Hongbo Wang^{1, *}, Qian Xue¹, Tong Cui¹, Yangyang Li², Huacheng Zeng³

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 337-351, 2020, DOI:10.32604/cmc.2020.07290 - 30 March 2020

Abstract As a major function of smart transportation in smart cities, vehicle model recognition plays an important role in intelligent transportation. Due to the difference among different vehicle models recognition datasets, the accuracy of network model training in one scene will be greatly reduced in another one. However, if you don’t have a lot of vehicle model datasets for the current scene, you cannot properly train a model. To address this problem, we study the problem of cold start of vehicle model recognition under cross-scenario. Under the condition of small amount of datasets, combined with the More >

Zheng Xu¹, Qingyuan Zhou², Zhiguo Yan³

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 1-3, 2019, DOI:10.31209/2019.100000082

Abstract This article has no abstract. More >

Zheng Xu¹, Qingyuan Zhou², Zhiguo Yan³

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 693-694, 2019, DOI:10.31209/2019.100000072

Abstract This article has no abstract. More >

Jcy Chen^1,*, Wc Chen¹, Tim Chen¹, Alex Wilson², N. Fadilah Jamaludin³, Nertrand Kapron¹, Tim Chen^4,5, John Burno⁵

Sound & Vibration, Vol.53, No.6, pp. 245-250, 2019, DOI:10.32604/sv.2019.04233

Abstract This paper proposes a novel artificial intelligence sythethized controller in the mechanical system which has high speed computation because of the LMI type criterion. The proposed membership functions are adopted and stabilization criterion of the closed-loop T-S fuzzy systems are obtained through a new parametrized LMI (linear matrix) inequality which is rearranged by machine learning membership functions. More >

Youqin Huang¹, Jiayong Li¹, Jiyang Fu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 845-875, 2019, DOI:10.32604/cmes.2019.07653

Abstract In last few years, big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques. However, until now, there has been no comprehensive review on its applications in civil engineering. To fill this gap, this paper reviews the application and development of artificial intelligence in civil engineering in recent years, including intelligent algorithms, big data and deep learning. Through the work of this paper, the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be More >

Ryuji Shioya and Hongjie Zheng

The International Conference on Computational & Experimental Engineering and Sciences, Vol.22, No.4, pp. 195-195, 2019, DOI:10.32604/icces.2019.05008

Abstract Oil-filled power transformers play an important role in the modern network system. Stability of power supply can be achieved by early detection of power transformer fault and continuous monitoring of equipment status. Transformers in operation are constantly affected by various types of stresses such as electrical, thermal and mechanical stress. Much attention is needed on maintenance of transformers in order to have fault free electric supply and to maximize the lifetime of a transformer. In recent years, Dissolved gas analysis (DGA) has been widely used for diagnostic fault of power transformers. Although DGA is an… More >

Yan Zou^1,2,3,*, Shaoliang Zhang¹, Yanhai Min¹

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 295-310, 2019, DOI:10.32604/cmes.2019.03873

Abstract The high precision population forecasting and spatial distribution modeling are very important for the theory and application of population sociology, city planning and Geo-Informatics. However, the two problems need to be solved for providing the high precision population information. One is how to improve the population forecasting precision of small area (e.g., street scale); another is how to improve the spatial resolution of urban population distribution model. To solve the two problems, some new methods are proposed in this contribution. (1) To improve the precision of small area population forecasting, a new method is developed… More >

Yiğit Kültür, Mehmet Ufuk Çağlayan

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 807-817, 2018, DOI:10.1080/10798587.2017.1342415

Abstract Because credit card fraud costs the banking sector billions of dollars every year, decreasing the losses incurred from credit card fraud is an important driver for the sector and end-users. In this paper, we focus on analyzing cardholder spending behavior and propose a novel cardholder behavior model for detecting credit card fraud. The model is called the Cardholder Behavior Model (CBM). Two focus points are proposed and evaluated for CBMs. The first focus point is building the behavior model using single-card transactions versus multi-card transactions. As the second focus point, we introduce holiday seasons as More >

Shaofei Ren^1,2, Guorong Chen² , Tiange Li² , Qijun Chen², Shaofan Li^{2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 287-307, 2018, DOI:10.31614/cmes.2018.04697

Abstract In this work, we have developed a novel machine (deep) learning computational framework to determine and identify damage loading parameters (conditions) for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure. We have shown that the developed machine learning algorithm can accurately and (practically) uniquely identify both prior static as well as impact loading conditions in an inverse manner, based on the residual plastic strain and plastic deformation as forensic signatures. The paper presents the detailed machine learning algorithm, data acquisition and learning processes, and validation/verification More >

Martínez Corral L¹, E Martínez-Rubin², F F lores-García³, M Vázquez-Rueda³, J Frías-Ramírez², MA Segura-Castruita²

Phyton-International Journal of Experimental Botany, Vol.80, pp. 79-84, 2011, DOI:10.32604/phyton.2011.80.079

Abstract Potato (Solanum tuberosum L.) is a crop whose production yield at national level is very low compared with that in the most productive countries. This is because it is a partially automated crop with deficient and inadequate agronomic practices, low technification levels and great quantity of work wages required per hectare of cultivation. The necessity to generate technical and modern procedures that increase crop production, quality and yield has fostered development of projects leading to obtain seedlings free of pathogens with material of high genetic, physiological and sanitary quality. Utilization of a vision system for the… More >