Ma-raca

3D-Point-Cloud-Analytics

Portfolio for 3D Point Cloud Processing from www.shenlanxueyuan.com China

Chaotic-GSA-for-Engineering-Design-Problems

All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.

Det3D

A general 3D object detection codebse.

enginnering-design-problems-with-python

classical engineering design problems single objective fitness function pressure vessel welded beam and tension/compression spring design optimization problems

hdrnet

An implementation of 'Deep Bilateral Learning for Real-Time Image Enhancement', SIGGRAPH 2017

hello-world

This is a try

kitti_eval

mic_array

DOA, VAD and KWS for ReSpeaker Microphone Array

nlopt

library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

Open3D

Open3D: A Modern Library for 3D Data Processing

Ma-raca/UMAD-dataset

Ma-raca/UMAD-dataset-page

Ma-raca/nlopt

library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

Ma-raca/enginnering-design-problems-with-python

classical engineering design problems single objective fitness function pressure vessel welded beam and tension/compression spring design optimization problems

Ma-raca/OpenPCDet

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

Ma-raca/Det3D

A general 3D object detection codebse.

Ma-raca/pointpillars_pytorch_trt

Ma-raca/3D-Point-Cloud-Analytics

Portfolio for 3D Point Cloud Processing from www.shenlanxueyuan.com China

Ma-raca/StereoVision

Library and utilities for 3d reconstruction from stereo cameras.

Ma-raca/Open3D

Open3D: A Modern Library for 3D Data Processing

Ma-raca/Chaotic-GSA-for-Engineering-Design-Problems

All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.

Ma-raca/hello-world

This is a try

Ma-raca/kitti_eval

Ma-raca/mic_array

DOA, VAD and KWS for ReSpeaker Microphone Array

Ma-raca/hdrnet

An implementation of 'Deep Bilateral Learning for Real-Time Image Enhancement', SIGGRAPH 2017