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VCSAP: Online reinforcement learning exploration method based on visitation count of state-action pairs.
Neural Netw
January 2025
Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Changchun 130012, China; College of Computer Science and Technology, Jilin University, Changchun 130012, China; College of Software, Jilin University, Changchun 130012, China. Electronic address:
In the domain of online reinforcement learning, strategies that leverage inherent rewards for exploration tend to achieve commendable outcomes within contexts characterized by deceptive or sparse rewards. Counting through the visitation of states is an efficient count-based exploration method to get the proper intrinsic reward. However, only the novelty of the states encountered by the agent is considered in this exploration method, resulting in the over-exploration of a certain state-action pair and falling into a locally optimal solution.
View Article and Find Full Text PDFA novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles.
Front Plant Sci
December 2024
Institute of Technology, Anhui Agricultural University, Hefei, China.
Introduction: The rapid urbanization of rural regions, along with an aging population, has resulted in a substantial manpower scarcity for agricultural output, necessitating the urgent development of highly intelligent and accurate agricultural equipment technologies.
Methods: This research introduces YOLOv8-PSS, an enhanced lightweight obstacle detection model, to increase the effectiveness and safety of unmanned agricultural robots in intricate field situations. This YOLOv8-based model incorporates a depth camera to precisely identify and locate impediments in the way of autonomous agricultural equipment.
A cutting-edge neural network approach for predicting the thermoelectric efficiency of defective gamma-graphyne nanoribbons.
Sci Rep
January 2025
Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China.
This study predicts the thermoelectric figure of merit (ZT) for defective gamma-graphyne nanoribbons (γ-GYNRs) using binary coding, convolutional neural networks (CNN), long short-term memory networks (LSTM), and multi-scale feature fusion. The approach accurately predicts ZT values with only 500 initial structures (3% of 16,512 candidates), achieving an R above 0.91 and a mean absolute error (MAE) of 0.
View Article and Find Full Text PDFValidity of the Actigraph-GT9X accelerometer for measuring steps and energy expenditures in heart failure patients.
PLoS One
January 2025
Department of Kinesiology and Health Science, Virginia Commonwealth University, Richmond, VA, United States of America.
Introduction: The ActiGraph (AG) accelerometer is widely used to assess physical activity (PA) in heart failure (HF) patients. However, the validity of the AG in this population remains unexplored.
Objective: Therefore, this study examined the criterion validity of the AG-GT9X for measuring step counts (SC) and energy expenditure (EE) among HF patients.
Evaluation of an acne lesion detection and severity grading model for Chinese population in online and offline healthcare scenarios.
Sci Rep
January 2025
Ping An Technology, Shanghai, China.
Accurate acne severity grading is crucial for effective clinical treatment and timely follow-up management. Although some artificial intelligence methods have been developed to automate the process of acne severity grading, the diversity of acne image capture sources and the various application scenarios can affect their performance. Therefore, it's necessary to design special methods and evaluate them systematically before introducing them into clinical practice.
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