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Deep Reinforcement Learning Applications
Deep Reinforcement Learning Applications
UAV tracking and detection.mp4UAV-Assisted Moving Object Detection and Tracking Using Deep Reinforcement Learing in Urban Environments
UAV-Assisted Moving Object Detection and Tracking Using Deep Reinforcement Learing in Urban Environments
UAV-assisted integrated object detection and tracking system
Moving-object detection using self-attention and heatmap -based CNN model
DQN-based deep reinforcement learning object tracking
Apply to urban environments for various weather conditions
TSC.mp4Pedestrian-Aware Vehicle Traffic Signal Control Using Dueling DQN
Pedestrian-Aware Vehicle Traffic Signal Control Using Dueling DQN
Pedestrian-aware TSC
Multi-purpose objective function in terms of vehicle waiting time, emergency vehicle preference, lane fairness, and pedestrian maximum waiting time requirements.
Dueling DQN based deep reinforcement learning architecture
AutoDriving.mp4Autonomous Driving using Deep Q-Network (DQN)
Autonomous Driving using Deep Q-Network (DQN)
Autonomous driving using DQN reinforcement learning
Implement digital twin environments
Considering obstacles, other vehicles, and traffic signals
Efficient DQN learning architecture
Reward function design
TrafficSignalControl.mp4Deep Reinforcement Learning-based Traffic Signal Control
Deep Reinforcement Learning-based Traffic Signal Control
Optimal vehicle traffic signal control
Reduce waiting time and queue length
Provide fairness and consider emergency vehicles
Support inter-signal coordination between intersections
Jointly consider vehicle traffic and pedestrian waiting time at the crosswalks.
Internet of Things (IoT) Applications
Internet of Things (IoT) Applications
Evacuation_Path_ML(2021).mp4Evacuation_Path(GBM)2021.mp4Evacuation_path2(2021).mp4AR Emergency Evacuation System Using Machine Learning and IoT Technologies for Indoor Environments (2020~2021)
AR Emergency Evacuation System Using Machine Learning and IoT Technologies for Indoor Environments (2020~2021)
Indoor disaster monitoring IoT sensor network implementation (Rassberry Pi embedded devices)
Q-learning based sensing data routing protocol implementation at IoT edge devices
GBM (Gradient Boosting Machine) based Ensemble Prediction for disaster area prediction
Beacon IoT device implementation and RSS (Received Signal Strength-based DNN (Deep Neural Network) positioning
Reinforcement learning based optimum evacuation path decision
WiFi and 5G based communication for emergency information delivery
AR(Augmented Reality) based individual navigation system: avoiding the disaster areas and congested areas for fast and safe evacuation.
WildFirePredictionRNN(2018).mp4DNN- and RNN-Based Wildfire Detection and Disaster Area Prediction Simulator in Wireless Sensor Networks (2018)
DNN- and RNN-Based Wildfire Detection and Disaster Area Prediction Simulator in Wireless Sensor Networks (2018)
Wildfire detection wireless sensor network topology simulation
Adaptive duty-cycle enabled sensor node implementation for energy saving
DNN-based sensing value estimation for in-active sensors using active sensor information
RNN-based disaster area prediction
GestureRecogDNN(2017).movCNN-Based Gesture Recognition Using Ultrasonic Signal and Remote IoT Device Control System (2018)
CNN-Based Gesture Recognition Using Ultrasonic Signal and Remote IoT Device Control System (2018)
Ultrasonic sinusoidal signal generation
Doppler effect visualization due to the gestures
CNN-based gesture recognition
MQTT IoT protocol using cloud server
Remote home automation IoT device control
UAViotSensing(2016).mp4UAV Assisted IoT Sensor Networking and UAV Path Planning Simulator (2016-2017)
UAV Assisted IoT Sensor Networking and UAV Path Planning Simulator (2016-2017)
Sensing data acquisition using multiple UAVs
Optimum UAV path decision with energy and time constraints
UAV risk managements due to the geographical conditions
Multiple UAV connectivity control and ad-hoc routing
Bio-inspired AI algorithm implementation: GA (Genetic Algorithm), ACO (Ant Colony Optimization), and PSO(Particle Swarm Optimization)
Vehicular and Flying Ad-hoc Network Applications
Vehicular and Flying Ad-hoc Network Applications
군집주행.wmv자율주행자동차(DNN)(2018).mp4VANETq-learning(2017).mp4Machine Learning-Based Autonomous Driving and Vehicular Ad-hoc Network Simulator (2016-2018)
Machine Learning-Based Autonomous Driving and Vehicular Ad-hoc Network Simulator (2016-2018)
CNN based lane and vanishing point detection
Q-learning based autonomous driving implementation using robot mobile platform
Vehicle platooning implementation using reinforcement learning
DNN-based autonomous driving simulator implementation
Reinforcement Learning based VANET routing simulator
FANETRouting(2016).mp4Flying Ad-hoc Network (FANET) Routing Simulator (2016)
Flying Ad-hoc Network (FANET) Routing Simulator (2016)
Directional beam-forming for secure and reliable data transmission
On-demand Ad-hoc routing between UAVs
Dynamic direction and angle adjustment algorithm implementation
GUI based FANET simulator implementation
Cognitive Radio Network Applications
Cognitive Radio Network Applications
CRPolicyEngine(2013).wmvGAbasedCR(2015).aviCRsimulator(2013).mp4CR-AdhocCCCselection(2012).aviCognitive Radio Networking Simulator (2012-2015)
Cognitive Radio Networking Simulator (2012-2015)
Tactical military network environment configuration
Cognitive radio policy engine implementation
CR tactical cluster-based ad-hoc networking simulator
Dynamic frequency hopping channel management
Common control channel selection simulator
GA(Genetic Algorithm)-based CR network coexist management
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