Automatic Channel Pruning with Hyper-parameter Search and Dynamic Masking

Abstract In this paper, new distributed adaptive methods are proposed for solving both leaderless and leader–follower consensus problems in networks of uncertain robot manipulators, by estimating only the gravitational torque forces. Comparing with the existing adaptive methods, which require the estimation of the whole dynamics, presented methods reduce the excitation levels required for efficient parameter search, the convergence time, and the complexity of the regressor. Additionally, proposed schemes eliminate the need for velocity information exchange between the agents. Global asymptotic synchronization is shown by introducing new Lyapunov functions. Simulation results are provided for a network of 10 4-DOF robot manipulators.

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A Sales Prediction Method Based on LSTM with Hyper-Parameter Search

Journal of Physics Conference Series ◽

10.1088/1742-6596/1756/1/012015 ◽

2021 ◽

Vol 1756 (1) ◽

pp. 012015

Author(s):

Yun Dai ◽

Jinghao Huang

Keyword(s):

Prediction Method ◽

Sales Prediction ◽

Parameter Search

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Fast Implementation of Insect Multi-Target Detection Based on Multimodal Optimization

Remote Sensing ◽

10.3390/rs13040594 ◽

2021 ◽

Vol 13 (4) ◽

pp. 594

Author(s):

Rui Wang ◽

Yiming Zhang ◽

Weiming Tian ◽

Jiong Cai ◽

Cheng Hu ◽

...

Keyword(s):

Target Detection ◽

Computational Cost ◽

Detection Performance ◽

Coherent Detection ◽

Observation Data ◽

Multimodal Optimization ◽

Performance Simulation ◽

Single Target ◽

Parameter Search ◽

Long Time

Entomological radars are important for scientific research of insect migration and early warning of migratory pests. However, insects are hard to detect because of their tiny size and highly maneuvering trajectory. Generalized Radon–Fourier transform (GRFT) has been proposed for effective weak maneuvering target detection by long-time coherent detection via jointly motion parameter search, but the heavy computational burden makes it impractical in real signal processing. Particle swarm optimization (PSO) has been used to achieve GRFT detection by fast heuristic parameter search, but it suffers from obvious detection probability loss and is only suitable for single target detection. In this paper, we convert the realization of GRFT into a multimodal optimization problem for insect multi-target detection. A novel niching method without radius parameter is proposed to detect unevenly distributed insect targets. Species reset and boundary constraint strategy are used to improve the detection performance. Simulation analyses of detection performance and computational cost are given to prove the effectiveness of the proposed method. Furthermore, real observation data acquired from a Ku-band entomological radar is used to test this method. The results show that it has better performance on detected target amount and track continuity in insect multi-target detection.

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