A space crawling robotic bio-paw (SCRBP) enabled by triboelectric sensors for surface identification

Hou, Xuyan; Zhang, Li; Su, Yilin; Gao, Guowei; Liu, Yuhui; Na, Zhonglai; Xu, QingZhang; Ding, Tianxiang; Xiao, Li; Li, Long; Chen, Tao

doi:10.1016/j.nanoen.2022.108013

Cited by 103 publications

(32 citation statements)

References 43 publications

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“…Improvement of Biological Neural Networks Linear Differentiation Method-The algorithm modifies the convergence coefficient to increase the share of common searches, avoid hitting the optimal solution in the area, increase memory capacity, improve convergence efficiency, increase weights for different positions, and perform searches. [56][57][58] Be clear on your direction and use greedy strategies to avoid excessive unnecessary searches. The new RSEFNN combines neural network linear differentiation schemes with Lyapunov stabilization methods for nonlinear systems and UAV applications.…”

Section: Discussion Conclusion and Future Studymentioning

confidence: 99%

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Chen,

Meng,

Wang

et al. 2024

International Journal of Advanced Robotic Systems

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Currently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative unmanned aerial vehicles will offer diverse interpretations which support a multiperspective view of the scene. Due to diverse interpretations of unmanned aerial vehicles usually deviates, thus, unmanned aerial vehicles require a consensus interpretation for the scenario. To previous purposes, this study presents an original consensus-based method to pilot multi-unmanned aerial vehicle systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Further, a fuzzy neural network generalized prediction control system known as a recurrent self-evolving fuzzy neural network is mainly used to ensure stability through the use of a descending gradient online learning rule. At the same time, users can think along the lines of evolutionary biological design. Unmanned aerial vehicles can be modeled as system experts for solving group problems that require the definition of conditions that best describe the scene. First, this method allows each unmanned aerial vehicle to set high-level conditions for detection events by aggregating events based on fuzzy information. These aggregated events are modeled by a fuzzy system ontology, which allows each unmanned aerial vehicle to report its preferences in conditions. Therefore, the interpretation of each drone is compressed to achieve a collective interpretation of the state. The final polls, consent and affinity polls confirmed the final decision group’s reliability ratings. The rated consensus indicates how well the collective interpretation of the scene matches each drone’s point of view.

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Section: Discussion Conclusion and Future Studymentioning

confidence: 99%

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Chen,

Meng,

Wang

et al. 2024

International Journal of Advanced Robotic Systems

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“…Finally, count the number of channels that can be matched; when it is greater than the threshold value of the number of channels set, it is considered that the human fall behavior occurred in this experiment, and determine the preperceived time of human fall [20][21][22][23][24][25].…”

Section: Intelligent Perception Of Human Fallmentioning

confidence: 99%

Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics

Ding

2023

Journal of Sensors

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In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. It can be widely used in human-computer interaction, machine vision, and other fields.

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“…This approach surpasses conventional methods in discovering superior solutions for resource allocation across various service types. In Reference [80], the authors address the challenge of providing 5G services in an automated warehouse environment by utilizing swarm robotics [81][82][83] controlled using an industrial controller over the 5G network. They suggest a game-theoretic Coordinated Multipoint clustering algorithm integrated with PPO to manage the warehouse robots responsible for storing goods.…”

Section: Proximal Policy Optimization (Ppo)mentioning

confidence: 99%

Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

Khani,

Jamali,

Sohrabi

et al. 2023

Trans Emerging Tel Tech

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This paper reviews recent research on resource allocation in 5G cloud‐based radio access networks (C‐RAN) using deep reinforcement learning (DRL) algorithms. It explores the potential of DRL for learning complex decision‐making policies without human intervention. The paper first introduces the C‐RAN architecture and resource allocation concepts, followed by an overview of DRL algorithms applied to C‐RAN. It discusses the challenges and potential solutions in applying DRL to C‐RAN resource allocation, including scalability, convergence, and fairness. The review concludes by highlighting open research directions for future investigation. By providing insights into the state‐of‐the‐art techniques for resource allocation in 5G C‐RAN using DRL, this paper emphasizes their potential impact on advancing 5G network technology.

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A space crawling robotic bio-paw (SCRBP) enabled by triboelectric sensors for surface identification

Cited by 103 publications

References 43 publications

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics

Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

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