Selective ensemble based on extreme learning machine and improved discrete artificial fish swarm algorithm for haze forecast

Zhu, Xuhui; Ni, Zhiwei; Cheng, Ming; Jin, Feifei; Li, Jingming; Weckman, Gary R.

doi:10.1007/s10489-017-1027-8

Cited by 31 publications

(20 citation statements)

References 41 publications

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“…However, there is currently no effective method for accurately estimating the number of nodes of the hidden [30]. ELM is bedeviled by the curse of dimensionality, which runs in high-dimensional feature space [31]. To improve robustness and the capability of nonlinear approximation, Huang et al [32] added the kernel function to an extreme learning machine.…”

Section: Kernel Extreme Learning Machinementioning

confidence: 99%

Image Quality Assessment Based on Quaternion Singular Value Decomposition

et al. 2020

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We propose an image quality assessment metric based on quaternion singular value decomposition that represents a color image as a quaternion matrix, separates image noise information using singular value decomposition and extracts features from both the whole image and its noise information. In the proposed method, the color image and its local variance are represented by using quaternion and then performing singular value decomposition. Later, 75% of singular values are taken as image noise information. We extract a luminance comparison, contrast comparison, structure comparison, phase congruency and gradient magnitude from whole color images and extract the peak signal-to-noise ratio from image noise information as features. Finally, these features are used as the input to a kernel extreme learning machine to predict the quality of the tested images. Extensive experiments performed on four benchmark image quality assessment databases demonstrate that the proposed metric achieves high consistency with the subjective evaluations and outperforms state-of-the-art image quality assessment metrics. INDEX TERMS Quaternion singular value decomposition, kernel extreme learning machine, image quality assessment.

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Section: Kernel Extreme Learning Machinementioning

confidence: 99%

Image Quality Assessment Based on Quaternion Singular Value Decomposition

et al. 2020

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“…ELMs were also used for gas flow measurement [42], which can analytically determine the output weights of networks and provide high metering accuracy at fast learning speed as well as require least human intervention. In addition, the ELM-based prediction models achieved good accuracy in other applications, such as price forecast [43,44], haze forecast [45] and photovoltaic power generation forecasting [46]. Motivated by its good prediction ability and robustness, this paper uses ELM to predict the amount of tasks on the agent side, so as to improve the accuracy of dynamic task partitioning.…”

Section: Related Workmentioning

confidence: 99%

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Zou

Min

2019

Applied Sciences

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Featured Application: A distributed face recognition system can quickly handle a large number of face recognition tasks, but the problem of load imbalance may lead to time delay and sharp increase of CPU utilization. To deal with the problem, this paper proposes a dynamic load balancing method based on prediction, which can effectively alleviate the time delay and sharp increase of CPU utilization. Distributed face recognition can be widely used in many important research fields and industries such as public security, customs, finance, security. Also, the method proposed in this paper can help provide new ideas for solving task assignment and load balancing problems in other distributed systems such as speech recognition, fingerprint recognition, and vehicle identification.Abstract: With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization.

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“…In a social form, it is searching for food, immigration, dealing with dangers and interactions between the fish in a swarm that result in intelligent group behavior [43,44]. The AFSA has many advantages such as flexibility, great convergence speed, great accuracy, fault tolerance and so on [45].…”

Section: Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Application of an Artificial Fish Swarm Algorithm in an Optimum Tuned Mass Damper Design for a Pedestrian Bridge

et al. 2018

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Tuned mass damper (TMD) has a wide application in the human-induced vibration control of pedestrian bridges and its parameters have great influence on the control effects, hence it should be well designed. A new optimization method for a TMD system is proposed in this paper, based on the artificial fish swarm algorithm (AFSA), and the primary structural damping is taken into consideration. The optimization goal is to minimize the maximum dynamic amplification factor of the primary structure under external harmonic excitations. As a result, the optimized TMD has a smaller maximum dynamic amplification factor and better robustness. The optimum TMD parameters for a damped primary structure with different damping ratios and different TMD mass ratios are summarized in a table for simple, practical design, and the fitting equation is also provided. The TMD configuration optimized by the proposed method was shown to be superior to that optimized by other classical optimization methods. Finally, the application of an optimized TMD based on AFSA for a pedestrian bridge is proposed as a case study. The results show that the TMD designed based on AFSA has a smaller maximum dynamic amplification factor than the TMD designed based on the classic Den Hartog method and the TMD designed based on the Ioi Toshihiro method, and the optimized TMD has a good effect in controlling human-induced vibrations at different frequencies.

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Selective ensemble based on extreme learning machine and improved discrete artificial fish swarm algorithm for haze forecast

Cited by 31 publications

References 41 publications

Image Quality Assessment Based on Quaternion Singular Value Decomposition

Image Quality Assessment Based on Quaternion Singular Value Decomposition

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Application of an Artificial Fish Swarm Algorithm in an Optimum Tuned Mass Damper Design for a Pedestrian Bridge

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