531 skills found · Page 5 of 18
bernhard-schmitzer / MultiScaleOTMore user-friendly version of multi-scale algorithm library for optimal transport.
danyalimran93 / Artificial Intelligence State Space SearchDifferent Searching algorithms (DFS, BFS, IDS, Greedy, A*) opting to find optimal path from source to destination
jgallagher / GosacaImplementation of "An Optimal Suffix Array Construction Algorithm" described in a Technical Report by Ge Nong
SmileOfHeart / TrainControlOptimationOptimal for traction energy consumption of subway by different Algorithm
Mr-November / MicsolverAn actuator space optimal kinematic path tracking framework for tendon-driven continuum robots: Theory, algorithm and validation
RishabhArya / Campus Navigation SystemDijkstra Algorithm is one of the most famous algorithms in computer science. There might be several possible routes to reach a destination point. If someone doesn’t travel through optimal path, it will consume more time and energy. This project aims to determine locations of the node that reflect all the nodes in the list, build the route by connecting nodes and evaluate the optimal path by using Dijkstra algorithm. Dijkstra’s Algorithm is also known as a single source shortest path algorithm which is used to find the shortest distance/path from one node to another node in a graph. This algorithm can be used only for positive distances from one location to another.
joemar25 / 8 PuzzleA Final Project for CS Elective 1 (Artificial Intelligence), a C++ Project that we made using the IDS and A* search algorithm for finding the optimal solution.
Sdutta12a / Implementation Of Glasius Bio Inspires Neural Network For Complete Coverage Path Planning Of AUVComplete coverage path planning for an AUV uses the Glasius Bio-Inspired Neural Network (GBNN) algorithm. The underwater workspace is discretized into a 2D grid map, forming a corresponding neural network. The GBNN algorithm updates neural activity to reflect grid states, guiding the AUV for optimal path coverage and obstacle avoidance.
Nirespire / Dijkstra Travel PlannerA travel planner that calculates the optimal travel route by plane or bus based on Dijkstra's famous graph algorithm
kshitija2 / Interactive Multi Objective Reinforcement LearningMulti-objective reinforcement learning deals with finding policies for tasks where there are multiple distinct criteria to optimize for. Since there may be trade-offs between the criteria, there does not necessarily exist a globally best policy; instead, the goal is to find Pareto optimal policies that are the best for certain preference functions. The Pareto Q-learning algorithm looks for all Pareto optimal policies at the same time. Introduced a variant of Pareto Q-learning that asks queries to a user, who is assumed to have an underlying preference function and also the scalarized Q-learning algorithm which reduces the dimensionality of multi-objective space by using scalarization function and ask user preferences by taking weights for scalarization. The goal is to find the optimal policy for that user’s preference function as quickly as possible. Used two benchmark problems i.e. Deep Sea Treasure and Resource Collection for experiments.
Lei-IT / GMAMPGeneralized approximate message passing (GAMP) and generalized vector AMP (GVAMP) are Bayes-optimal algorithms widely used for unknown signal reconstruction of generalized linear models (GLM). However, they both have their own limitations, i.e., either requirements for independent and identically distributed (IID) transformation matrices or high-complexity matrix inverse. In this article, we provide a universal generalized memory AMP (GMAMP) framework including the existing orthogonal AMP/VAMP, GVAMP, and MAMP as instances. It gives new directions to address GLM and performs well in ill-conditional systems with low complexity. The proposed Bayes-optimal GMAMP is an example that overcomes the IID-matrix limitation of GAMP and avoids the high-complexity matrix inverse in GVAMP. Our proposed framework paves the way for compressed sensing, imaging, signal processing, communications, deep learning, and other fields.
devamsheth21 / Bearing Fault Detection Using Deep Learning ApproachDetection and multi-class classification of Bearing faults using Image classification from Case Western Reserve University data of bearing vibrations recorded at different frequencies. Developed an algorithm to convert vibrational data into Symmetrized Dot Pattern images based on a Research paper. Created an Image dataset of 50 different parameters and 4 different fault classes, to select optimum parameters for efficient classification. Trained and tested 50 different datasets on different Image-net models to obtain maximum accuracy. Obtained an accuracy of 98% for Binary classification of Inner and Outer race faults on Efficient Net B7 model on just 5 epochs.
dylan-campbell / GosmaThe Globally-Optimal Spherical Mixture Alignment (GOSMA) algorithm
bo-pang / Time Varying ADPSeveral ADP algorithms code for the data-driven optimal control of linear time-varying systems
rithinch / Pareto Optimal Student Supervisor Allocation🎓An AI tool to assist universities with optimal allocation of students to supervisors for their dissertations. Devised a multi-objective genetic algorithm for the task.
jundsp / Fast Partial TrackingFast partial tracking of audio with real-time capability through linear programming. Hungarian algorithm provides optimal spectral peak-to-peak matching in polynomial time.
kylestach / ControlsToy implementations of algorithms for path planning, system modelling, optimal control, estimation, and more.
Akshaybj0221 / Obstacle Avoidance AStarUsed Hough Planes to convert 3D obstacles into a 2D grid. Used A Star algorithm to find optimal path from Start point to Goal point. Also planned to incorporate an A-Star algorithm for a 3D point cloud map which we get by running SLAM on Turtlebot in the Robotics Realization Lab, UMD, CP
trinhminhtriet / A Star SearchA* (A-star) algorithm is a widely used graph traversal and pathfinding algorithm known for its completeness, optimality, and efficiency.
JuliaQuantumControl / QuOptimalControl.jlLibrary for solving quantum optimal control problems in Julia. Currently offers support for GRAPE and dCRAB algorithms using piecewise constant controls.
