The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an over...The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.展开更多
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc...The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.展开更多
Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,a...Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.展开更多
The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the probl...The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.展开更多
Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires a...Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body.Herein,we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring.The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device.The hydrogel as core substrate exhibits strong toughness and water retention,and the multimodal sensing of temperature,pressure,and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference.The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability.Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring.Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network.Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved,which provides a promising approach for sleep monitoring.展开更多
Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have ...Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.展开更多
Integration of sensors with engineering thermoplastics allows to track their health and surrounding stimuli.As one of vital backbones to construct sensor systems,copper(Cu)is highly conductive and cost-effective,yet t...Integration of sensors with engineering thermoplastics allows to track their health and surrounding stimuli.As one of vital backbones to construct sensor systems,copper(Cu)is highly conductive and cost-effective,yet tends to easily oxidize during and after processing.Herein,an in-situ integrated sensor system on engineering thermoplastics via hybrid laser direct writing is proposed,which primarily consists of laser-passivated functional Cu interconnects and laser-induced carbon-based sensors.Through a one-step photothermal treatment,the resulting functional Cu interconnects after reductive sintering and passivation are capable of resisting long-term oxidation failure at high temperatures(up to 170℃)without additional encapsulations.Interfacing with signal processing units,such an all-in-one system is applied for long-term and real-time temperature monitoring.This integrated sensor system with facile laser manufacturing strategies holds potentials for health monitoring and fault diagnosis of advanced equipment such as aircrafts,automobiles,high-speed trains,and medical devices.展开更多
Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense in...Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information,yet conventional signal processing methods struggle with the massive scale,noise,and artificial sensory systems characteristics of data generated by artificial sensory devices.Integrating artificial intelligence(AI)is essential for addressing these challenges and enhancing the performance of artificial sensory systems,making it a rapidly growing area of research in recent years.However,no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods.In this review,we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses:touch,taste,vision,smell,and hearing.We categorize the AI-enabled capabilities of artificial sensory systems into four key areas:cognitive simulation,perceptual enhancement,adaptive adjustment,and early warning.We introduce specialized AI algorithms and raw data processing methods for each function,designed to enhance and optimize sensing performance.Finally,we offer a perspective on the future of AI-integrated artificial sensory systems,highlighting technical challenges and potential real-world application scenarios for further innovation.Integration of AI with artificial sensory systems will enable advanced multimodal perception,real-time learning,and predictive capabilities.This will drive precise environmental adaptation and personalized feedback,ultimately positioning these systems as foundational technologies in smart healthcare,agriculture,and automation.展开更多
By anchoring Tb^(3+)ions on its free carboxyl groups of the nanocaged NiMOF,a dual-emission self-calibrating sensor of Tb^(3+)@NiMOF was fabricated through coordination post-synthetic modification(PSM)strategy.With Tb...By anchoring Tb^(3+)ions on its free carboxyl groups of the nanocaged NiMOF,a dual-emission self-calibrating sensor of Tb^(3+)@NiMOF was fabricated through coordination post-synthetic modification(PSM)strategy.With Tb^(3+)ions as the secondary fluorescent signal and sensing active sites,Tb^(3+)@NiMOF presents great potentials in visually and efficiently monitoring EPI in serum,with high sensitivity and selectivity,fast response,excellent recyclable,and the low detection limit(LOD,3.06 ng/mL).Furthermore,a tandem combinational logic gate based intelligent detection system was constructed to improve the practicability and convenience of epinephrine(EPI)detection in serum by comparing the light emitted colour with the series standard colour cards preset in the smartphone.This work provides a promising approach of developing metal-organic frameworks(MOFs)based self-calibrating sensors for intelligent detection of bioactive molecules.展开更多
BACKGROUND Most patients who were included in previous studies on achalasia had increased lower esophageal sphincter(LES)pressure.Peroral endoscopic myotomy(POEM)has been confirmed to be effective at relieving the cli...BACKGROUND Most patients who were included in previous studies on achalasia had increased lower esophageal sphincter(LES)pressure.Peroral endoscopic myotomy(POEM)has been confirmed to be effective at relieving the clinical symptoms of achalasia associated with increased LES pressure.AIM To identify the safety and efficacy of POEM for patients with normal LES integrated relaxation pressure(LES-IRP).METHODS The clinical data of patients who underwent POEM successfully in The First Medical Center of Chinese PLA General Hospital were retrospectively analyzed.A total of 481 patients who underwent preoperative high-resolution manometry(HRM)at our hospital were ultimately included in this research.According to the HRM results,the patients were divided into two groups:71 patients were included in the normal LES-IRP group(LES-IRP<15 mmHg)and 410 patients were included in the increased LES-IRP group(LES-IRP≥15 mmHg).Clinical characteristics,procedure-related parameters,adverse events,and outcomes were compared between the two groups to evaluate the safety and efficacy of POEM for patients with normal LES-IRP.RESULTS Among the 481 patients included in our study,209 were males and 272 were females,with a mean age of 44.2 years.All patients underwent POEM without severe adverse events.The median pre-treatment Eckardt scores of the normal LES-IRP and increased LES-IRP groups were 7.0 and 7.0(P=0.132),respectively,decreasing to 1.0 and 1.0 post-treatment(P=0.572).The clinical success rate of the normal LES-IRP group was 87.3%(62/71),and that of the increased LES-IRP group was 91.2%(374/410)(P=0.298).Reflux symptoms were measured by the GerdQ questionnaire,and the percentages of patients with GerdQ scores≥9 in the normal LES-IRP and increased LES-IRP groups were 8.5%and 10.7%,respectively(P=0.711).After matching,the rates of clinical success and the rates of GerdQ score≥9 were not significantly different between the two groups.CONCLUSION Our results suggest that POEM is safe and effective for achalasia and patients with normal LES-IRP.In addition,in patients with normal LES-IRP,compared with those with increased LES-IRP,POEM was not associated with a greater incidence of reflux symptoms.展开更多
Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environ...Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant challenge.To address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration.Specifically,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less efficient.Meanwhile,visual texture information complements LiDAR-Visual localization by providing additional contextual details.By incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of localization.This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.展开更多
Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orien...Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orientation,rendering it inseparable from being merely a derivative of geographical science or technology.This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises.Furthermore,it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering.Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering.Geographical engineering serves as an engine for promoting integrated geography research,and in response to the challenge posed by fragmented geographies,this paper advocates for an urgent revitalization of geographical engineering.The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.展开更多
The integrated optical true time delay phased array antenna system has the advantages of high bandwidth,small size,low loss and strong antiinterference capability,etc.The high integration of the optically controlled p...The integrated optical true time delay phased array antenna system has the advantages of high bandwidth,small size,low loss and strong antiinterference capability,etc.The high integration of the optically controlled phased array antenna system is a necessary trend for the future development of the phased array,and it is also a major focus and difficulty in the current research of integrated microwave photonics.This paper firstly introduces the basic principle and development history of optical true time delay phased array antenna system based on microwave photonics,and briefly introduces the main implementation methods and integration platform of optical true time delay.Then,the application and development prospect of optical true time delay technology in beam control of phased array antenna system are mainly presented.Finally,according to the current research progress,the possible research directions of integrated optically controlled phased array antenna systems in the future are proposed.展开更多
Integrated perovskite-organic solar cells(IPOSCs) offer a promising hybrid approach that combines the advantages of perovskite and organic solar cells, enabling efficient photon absorption across a broad spectrum with...Integrated perovskite-organic solar cells(IPOSCs) offer a promising hybrid approach that combines the advantages of perovskite and organic solar cells, enabling efficient photon absorption across a broad spectrum with a simplified architecture. However, challenges such as limited charge mobility in organic bulk heterojunction(BHJ) layers, and energy-level mismatch at the perovskite/BHJ interface still sustain. Recent advancements in non-fullerene acceptors(NFAs), interfacial engineering, and emerging materials have improved charge transfer/transport, and overall power conversion efficiency(PCE) of IPOSCs.This review explores key developments in IPOSCs, focusing on low-bandgap materials for near-infrared absorption, energy alignment optimization, and strategies to enhance photocurrent density and device performance. Future innovations in material selection and device architecture will be crucial for further improving the efficiency of IPOSCs, bringing them closer to practical application in next-generation photovoltaic technologies.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.展开更多
The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow ...The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.展开更多
To detect space gravitational waves in the extremely low-frequency band,the telescope and optic-al platform require high stability and reliability.However,the cantilevered design presents challenges,espe-cially in the...To detect space gravitational waves in the extremely low-frequency band,the telescope and optic-al platform require high stability and reliability.However,the cantilevered design presents challenges,espe-cially in the glass-metal hetero-bonding process.This study focuses on the analysis and experimental re-search of the bonding layer in the integrated structure.By optimizing the structural configuration and select-ing suitable bonding processes,the reliability of the telescope system is enhanced.The research indicates that using J-133 adhesive achieves the best performance,with a bonding layer thickness of 0.30 mm and a metal substrate surface roughness of Ra 0.8.These findings significantly enhance the reliability of the optical sys-tem while minimizing potential risks.展开更多
In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy sys...In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.展开更多
Artificial Intelligence(AI)has shown the power to enhance the functionality of sensors and enable intelligent human‐machine interfaces through machine learning‐based data analysis.However,the good performance of AI ...Artificial Intelligence(AI)has shown the power to enhance the functionality of sensors and enable intelligent human‐machine interfaces through machine learning‐based data analysis.However,the good performance of AI is always accompanied by a large amount of data and high computational complexity.Though cloud computing appears to be the right solution to this issue with the advent of the 5G era,a certain intelligence of the edge terminal is also important to make the entire integrated intelligent system more efficient.The current development of microelectronic,wearable,AI,and neuromorphic technologies pave the way to realize advanced edge computing by integrating silicon‐based high‐computing‐power neuromorphic chips with anthropomorphic wearable sensory devices and show the potential to achieve human‐like self‐sustainable decentralized intelligence to enable the next‐generation of AI.Hence,in this review,we systematically introduce the related progress in terms of wearable electronics that can mimic the biological features of humans'sensory systems and the development of neuromorphic/in‐sensor computing technologies.Discussion on implementing the integrated human‐like perception and sensation system with silicone‐based computing chips and non‐silicone‐based wearable functional units and our perspectives are also provided.展开更多
基金the support from the National Natural Science Foundation of China(22272004,62272041)the Fundamental Research Funds for the Central Universities(YWF-22-L-1256)+1 种基金the National Key R&D Program of China(2023YFC3402600)the Beijing Institute of Technology Research Fund Program for Young Scholars(No.1870011182126)。
文摘The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.
基金the National Research Foundation(NRF)Singapore mid-sized center grant(NRF-MSG-2023-0002)FrontierCRP grant(NRF-F-CRP-2024-0006)+2 种基金A*STAR Singapore MTC RIE2025 project(M24W1NS005)IAF-PP project(M23M5a0069)Ministry of Education(MOE)Singapore Tier 2 project(MOE-T2EP50220-0014).
文摘The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.
基金supported by the National Natural Science Foundation of China(No.62404111)Natural Science Foundation of Jiangsu Province(No.BK20240635)+2 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.24KJB510025)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY223157 and NY223156)Opening Project of Advanced Inte-grated Circuit Package and Testing Research Center of Jiangsu Province(No.NTIKFJJ202303).
文摘Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.
基金Supported by the Special Fund for Basic Scientific Research of Central-Level Public Welfare Scientific Research Institutes(2024-9007)。
文摘The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.
基金supported by the National Key Research and Development Program of China under Grant(2024YFE0100400)Taishan Scholars Project Special Funds(tsqn202312035)+2 种基金the open research foundation of State Key Laboratory of Integrated Chips and Systems,the Tianjin Science and Technology Plan Project(No.22JCZDJC00630)the Higher Education Institution Science and Technology Research Project of Hebei Province(No.JZX2024024)Jinan City-University Integrated Development Strategy Project under Grant(JNSX2023017).
文摘Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health.However,current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body.Herein,we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring.The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device.The hydrogel as core substrate exhibits strong toughness and water retention,and the multimodal sensing of temperature,pressure,and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference.The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability.Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring.Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network.Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved,which provides a promising approach for sleep monitoring.
文摘Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.
基金STI 2030-Major Projects(2022ZD0208601)National Natural Science Foundation of China(52105593)+2 种基金Zhejiang Provincial Natural Science Foundation of China(LDQ24E050001)‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang(2023C01051)Fundamental Research Funds for the Central Universities(226-2024-00085)。
文摘Integration of sensors with engineering thermoplastics allows to track their health and surrounding stimuli.As one of vital backbones to construct sensor systems,copper(Cu)is highly conductive and cost-effective,yet tends to easily oxidize during and after processing.Herein,an in-situ integrated sensor system on engineering thermoplastics via hybrid laser direct writing is proposed,which primarily consists of laser-passivated functional Cu interconnects and laser-induced carbon-based sensors.Through a one-step photothermal treatment,the resulting functional Cu interconnects after reductive sintering and passivation are capable of resisting long-term oxidation failure at high temperatures(up to 170℃)without additional encapsulations.Interfacing with signal processing units,such an all-in-one system is applied for long-term and real-time temperature monitoring.This integrated sensor system with facile laser manufacturing strategies holds potentials for health monitoring and fault diagnosis of advanced equipment such as aircrafts,automobiles,high-speed trains,and medical devices.
基金supported by the National Research Foundation(NRF)grant funded by the Korean government(MSIT)(RS-2023-00211580,RS-2023-00237308).
文摘Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information,yet conventional signal processing methods struggle with the massive scale,noise,and artificial sensory systems characteristics of data generated by artificial sensory devices.Integrating artificial intelligence(AI)is essential for addressing these challenges and enhancing the performance of artificial sensory systems,making it a rapidly growing area of research in recent years.However,no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods.In this review,we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses:touch,taste,vision,smell,and hearing.We categorize the AI-enabled capabilities of artificial sensory systems into four key areas:cognitive simulation,perceptual enhancement,adaptive adjustment,and early warning.We introduce specialized AI algorithms and raw data processing methods for each function,designed to enhance and optimize sensing performance.Finally,we offer a perspective on the future of AI-integrated artificial sensory systems,highlighting technical challenges and potential real-world application scenarios for further innovation.Integration of AI with artificial sensory systems will enable advanced multimodal perception,real-time learning,and predictive capabilities.This will drive precise environmental adaptation and personalized feedback,ultimately positioning these systems as foundational technologies in smart healthcare,agriculture,and automation.
基金Project supported by the National Natural Science Foundation of China(21801230,21905255)Natural Science Foundation of Shanxi Province(202203021211090)+2 种基金Young Academic Leader Supported Program of North University of China(QX201904)Shanxi Key Laboratory of Advanced Carbon Electrode Materials(202104010910019)The Key Laboratory Research Foundation of North University of China。
文摘By anchoring Tb^(3+)ions on its free carboxyl groups of the nanocaged NiMOF,a dual-emission self-calibrating sensor of Tb^(3+)@NiMOF was fabricated through coordination post-synthetic modification(PSM)strategy.With Tb^(3+)ions as the secondary fluorescent signal and sensing active sites,Tb^(3+)@NiMOF presents great potentials in visually and efficiently monitoring EPI in serum,with high sensitivity and selectivity,fast response,excellent recyclable,and the low detection limit(LOD,3.06 ng/mL).Furthermore,a tandem combinational logic gate based intelligent detection system was constructed to improve the practicability and convenience of epinephrine(EPI)detection in serum by comparing the light emitted colour with the series standard colour cards preset in the smartphone.This work provides a promising approach of developing metal-organic frameworks(MOFs)based self-calibrating sensors for intelligent detection of bioactive molecules.
文摘BACKGROUND Most patients who were included in previous studies on achalasia had increased lower esophageal sphincter(LES)pressure.Peroral endoscopic myotomy(POEM)has been confirmed to be effective at relieving the clinical symptoms of achalasia associated with increased LES pressure.AIM To identify the safety and efficacy of POEM for patients with normal LES integrated relaxation pressure(LES-IRP).METHODS The clinical data of patients who underwent POEM successfully in The First Medical Center of Chinese PLA General Hospital were retrospectively analyzed.A total of 481 patients who underwent preoperative high-resolution manometry(HRM)at our hospital were ultimately included in this research.According to the HRM results,the patients were divided into two groups:71 patients were included in the normal LES-IRP group(LES-IRP<15 mmHg)and 410 patients were included in the increased LES-IRP group(LES-IRP≥15 mmHg).Clinical characteristics,procedure-related parameters,adverse events,and outcomes were compared between the two groups to evaluate the safety and efficacy of POEM for patients with normal LES-IRP.RESULTS Among the 481 patients included in our study,209 were males and 272 were females,with a mean age of 44.2 years.All patients underwent POEM without severe adverse events.The median pre-treatment Eckardt scores of the normal LES-IRP and increased LES-IRP groups were 7.0 and 7.0(P=0.132),respectively,decreasing to 1.0 and 1.0 post-treatment(P=0.572).The clinical success rate of the normal LES-IRP group was 87.3%(62/71),and that of the increased LES-IRP group was 91.2%(374/410)(P=0.298).Reflux symptoms were measured by the GerdQ questionnaire,and the percentages of patients with GerdQ scores≥9 in the normal LES-IRP and increased LES-IRP groups were 8.5%and 10.7%,respectively(P=0.711).After matching,the rates of clinical success and the rates of GerdQ score≥9 were not significantly different between the two groups.CONCLUSION Our results suggest that POEM is safe and effective for achalasia and patients with normal LES-IRP.In addition,in patients with normal LES-IRP,compared with those with increased LES-IRP,POEM was not associated with a greater incidence of reflux symptoms.
基金supported by the project“GEF9874:Strengthening Coordinated Approaches to Reduce Invasive Alien Species(lAS)Threats to Globally Significant Agrobiodiversity and Agroecosystems in China”funding from the Excellent Talent Training Funding Project in Dongcheng District,Beijing,with project number 2024-dchrcpyzz-9.
文摘Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors.These vehicles are crucial in various fields,including environmental science research,ecological and environmental monitoring projects,disaster response,and emergency management.A key method employed in these vehicles for achieving high-precision positioning is LiDAR(lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping(SLAM).However,maintaining highprecision localization in complex scenarios,such as degraded environments or when dynamic objects are present,remains a significant challenge.To address this issue,we integrate both semantic and texture information from LiDAR and cameras to enhance the robustness and efficiency of data registration.Specifically,semantic information simplifies the modeling of scene elements,reducing the reliance on dense point clouds,which can be less efficient.Meanwhile,visual texture information complements LiDAR-Visual localization by providing additional contextual details.By incorporating semantic and texture details frompaired images and point clouds,we significantly improve the quality of data association,thereby increasing the success rate of localization.This approach not only enhances the operational capabilities of ecological monitoring vehicles in complex environments but also contributes to improving the overall efficiency and effectiveness of ecological monitoring and environmental protection efforts.
基金Under the auspices of National Natural Science Foundation of China(No.42293270)。
文摘Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orientation,rendering it inseparable from being merely a derivative of geographical science or technology.This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises.Furthermore,it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering.Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering.Geographical engineering serves as an engine for promoting integrated geography research,and in response to the challenge posed by fragmented geographies,this paper advocates for an urgent revitalization of geographical engineering.The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.
基金supported by Fund of State Key Laboratory of IPOC(BUPT)(No.IPOC2021ZT16),China.
文摘The integrated optical true time delay phased array antenna system has the advantages of high bandwidth,small size,low loss and strong antiinterference capability,etc.The high integration of the optically controlled phased array antenna system is a necessary trend for the future development of the phased array,and it is also a major focus and difficulty in the current research of integrated microwave photonics.This paper firstly introduces the basic principle and development history of optical true time delay phased array antenna system based on microwave photonics,and briefly introduces the main implementation methods and integration platform of optical true time delay.Then,the application and development prospect of optical true time delay technology in beam control of phased array antenna system are mainly presented.Finally,according to the current research progress,the possible research directions of integrated optically controlled phased array antenna systems in the future are proposed.
基金supported by National Natural Science Foundation of China (NSFC) (No. U2001216)Shenzhen Science and Technology Innovation Committee (No. 20231121102401001)the Shenzhen Key Laboratory Project (No. ZDSYS201602261933302)。
文摘Integrated perovskite-organic solar cells(IPOSCs) offer a promising hybrid approach that combines the advantages of perovskite and organic solar cells, enabling efficient photon absorption across a broad spectrum with a simplified architecture. However, challenges such as limited charge mobility in organic bulk heterojunction(BHJ) layers, and energy-level mismatch at the perovskite/BHJ interface still sustain. Recent advancements in non-fullerene acceptors(NFAs), interfacial engineering, and emerging materials have improved charge transfer/transport, and overall power conversion efficiency(PCE) of IPOSCs.This review explores key developments in IPOSCs, focusing on low-bandgap materials for near-infrared absorption, energy alignment optimization, and strategies to enhance photocurrent density and device performance. Future innovations in material selection and device architecture will be crucial for further improving the efficiency of IPOSCs, bringing them closer to practical application in next-generation photovoltaic technologies.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
基金co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
基金supported in part by Natural Science Foundation of Jiangsu Province under Grant BK20230255Natural Science Foundation of Shandong Province under Grant ZR2023QE281.
文摘The multi-terminal direct current(DC)grid has extinctive superiorities over the traditional alternating current system in integrating large-scale renewable energy.Both the DC circuit breaker(DCCB)and the current flow controller(CFC)are demanded to ensure the multiterminal DC grid to operates reliably and flexibly.However,since the CFC and the DCCB are all based on fully controlled semiconductor switches(e.g.,insulated gate bipolar transistor,integrated gate commutated thyristor,etc.),their separation configuration in the multiterminal DC grid will lead to unaffordable implementation costs and conduction power losses.To solve these problems,integrated equipment with both current flow control and fault isolation abilities is proposed,which shares the expensive and duplicated components of CFCs and DCCBs among adjacent lines.In addition,the complicated coordination control of CFCs and DCCBs can be avoided by adopting the integrated equipment in themultiterminal DC grid.In order to examine the current flow control and fault isolation abilities of the integrated equipment,the simulation model of a specific meshed four-terminal DC grid is constructed in the PSCAD/EMTDC software.Finally,the comparison between the integrated equipment and the separate solution is presented a specific result or conclusion needs to be added to the abstract.
文摘To detect space gravitational waves in the extremely low-frequency band,the telescope and optic-al platform require high stability and reliability.However,the cantilevered design presents challenges,espe-cially in the glass-metal hetero-bonding process.This study focuses on the analysis and experimental re-search of the bonding layer in the integrated structure.By optimizing the structural configuration and select-ing suitable bonding processes,the reliability of the telescope system is enhanced.The research indicates that using J-133 adhesive achieves the best performance,with a bonding layer thickness of 0.30 mm and a metal substrate surface roughness of Ra 0.8.These findings significantly enhance the reliability of the optical sys-tem while minimizing potential risks.
基金supported by the Central Government Guides Local Science and Technology Development Fund Project(2023ZY0020)Key R&D and Achievement Transformation Project in InnerMongolia Autonomous Region(2022YFHH0019)+3 种基金the Fundamental Research Funds for Inner Mongolia University of Science&Technology(2022053)Natural Science Foundation of Inner Mongolia(2022LHQN05002)National Natural Science Foundation of China(52067018)Metallurgical Engineering First-Class Discipline Construction Project in Inner Mongolia University of Science and Technology,Control Science and Engineering Quality Improvement and Cultivation Discipline Project in Inner Mongolia University of Science and Technology。
文摘In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.
基金supported by NRF‐CRP28‐2022‐0038“Integrating Wideband Tuneable Acoustic Filters on Silicon for High‐Speed Wireless Communication”(WBS:grant no.A‐8001503‐00‐00)National University of Singapore(NUS),Singapore,and RIE2025 IAF‐ICP under I2301E0027“Piezo Specialty Lab‐in‐Fab 2.0(LiF 2.0)-Enabling Unrivalled Power Efficient Transducers Beyond Material Limits”at National University of Singapore(NUS),Singapore.
文摘Artificial Intelligence(AI)has shown the power to enhance the functionality of sensors and enable intelligent human‐machine interfaces through machine learning‐based data analysis.However,the good performance of AI is always accompanied by a large amount of data and high computational complexity.Though cloud computing appears to be the right solution to this issue with the advent of the 5G era,a certain intelligence of the edge terminal is also important to make the entire integrated intelligent system more efficient.The current development of microelectronic,wearable,AI,and neuromorphic technologies pave the way to realize advanced edge computing by integrating silicon‐based high‐computing‐power neuromorphic chips with anthropomorphic wearable sensory devices and show the potential to achieve human‐like self‐sustainable decentralized intelligence to enable the next‐generation of AI.Hence,in this review,we systematically introduce the related progress in terms of wearable electronics that can mimic the biological features of humans'sensory systems and the development of neuromorphic/in‐sensor computing technologies.Discussion on implementing the integrated human‐like perception and sensation system with silicone‐based computing chips and non‐silicone‐based wearable functional units and our perspectives are also provided.
