DSpace-CRIS

Now showing 1 - 5 of 5

Some of the metrics are blocked by your
consent settings
Design of an integrated model for pothole detection and repair optimization using multimodal transformers and hybrid deep learning
(2025-12-01)
Meshram, Kundan
;
Saurabh, Aryan
;
Kharole, Vinay
;
Chatrabhuj
;
Mishra, Umank
The detection and timely repair of potholes are crucial for maintaining road safety and minimizing vehicle damage. However, existing methods often suffer from limitations such as reliance on single-modal data, poor generalization across diverse environments, and suboptimal resource management. To address these challenges, we propose a comprehensive framework for enhanced pothole detection and repair optimization using advanced deep learning techniques. Our approach integrates four key methodologies: Multimodal Enhanced Pothole Detection with Person-Level Data (M-E-Pot holeNet), Hybrid Machine Learning-Deep Learning for Classification (Hybrid-Pot holeNet), Deep Reinforcement Learning for Pot hole Detection and Repair Optimization (DRL-Pot holeOpt), and Transfer Learning for Pothole Detection in Diverse Environments (TL-Pot holeAdaptNet). M-E-Pot holeNet employs a Self-Supervised Multimodal Transformer (SSMT) to fuse camera, accelerometer, and crowdsourced smartphone data, achieving robust detection with a 97 % accuracy and under 2 % false positive rate. Hybrid-Pot holeNet combines Graph Attention Networks (GAT) and XGBoost, modeling spatial road features to classify potholes with 95 % accuracy and an F1-Score of 0.92. DRL-Pot holeOpt uses Soft Actor-Critic (SAC) with Bayesian Optimization to efficiently schedule repair tasks, reducing repair costs by up to 20 % and crew travel time by 15–25 %. Finally, TL-Pot holeAdaptNet leverages Domain-Adversarial Neural Networks (DANN) to ensure cross-domain adaptability, with 90 % accuracy in new environments and a 40–50 % reduction in domain discrepancy. This multi-faceted approach addresses the limitations of previous work by providing scalable, real-time, and resource-optimized solutions for pothole detection and maintenance, offering significant improvements in accuracy, cost efficiency, and adaptability.
Some of the metrics are blocked by your
consent settings
Reinforcement learning applied to scrum team towards large-scale global optimization
(2019-02-02)
Nootyaskool, Supakit
;
Ounsrimuang, Pimolrat
Large-scale problems have size of problem over a thousand dimensions in finding a best solution that uses long computation times. In this work, we use an idea of scrum methodology that is a well-known in software development companies, to create an optimization algorithm. The scrum methodology describing about the team organization likes as rugby team management that player have expert in game. The proposed algorithm is developed based on concept of the evolutionary computation by this work added agent specifics in leaning environment of the problem. The specific of the agent is reinforcement learning by taking an action and getting reward. The proposed algorithm was experimented on a large-scale global optimization finding optimum point of numerical function, comparing between with and without reinforcement learning. The experiment result showed that the usage of reinforcement learning has good results.
Some of the metrics are blocked by your
consent settings
The implementation of spectrum sensing and spectrum allocation on cognitive radio
(2017-03-29)
Rungsawang, Pitcha
;
Khawne, Amnach
Cognitive Radio is a system designed to use radio spectrum in the most efficient way possible - as demands for radio spectrum have been heavily increased nowadays. The system works by allowing unlicensed users to use radio channel on the licensed users' spectrum without interfering with the licensed users. The system needs spectrum sensing to verify the usage of each specific spectrum and in ever-shifting environment, Reinforcement Learning is also used for establishing spectrum allocation as well.
Some of the metrics are blocked by your
consent settings
Prototype selection using reinforcement learning and minimal consistent subset identification guide
(2010-01-01)
Kruatrachue, Boontee
;
Choowong, Teeratorn
This paper try to apply Reinforcement Learning (RL) to a task with large number of states. This usually is a difficult task since RL has less chance to visit all state or has enough number of visit to learn average reward accurately. Moreover, RL may not be able to learn or obtain any optimal solution as RL learn by averaging rewards from each action performing in each state. In order to alleviate this RL learning problem, any solution to a task such as, non-optimal algorithm or heuristics can collaborate with RL by using their knowledge to prune the non-optimal action in each state. This reduces search space of RL and helps it learn faster. A Minimal consistent subset problem is used as an example to demonstrate how RL can learn faster with the help of other heuristics. ©ICROS.
Some of the metrics are blocked by your
consent settings
Episode continuation and exploration start for reinforcement learning with large number of states
(2009-01-01)
Kruatrachue, Boontee
;
Anantapornkit, Ekaphol
This paper presents simple techniques to improve the learning rate of the RL algorithm in a task with large number of states that the algorithm cannot fully explore by going through all possible states. The problem with the standard RL algorithm is that varying the ε parameter in the ε-greedy policy is not sufficient to improve its learning performance. The techniques allow the algorithm to focus on exploring the local path to eventually obtain more rewards and occasionally switching to another path to avoid being trapped in the local region of the search space. In order to investigate the effectiveness of the techniques, the minimal consistent subset identification (MCSI) problem is used as a test problem. The paper concludes by comparing the size of the identified subset obtained from the standard RL algorithm and the proposed algorithm along with those of the standard MCSI method.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results