In this paper, a fractional order Kalman filter (FOKF) is presented, this is based on a system expressed by fractional differential equations according to the Riemann-Liouville definition. In order to get the best fit...
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In this paper, a fractional order Kalman filter (FOKF) is presented, this is based on a system expressed by fractional differential equations according to the Riemann-Liouville definition. In order to get the best fitting of the FOKF, the cuckoo search optimization algorithm (CS) was used. The purpose of using the CS algorithm is to optimize the order of the observer, the fractional Riccati equation and the FOKF tuning parameters. The Grunwald-Letnikov approximation was used to compute the numerical solution of the FOKF. To show the effectiveness of the proposed FOKF, four examples are presented, the brain activity, the cutaneous potential recordings of a pregnant woman, the earthquake acceleration, and the Chua's circuit response. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.
In the context of work zone safety, worker presence and its impact on crash severity has been less explored. Moreover, there is a lack of research on contributing factors by time-of-day. To accomplish this, first a mi...
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In the context of work zone safety, worker presence and its impact on crash severity has been less explored. Moreover, there is a lack of research on contributing factors by time-of-day. To accomplish this, first a mixed logit model was used to determine statistically significant crash severity contributing factors and their effects. Significant factors in both models included work-zone-specific characteristics and crash-specific characteristics, where environmental characteristics were only significant in the daytime model. In addition, results from parameter transferability test provided evidence that daytime and nighttime crashes need to be modeled separately. Further, to explore the nonlinear relationship between crash severity levels and time-of-day, as well as compare the effects of variables to that of the logit model and assess prediction performance, a Support Vector Machines (SVM) model trained by cuckoosearch (CS) algorithm was utilized. Opening the SVM black-box, a variable impact analysis was also performed. In addition to the characteristics identified in the logit models, the SVM models also included the impacts of vehicle-level characteristics. The variable impact analysis illustrated that the termination area of the work zone is most critical for both daytime and nighttime crashes, as this location has the highest increase in severe injury likelihood. In summary, results of this study demonstrate that work zone crashes need to be modeled separately by time-of-day with a high level of confidence. Furthermore, results show that the CS-SVM models provide better prediction performance compared to the SVM and logit models.
The lossy nature of the JPEG compression leaves traces which are utilized by the forensic agents to identify the local tampering in the image. In this paper, a tricky anti-forensic method has been proposed to remove t...
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The lossy nature of the JPEG compression leaves traces which are utilized by the forensic agents to identify the local tampering in the image. In this paper, a tricky anti-forensic method has been proposed to remove the traces left by the JPEG compression in both the spatial domain and discrete cosine transform domain. A novel Least cuckoosearchalgorithm is devised in the proposed anti-forensic compression scheme. Moreover, a new fitness function called histogram deviation is formulated in the optimizationalgorithm. The experimentation of the proposed anti-forensic compression scheme is performed over uncompressed images from UCID database. The performance of the proposed method is evaluated, and it is compared with the existing methods using PSNR, MSE and classification accuracy as measures. The experimentation ensued with promising results, i.e. accuracy of 0.97, PSNR of 44.34dB, and MSE of 0.1789 which prove the efficacy of the proposed method.
A two link planar rigid robotic manipulator is a highly nonlinear, coupled and multi-input multi-output system. For its effective control, an intelligent adaptive fractional order fuzzy sliding mode proportional integ...
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A two link planar rigid robotic manipulator is a highly nonlinear, coupled and multi-input multi-output system. For its effective control, an intelligent adaptive fractional order fuzzy sliding mode proportional integral and derivative controller (FOFSMCPID) has been presented in this work. Sliding mode controller (SMC) is designed by using exponential law and closed loop stability analysis is demonstrated by using Lyapunov theorem. The chattering in the SMC controller has been effectively reduced with the help of boundary layer along with the fuzzy logic. For tuning of the controller, a weighted sum of integral of absolute error and chatter has been considered as the objective function to be minimized using cuckoo search optimization algorithm. To demonstrate the efficacy of FOFSMCPID controller, the results have been compared with integer order fuzzy sliding mode proportional, integral and derivative controller (IOFSMCPID), integer order fuzzy sliding mode proportional and derivative controller (IOFSMCPD) and fractional order fuzzy sliding mode proportional and derivative (FOFSMCPD) controller for trajectory tracking, disturbance rejection, noise suppression and model uncertainties. The detailed presented investigations have demonstrated that FOFSMCPID controller exhibits much superior performance over IOFSMCPID, IOFSMCPD and FOFSMCPD controllers.
ABSTRACTABSTRACTThe lossy nature of the JPEG compression leaves traces which are utilized by the forensic agents to identify the local tampering in the image. In this paper, a tricky anti-forensic method has been prop...
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ABSTRACTABSTRACTThe lossy nature of the JPEG compression leaves traces which are utilized by the forensic agents to identify the local tampering in the image. In this paper, a tricky anti-forensic method has been proposed to remove the traces left by the JPEG compression in both the spatial domain and discrete cosine transform domain. A novel Least cuckoosearchalgorithm is devised in the proposed anti-forensic compression scheme. Moreover, a new fitness function called histogram deviation is formulated in the optimizationalgorithm. The experimentation of the proposed anti-forensic compression scheme is performed over uncompressed images from UCID database. The performance of the proposed method is evaluated, and it is compared with the existing methods using PSNR, MSE and classification accuracy as measures. The experimentation ensued with promising results, i.e. accuracy of 0.97, PSNR of 44.34 dB, and MSE of 0.1789 which prove the efficacy of the proposed method.
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