Search Results for author:
Found 19 papers, 3 papers with code
Two-Stage Violence Detection Using ViTPose and Classification Models at Smart Airports
This study introduces an innovative violence detection framework tailored to the unique requirements of smart airports, where prompt responses to violent situations are crucial.
Dynamic deep-reinforcement-learning algorithm in Partially Observed Markov Decision Processes
Reinforcement learning has been greatly improved in recent studies and an increased interest in real-world implementation has emerged in recent years.
Region of Attraction Estimation Using Union Theorem in Sum-of-Squares Optimization
The main novelty of this paper is the Union theorem which enables the use of multiple shape functions to create a polynomial Lyapunov function encompassing all the areas generated by the shape functions.
An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards
In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties.
Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables.
A Transistor Operations Model for Deep Learning Energy Consumption Scaling Law
At a general computational energy model level, there is both strong dependency to both the hardware architecture (e. g. generic processors with different configuration of inner components- CPU and GPU, programmable integrated circuits - FPGA), as well as different interacting energy consumption aspects (e. g., data movement, calculation, control).
Two-timescale Resource Allocation for Automated Networks in IIoT
The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT).
Bayesian Learning Approach to Model Predictive Control
High-level frameworks have been developed separately in the earlier studies on Bayesian learning and sampling-based model predictive control.
Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space
We believe that this approach of exploiting general data distribution knowledge form neural networks can be applied to a wide range of scarce data open challenges.
A Learning-Based Computational Impact Time Guidance
This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem.
Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation
Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning.
RoIFusion: 3D Object Detection from LiDAR and Vision
When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensor (e. g. camera, LIDAR) typically increases the robustness of 3D detectors.
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions
In this paper, we achieve partially explainable learning model by symbolically explaining the role of activation functions (AF) under a scalable topology.
Improving Learning Effectiveness For Object Detection and Classification in Cluttered Backgrounds
The performance of the proposed framework is investigated through empirical tests and compared with that of the model trained with the COCO dataset.
Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
Unlike conventional operation that directly applies MLPs on high-dimensional features of point cloud, our model goes wider by splitting features into groups in advance, and each group with certain smaller depth is only responsible for respective MLP operation, which can reduce complexity and allows to encode more useful information.
A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design
This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in flight control.
Fast Hierarchical Neural Network for Feature Learning on Point Cloud
In order to balance model performance and complexity, we introduce a novel neural network architecture exploiting local features from a manually subsampled point set.
GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud
In this paper, we propose a novel neural network for point cloud, dubbed GAPNet, to learn local geometric representations by embedding graph attention mechanism within stacked Multi-Layer-Perceptron (MLP) layers.
Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents.
