A Novel Artificial Fish Swarm Algorithm for Recalibration of Fiber Optic Gyroscope Error Parameters

Abstract

The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes’ pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

Design and Implementation an Automated System for Analyzing Brushstrokes to Distinguish between Van Gogh and his Contemporaries by using Swarm Intelligent Method

“…This experiment is run on a database containing 988 artistic images (with 49 visual classification problems divided into a multiclass problem with 27 classes and 48 binary problems), where he show the inference and training running times, and quantitative comparisons with respect to several retrieval and annotation performance measures. Jia Li, et.al in 2012 [5], they compared van Gogh painting with his contemporaries by statistically analyzing a massive set of automatically extracted brushstrokes. A novel extraction method is developed by exploiting an integration of edge detection and clustering-based segmentation.…”

“…Hough transform is most commonly used for the detection of regular curves such as lines, circles, ellipses, etc. The main After Edge linking step, A brushstroke fully enclosed by an edge is spatially isolated from other non-edge pixels and forms a connected component, as in [5 ][21], extract all the connected components from the image by setting the edge pixels as foreground, and the non-edge pixels as background. After extracted a connected component from the previous step, The Resize operation on the image is executed, a brushstroke must be in size (256 x 265 pixel).…”

“…As in [5], Figure 5 illustrates the process using real candidate brushstroke segments and their skeletons (a) (b) Fig. 5: The process using real candidate brushstroke segments and their skeletons (a) brushstrokes extracted image (b) brushstrokes extracted image after processing steps…”

“…They will not know if an artist was ever truly inspired by a work unless he or she has said, so for the sake of finding connections and progressing through movements of art, a general consensus is agreed upon if the argument is convincing enough. [4] [5]. For example, (see Figure 1),illustrates a commonly cited comparison for studying influence, in the work of Van Gogh and Artist Yahya Zaki, and other Van Gogh Contemporaries where similarity is clear in composition, pose, and subject matter.…”

The evolution of artificial ecosystem based on the ecological chain algorithm

“…None of them explores the life behaviors and characteristics of artificial life population made up of multiple artificial life individuals [7]. Of course, some models have been developed to explore the life behaviors and characteristics of artificial life population, namely the "L System" of Linder Mayer [8], the "BOLD" of C. Reynolds [9], and the "Xiaoyuan's Fish" of Tu Xiaoyuan [10]. However, these models take the perspective of population, and fail to probe into the interaction and mutual impact of the population with other populations and the environment.…”