Prosthetic devices designed to assist individuals with damaged or missing body parts have made significant strides,particularly with advancements in machine intelligence and bioengineering.Initially focused on movemen...Prosthetic devices designed to assist individuals with damaged or missing body parts have made significant strides,particularly with advancements in machine intelligence and bioengineering.Initially focused on movement assistance,the field has shifted towards developing prosthetics that function as seamless extensions of the human body.During this progress,a key challenge remains the reduction of interface artifacts between prosthetic components and biological tissues.Soft electronics offer a promising solution due to their structural flexibility and enhanced tissue adaptability.However,achieving full integration of prosthetics with the human body requires both artificial perception and efficient transmission of physical signals.In this context,synaptic devices have garnered attention as next-generation neuromorphic computing elements because of their low power consumption,ability to enable hardware-based learning,and high compatibility with sensing units.These devices have the potential to create artificial pathways for sensory recognition and motor responses,forming a“sensory-neuromorphic system”that emulates synaptic junctions in biological neurons,thereby connecting with impaired biological tissues.Here,we discuss recent developments in prosthetic components and neuromorphic applications with a focus on sensory perception and sensorimotor actuation.Initially,we explore a prosthetic system with advanced sensory units,mechanical softness,and artificial intelligence,followed by the hardware implementation of memory devices that combine calculation and learning functions.We then highlight the importance and mechanisms of soft-form synaptic devices that are compatible with sensing units.Furthermore,we review an artificial sensory-neuromorphic perception system that replicates various biological senses and facilitates sensorimotor loops from sensory receptors,the spinal cord,and motor neurons.Finally,we propose insights into the future of closed-loop neuroprosthetics through the technical integration of soft electronics,including bio-integrated sensors and synaptic devices,into prosthetic systems.展开更多
Rotary steering systems(RSSs)have been increasingly used to develop horizontal wells.A static push-the-bit RSS uses three hydraulic modules with varying degrees of expansion and contraction to achieve changes in the p...Rotary steering systems(RSSs)have been increasingly used to develop horizontal wells.A static push-the-bit RSS uses three hydraulic modules with varying degrees of expansion and contraction to achieve changes in the pushing force acting on the wellbore in different sizes and directions within a circular range,ultimately allowing the wellbore trajectory to be drilled in a predetermined direction.By analyzing its mathematical principles and the actual characteristics of the instrument,a vector force closed-loop control method,including steering and holding modes,was designed.The adjustment criteria for the three hydraulic modules are determined to achieve rapid adjustment of the vector force.The theoretical feasibility of the developed method was verified by comparing its results with the on-site application data of an imported rotary guidance system.展开更多
Conventional open-loop deep brain stimulation(DBS)systems with fixed parameters fail to accommodate interindividual pathological differences in Parkinson's disease(PD)management while potentially inducing adverse ...Conventional open-loop deep brain stimulation(DBS)systems with fixed parameters fail to accommodate interindividual pathological differences in Parkinson's disease(PD)management while potentially inducing adverse effects and causing excessive energy consumption.In this paper,we present an adaptive closed-loop framework integrating a Yogi-optimized proportional–integral–derivative neural network(Yogi-PIDNN)controller.The Yogi-augmented gradient adaptation mechanism accelerates the convergence of general PIDNN controllers in high-dimensional nonlinear control systems while reducing control energy usage.In addition,a system identification method establishes input–output dynamics for pre-training stimulation waveforms,bypassing real-time parameter-tuning constraints and thereby enhancing closed-loop adaptability.Finally,a theoretical analysis based on Lyapunov stability criteria establishes a sufficient condition for closed-loop stability within the identified model.Computational validations demonstrate that our approach restores thalamic relay reliability while reducing energy consumption by(81.0±0.7)%across multi-frequency tests.This study advances adaptive neuromodulation by synergizing data-driven pre-training with stability-guaranteed real-time control,offering a novel framework for energy-efficient and personalized Parkinson's therapy.展开更多
This paper proposes a separated trajectory tracking controller for fishing ships at sea state level 6 to solve the trajectory tracking problem of a fishing ship in a 6-level sea state,and to adapt to different working...This paper proposes a separated trajectory tracking controller for fishing ships at sea state level 6 to solve the trajectory tracking problem of a fishing ship in a 6-level sea state,and to adapt to different working environments and safety requirements.The nonlinear feedback method is used to improve the closed-loop gain shaping algorithm.By introducing the sine function,the problem of excessive control energy of the system can be effectively solved.Moreover,an integral separation design is used to solve the influence of the integral term in conventional PID controllers on the transient performance of the system.In this paper,a common 32.98 m large fiberglass reinforced plastic(FRP)trawler is adopted for simulation research at the winds scale of Beaufort No.7.The results show that the track error is smaller than 3.5 m.The method is safe,feasible,concise and effective and has popularization value in the direction of fishing ship trajectory tracking control.This method can be used to improve the level of informatization and intelligence of fishing ships.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2020R1C1C1005567)supported by the NAVER Digital Bio Innovation Research Fund,funded by NAVER Corporation(Grant No.[37-2023-0040])+3 种基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-00261,Development of low power/low delay/self-power suppliable RF simultaneous information and power transfer system and stretchable electronic epineurium for wireless nerve bypass implementation)supported by Institute for Basic Science(IBS-R015-D1,IBSR015-D2)supported by a grant of the Korea-US Collaborative Research Fund(KUCRF)funded by the Ministry of Science and ICT and Ministry of Health&Welfare,Republic of Korea(Grant Number.RS-2024-00467213)。
文摘Prosthetic devices designed to assist individuals with damaged or missing body parts have made significant strides,particularly with advancements in machine intelligence and bioengineering.Initially focused on movement assistance,the field has shifted towards developing prosthetics that function as seamless extensions of the human body.During this progress,a key challenge remains the reduction of interface artifacts between prosthetic components and biological tissues.Soft electronics offer a promising solution due to their structural flexibility and enhanced tissue adaptability.However,achieving full integration of prosthetics with the human body requires both artificial perception and efficient transmission of physical signals.In this context,synaptic devices have garnered attention as next-generation neuromorphic computing elements because of their low power consumption,ability to enable hardware-based learning,and high compatibility with sensing units.These devices have the potential to create artificial pathways for sensory recognition and motor responses,forming a“sensory-neuromorphic system”that emulates synaptic junctions in biological neurons,thereby connecting with impaired biological tissues.Here,we discuss recent developments in prosthetic components and neuromorphic applications with a focus on sensory perception and sensorimotor actuation.Initially,we explore a prosthetic system with advanced sensory units,mechanical softness,and artificial intelligence,followed by the hardware implementation of memory devices that combine calculation and learning functions.We then highlight the importance and mechanisms of soft-form synaptic devices that are compatible with sensing units.Furthermore,we review an artificial sensory-neuromorphic perception system that replicates various biological senses and facilitates sensorimotor loops from sensory receptors,the spinal cord,and motor neurons.Finally,we propose insights into the future of closed-loop neuroprosthetics through the technical integration of soft electronics,including bio-integrated sensors and synaptic devices,into prosthetic systems.
基金supported by the Opening Foundation of China National Logging Corporation(CNLC20229C06)the China Petroleum Technical Service Corporation's science project'Development and application of 475 rotary steering system'(2024T-001001)。
文摘Rotary steering systems(RSSs)have been increasingly used to develop horizontal wells.A static push-the-bit RSS uses three hydraulic modules with varying degrees of expansion and contraction to achieve changes in the pushing force acting on the wellbore in different sizes and directions within a circular range,ultimately allowing the wellbore trajectory to be drilled in a predetermined direction.By analyzing its mathematical principles and the actual characteristics of the instrument,a vector force closed-loop control method,including steering and holding modes,was designed.The adjustment criteria for the three hydraulic modules are determined to achieve rapid adjustment of the vector force.The theoretical feasibility of the developed method was verified by comparing its results with the on-site application data of an imported rotary guidance system.
基金supported by the National Natural Science Foundation of China(Grant Nos.12372064 and 12172291)the Youth and Middle-Aged Science and Technology Development Program of Shanghai Institute of Technology(Grant No.ZQ2024-10)。
文摘Conventional open-loop deep brain stimulation(DBS)systems with fixed parameters fail to accommodate interindividual pathological differences in Parkinson's disease(PD)management while potentially inducing adverse effects and causing excessive energy consumption.In this paper,we present an adaptive closed-loop framework integrating a Yogi-optimized proportional–integral–derivative neural network(Yogi-PIDNN)controller.The Yogi-augmented gradient adaptation mechanism accelerates the convergence of general PIDNN controllers in high-dimensional nonlinear control systems while reducing control energy usage.In addition,a system identification method establishes input–output dynamics for pre-training stimulation waveforms,bypassing real-time parameter-tuning constraints and thereby enhancing closed-loop adaptability.Finally,a theoretical analysis based on Lyapunov stability criteria establishes a sufficient condition for closed-loop stability within the identified model.Computational validations demonstrate that our approach restores thalamic relay reliability while reducing energy consumption by(81.0±0.7)%across multi-frequency tests.This study advances adaptive neuromodulation by synergizing data-driven pre-training with stability-guaranteed real-time control,offering a novel framework for energy-efficient and personalized Parkinson's therapy.
基金supported by Liaoning Provincial Department of Education 2023 Basic Research Projects for Universities and Colleges(Grant No.JYTQN2023131)Liaoning Provincial Science and Technology Program:Cooperative Control and Recognition of Unmanned Vessels for Fishing Vessel Operation Scenarios(Grant No.600024003)Liaoning Provincial Department of Education Scientific Research Funding Project(Grant No.LJKZ0726).
文摘This paper proposes a separated trajectory tracking controller for fishing ships at sea state level 6 to solve the trajectory tracking problem of a fishing ship in a 6-level sea state,and to adapt to different working environments and safety requirements.The nonlinear feedback method is used to improve the closed-loop gain shaping algorithm.By introducing the sine function,the problem of excessive control energy of the system can be effectively solved.Moreover,an integral separation design is used to solve the influence of the integral term in conventional PID controllers on the transient performance of the system.In this paper,a common 32.98 m large fiberglass reinforced plastic(FRP)trawler is adopted for simulation research at the winds scale of Beaufort No.7.The results show that the track error is smaller than 3.5 m.The method is safe,feasible,concise and effective and has popularization value in the direction of fishing ship trajectory tracking control.This method can be used to improve the level of informatization and intelligence of fishing ships.
