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Journal Articles
Accepted Manuscript
Publisher: ASME
Article Type: Editorial
J. Comput. Inf. Sci. Eng.
Paper No: JCISE-25-1062
Published Online: February 14, 2025
Topics:
User interfaces
Journal Articles
Accepted Manuscript
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng.
Paper No: JCISE-24-1446
Published Online: February 13, 2025
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. March 2025, 25(3): 031008.
Paper No: JCISE-24-1071
Published Online: February 6, 2025
Journal Articles
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. March 2025, 25(3): 031009.
Paper No: JCISE-24-1127
Published Online: February 6, 2025
Topics:
Modal analysis
Journal Articles
Accepted Manuscript
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng.
Paper No: JCISE-24-1255
Published Online: February 6, 2025
Journal Articles
Accepted Manuscript
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng.
Paper No: JCISE-24-1553
Published Online: February 6, 2025
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 1 Reward structure representation for the chaser drone More about this image found in Reward structure representation for the chaser drone
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 2 The proposed DDPG-based model consists of actor, critic, and target networks for learning the control policy for the chaser drone More about this image found in The proposed DDPG-based model consists of actor, critic, and target network...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 3 Comprehensive system architecture for training and evaluating the RL-based chaser drone policy using Gazebo, ROS, YOLOv5, DDPG, and Ardupilot More about this image found in Comprehensive system architecture for training and evaluating the RL-based ...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 4 ( a ) Total reward per episode during training episodes of the δ c h a s e r and ( b ) critic loss, when learning policy for δ c h a s e r More about this image found in ( a ) Total reward per episode during training episodes of the δ ...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 5 ( a ) Total reward per episode during evaluation, when the trained policy was used without any updates and ( b ) the absolute value error during this process More about this image found in ( a ) Total reward per episode during evaluation, when the trained pol...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 6 δ c h a s e r ’s view and trajectory followed. The top part shows the FOV view, and the bottom part shows the trajectory of δ c h a s e r and δ i n t r u d e r . The X - and Y -axis represent distance in meters. More about this image found in δ c h a s e r ’s view and trajectory followed. The top part show...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 7 Reward spread along with its sub-components for long endurance test during evaluation and average mean trajectory and distance errors when the episode ran continuously for 4 h in Gazebo More about this image found in Reward spread along with its sub-components for long endurance test during ...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 8 ( a ) Average mean trajectory error and ( b ) average mean distance error in various testing environments More about this image found in ( a ) Average mean trajectory error and ( b ) average mean dis...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 9 Total reward per episode during evaluation episodes of the chaser drone in various testing environments More about this image found in Total reward per episode during evaluation episodes of the chaser drone in ...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 10 Comparison of the learned policy versus the PID controller during evaluation for variable trajectories. The graph shows the total reward accumulated over time, highlighting the effectiveness of the RL-based policy in maintaining the intruder within the FOV and adapting to sudden changes in... More about this image found in Comparison of the learned policy versus the PID controller during evaluatio...
Image
in Deep Reinforcement Learning Based Localization and Tracking of Intruder Drone
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 11 Comparison of learned policy versus PID controller during evaluation based on TTT. When the δ i n t r u d e r is detected in FOV, the instance is recorded and shown in the graph. More about this image found in Comparison of learned policy versus PID controller during evaluation based ...
Image
in MODAL-DRN-BL: A Framework for Modal Analysis Based on Dilated Residual Broad Learning Networks
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 1 The MODAL-DRN-D-BL-38 framework. Each rectangular block represents a Conv-BN-ReLU group, except for the 11 × 11 Conv 10, which is specified by its filter size and channel count (BN (batch normalization) and ReLU (rectified linear unit)). The network is organized into layers in such ... More about this image found in The MODAL-DRN-D-BL-38 framework. Each rectangular block represents a Conv-B...
Image
in MODAL-DRN-BL: A Framework for Modal Analysis Based on Dilated Residual Broad Learning Networks
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 2 MAE scores of different models More about this image found in MAE scores of different models
Image
in MODAL-DRN-BL: A Framework for Modal Analysis Based on Dilated Residual Broad Learning Networks
> Journal of Computing and Information Science in Engineering
Published Online: February 6, 2025
Fig. 3 MAE scores of different layers on MODAL-DRN models More about this image found in MAE scores of different layers on MODAL-DRN models
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