shows that the scheme has comprehensive security and better performance.
Abstract: Internet of Things (IoT) equipment is usually in a harsh environment, and its security has always been a widely concerned issue. Node identity authentication is an important means to ensure its security. Traditional IoT identity authentication protocols usually rely on trusted third parties. However, many IoT environments do not allow such conditions, and are prone to single point failure. Blockchain technology with decentralization features provides a new solution for distributed IoT system. In this paper, a blockchain based multi-WSN authentication scheme for IoT is proposed. The nodes of IoT are divided into base stations, cluster head nodes and ordinary nodes according to their capability differences, which are formed to a hierarchical network. A blockchain network is constructed among different types of nodes to form a hybrid blockchain model, including local chain and public chain. In this hybrid model, nodes identity mutual authentication in various communication scenarios is realized, ordinary node identity authentication operation is accomplished by local blockchain, and cluster head node identity authentication are realized in public blockchain. The analysis of security and performance shows that the scheme has comprehensive security and better performance.

328 citations


Authors

Showing all 4884 results

NameH-indexPapersCitations
Bin Liu138218187085
Jian Liu117209073156
Hao Wu10566942607
Jing Zhang95127142163
Yaochu Jin7851424672
Hong Hao6879519057
Guang-Ling Song6827921118
Kai Sun6647616720
Wen Yang6450114385
Hongbo Zeng6140613269
Ying Tan5863013915
Jinjun Chen573079587
Dongye Zhao5320511278
Yue-Sheng Wang5242410626
Gai-Ge Wang511357806
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202322
202238
2021478
2020407
2019350
2018264