306 research outputs found
LS_SVM Parameters Selection Based on Hybrid Complex Particle Swarm Optimization
AbstractIt is important to select parameters in the research area of support vector machine. For this reason, parameters selection for least squares support vector machine (LS_SVM) by hybrid complex particle swarm optimization is proposed in this paper. The proposed method reduces the disadvantage of traditional PSO in local optimum. Simulation of function estimation problem demonstrates t that LS_SVM based hybrid complex particle swarm optimization has better global optimization ability than LS_SVM based traditional PSO
Fault diagnosis method for rolling bearings based on the interval support vector domain description
Aiming at the fault classification problem of the rolling bearing under the uncertain structure parameters work condition, this paper proposes a fault diagnosis method based on the interval support vector domain description (ISVDD). Firstly, intrinsic time scale decomposition is performed for vibration signals of the rolling bearing to get the time-frequency spectrum samples. These samples are divided into a training set and a test set. Then, the training set is used to train the ISVDD. Meanwhile, the dynamic decreasing inertia weight particle swarm optimization is applied to improve the training accuracy of ISVDD model. Finally, the performance of the four interval classifiers is calculated in rolling bearing fault test set. The experimental results show the advantages of the ISVDD model: (1)Â ISVDD can extend the support vector domain description to solve the uncertain interval rolling bearing fault classification problem effectively; (2)Â The proposed ISVDD has the highest classification accuracy in four interval classification methods for the different rolling bearing fault types
Chiral topological whispering gallery modes formed by gyromagnetic photonic crystals
We explore a hexagonal cavity that supports chiral topological whispering
gallery (CTWG) modes, formed by a gyromagnetic photonic crystal. This mode is a
special type of topologically protected optical mode that can propagate in
photonic crystals with chiral direction. Finite element method simulations show
that discrete edge states exist in the topological band gap due to the coupling
of chiral edge states and WG modes. Since the cavity only supports edge state
modes with group velocity in only one direction, it can purely generate
traveling modes and be immune to interference modes. In addition, we introduced
defects and disorder to test the robustness of the cavity, demonstrating that
the CTWG modes can be effectively maintained under all types of perturbations.
Our topological cavity platform offers useful prototype of robust topological
photonic devices. The existence of this mode can have important implications
for the design and application of optical devices.Comment: 17 pages, 8 figure
Gear compound fault detection method based on improved multiscale permutation entropy and local mean decomposition
The traditional multiscale entropy algorithm shows inconsistency because some points are ignored when the signal is coarsened. To solve this problem, this paper proposes an improved multiscale permutation entropy (IMSPE). Firstly, the fault signal is decomposed into several product functions (PF) by local mean decomposition (LMD). Secondly, IMSPE is proposed to extract fault features of product functions. IMSPE integrates the information of multiple coarse sequences and solves problems of entropy inconsistency. Finally, the proposed method based on LMD and IMSPE is applied into gear fault diagnosis system. The experiment shows the proposed method can distinguish different gear fault types with a higher accuracy than traditional methods
Novel gear fault diagnosis approach using native Bayes uncertain classification based on PSO algorithm
Traditionally, gear faults can be classified with the ignorance of the sample uncertainty. In this paper, a novel approach is proposed for the problem diagnosis of uncertain gear interval faults. First, a statistical property interval feature vector composed of mean, standard deviation, skewness, kurtosis, etc. is proposed. Then, the native Bayes uncertain classification (NBU) is used for the diagnostics of these uncertain gear interval faults. Conventionally, the NBU utilizes all the attributes to distinguish fault types. However, each fault type has its own distinct classification accuracy for different feature vector attributes. Thus, the particle swarm optimization (PSO) is used to select the optimal feature vector attributes for each fault type in the NBU (NBU_PSO_EACH). The experimental results show: (1) the accuracy of the proposed method is better than that of NBU1, NBU2 or FBC; (2) in terms of accuracy, the proposed method is also more advanced than the method which selects the same optimal attributes for all fault types based on the PSO (NBU_PSO); (3) the proposed method can reduce the physical size of feature vectors
osmAG: Hierarchical Semantic Topometric Area Graph Maps in the OSM Format for Mobile Robotics
Maps are essential to mobile robotics tasks like localization and planning.
We propose the open street map (osm) XML based Area Graph file format to store
hierarchical, topometric semantic multi-floor maps of indoor and outdoor
environments, since currently no such format is popular within the robotics
community. Building on-top of osm we leverage the available open source editing
tools and libraries of osm, while adding the needed mobile robotics aspect with
building-level obstacle representation yet very compact, topometric data that
facilitates planning algorithms. Through the use of common osm keys as well as
custom ones we leverage the power of semantic annotation to enable various
applications. For example, we support planning based on robot capabilities, to
take the locomotion mode and attributes in conjunction with the environment
information into account. The provided C++ library is integrated into ROS. We
evaluate the performance of osmAG using real data in a global path planning
application on a very big osmAG map, demonstrating its convenience and
effectiveness for mobile robots.Comment: 7 page
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