Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applic...Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.展开更多
Facial expressions in nonhuman primates are complex processes involving psychological,emotional,and physiological factors,and may use subtle signals to communicate significant information.However,uncertainty surrounds...Facial expressions in nonhuman primates are complex processes involving psychological,emotional,and physiological factors,and may use subtle signals to communicate significant information.However,uncertainty surrounds the functional significance of subtle facial expressions in animals.Using artificial intelligence(AI),this study found that nonhuman primates exhibit subtle facial expressions that are undetectable by human observers.We focused on the golden snub-nosed monkeys(Rhinopithecus roxellana),a primate species with a multilevel society.We collected 3427 front-facing images of monkeys from 275 video clips captured in both wild and laboratory settings.Three deep learning models,EfficientNet,RepMLP,and Tokens-To-Token ViT,were utilized for AI recognition.To compare the accuracy of human performance,two groups were recruited:one with prior animal observation experience and one without any such experience.The results showed human observers to correctly detect facial expressions(32.1%for inexperienced humans and 45.0%for experienced humans on average with a chance level of 33%).In contrast,the AI deep learning models achieved significantly higher accuracy rates.The best-performing model achieved an accuracy of 94.5%.Our results provide evidence that golden snub-nosed monkeys exhibit subtle facial expressions.The results further our understanding of animal facial expressions and also how such modes of communication may contribute to the origin of complex primate social systems.展开更多
基金supported by the National Key Research and Development Program of China(No.2023YFF0715103)-financial supportNational Natural Science Foundation of China(Grant Nos.62306237 and 62006191)-financial support+1 种基金Key Research and Development Program of Shaanxi(Nos.2024GX-YBXM-149 and 2021ZDLGY15-04)-financial support,NorthwestUniversity Graduate Innovation Project(No.CX2023194)-financial supportNatural Science Foundation of Shaanxi(No.2023-JC-QN-0750)-financial support.
文摘Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.
基金the participants of the human test.This study was supported by the National Natural Science Foundation of China(32101238,32471565,31730104,and 62006191)the West Light Foundation of The Chinese Academy of Science(XAB2020YW04)+4 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB31020302)the Key Research and Development Program of Shaanxi(2021ZDLGY15-01,2021ZDLGY09-04,2022ZDLGY06-07,2021ZDL GY15-04,2021GY-004,and 2020GY-050)the Project to Attract Foreign Expert of China(G2022040013L)the Shaanxi Fundamental Science Research Project forMathematics and Physics(22JHQ038)the International Science and Technology Cooperation Research Project of Shenzhen(GJHZ20200731095204013).
文摘Facial expressions in nonhuman primates are complex processes involving psychological,emotional,and physiological factors,and may use subtle signals to communicate significant information.However,uncertainty surrounds the functional significance of subtle facial expressions in animals.Using artificial intelligence(AI),this study found that nonhuman primates exhibit subtle facial expressions that are undetectable by human observers.We focused on the golden snub-nosed monkeys(Rhinopithecus roxellana),a primate species with a multilevel society.We collected 3427 front-facing images of monkeys from 275 video clips captured in both wild and laboratory settings.Three deep learning models,EfficientNet,RepMLP,and Tokens-To-Token ViT,were utilized for AI recognition.To compare the accuracy of human performance,two groups were recruited:one with prior animal observation experience and one without any such experience.The results showed human observers to correctly detect facial expressions(32.1%for inexperienced humans and 45.0%for experienced humans on average with a chance level of 33%).In contrast,the AI deep learning models achieved significantly higher accuracy rates.The best-performing model achieved an accuracy of 94.5%.Our results provide evidence that golden snub-nosed monkeys exhibit subtle facial expressions.The results further our understanding of animal facial expressions and also how such modes of communication may contribute to the origin of complex primate social systems.
