Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN mode...Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.展开更多
With the prevalence of renewable distributed energy resources(DERs)such as photovoltaics(PVs),modern active distribution networks(ADNs)suffer from voltage deviation and power quality issues.However,traditional voltage...With the prevalence of renewable distributed energy resources(DERs)such as photovoltaics(PVs),modern active distribution networks(ADNs)suffer from voltage deviation and power quality issues.However,traditional voltage control methods often face a trade-off between efficiency and effectiveness,and rarely ensure robust voltage safety under typical state perturbations in practical distribution grids.In this paper,a robust model-free voltage regulation approach is proposed which simultaneously takes security and robustness into account.In this context,the voltage control problem is formulated as a constrained Markov decision process(CMDP).A safety-augmented multiagent deep deterministic policy gradient(MADDPG)algorithm is the trained to enable real-time collaborative optimization of ADNs,aiming to maintain nodal voltages within safe operational limits while minimizing total line losses.Moreover,a robust regulation loss is introduced to ensure reliable performance under various state perturbations in practical voltage controls.The proposed regulation algorithm effectively balance efficiency,safety,and robustness,and also demonstrates potential for generalizing these characteristics to other applications.Numerical studies vali-date the robustness of the proposed method under varying state perturbations on the IEEE test cases and the optimal integrated control performance when compared to other benchmarks.展开更多
Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interfere...Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interference immunity,acoustic stealth detection,and low cost,its short range restricts applicability.A target perturbation model under differential signal acquisition reveals that signal strength increases with local electric field intensity,target size,differential channel spacing,and conductivity contrast,but decreases with target-electrode distance.To extend detection,novel array configurations were explored.Simulations demonstrate that both rectangular and offset arrays significantly outperform the traditional collinear layout.Specifically,an offset array(with 8 m transmitting–receiving spacing)achieved an effective detection range enhancement exceeding 83%under the same distortion threshold while maintaining simplified electrode structure.Experimental validation confirmed a 100%increase in maximum detection distance to 5 m under identical noise thresholds compared to the collinear array.Furthermore,a fully connected neural network-based localization model achieved a mean positioning error of 14.12 cm at 3.15 m in static scenarios.In dynamic scenarios within 1–3 m,mean errors were controlled between 13.19 cm and 27.56 cm.Mechanistic analysis indicates that increasing the array baseline enhances the signal-to-noise ratio by simultaneously suppressing near-field environmental noise and amplifying far-field signal reception.Structural innovations in array design enabled this study to significantly expand the detection range of AES systems without compromising cost efficiency.These advancements directly promote the engineering application of AES technology,offering critical technical support for underwater defense security monitoring,long-range early warning systems,and maritime rights protection.展开更多
Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energ...Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.展开更多
Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-relate...Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-related diseases like Alzheimer’s disease,osteoporosis,low immunity,glucose and lipid metabolism disorders,and cardiovascular diseases.With the continuous increase of the elderly population,the pressure on the medical industry is increasing.To lower the burden on the medical industry and increase the average age of the elderly,it is vital to explore effective anti-aging materials.Ginseng Radix et Rhizoma(Renshen),as a traditional and precious Chinese medicinal herb,is known as the“king of all herbs”.It is famous for its effects of“tonifying Qi,restoring pulse”(helping with the generation of Qi(the fundamental,vital energy that continuously flows within the body)and the circulation of blood)and strengthening the body,nourishing the spleen and lungs,generating fluids and nourishing blood,calming the mind and improving intelligence.Recently,its anti-aging effect has received increasing attention from modern scientific research.This study summarizes the pharmacological effects of the main active ingredients of Renshen(ginsenosides,polysaccharides,etc.)on resisting aging,including preventing neuroaging,suppressing skin aging,mitigating ovarian aging,inhibiting osteoporosis and arthritis,enhancing the immune system of the elderly,protecting the cardiovascular system,resisting aging-induced fatigue and exerting the anti-tumor effects.Through network pharmacology and molecular docking,the anti-aging active ingredients of Renshen were screened,and the key targets and pathways of anti-aging active ingredients in Renshen were determined.Using network pharmacology,totally 106 drug targets and 3,479 disease targets were screened,and 79 common targets between aging and Renshen were identified.Three core targets were identified in the PPI network,including TNF,AKT1,and IL-1β.Molecular docking was used to obtain further verification.This study emphasizes the potential of Renshen as a source of anti-aging activity,which can be developed into a novel drug for the treatment of age-related diseases.展开更多
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
This study proposes a method for analyzing the security distance of an Active Distribution Network(ADN)by incorporating the demand response of an Energy Hub(EH).Taking into account the impact of stochastic wind-solar ...This study proposes a method for analyzing the security distance of an Active Distribution Network(ADN)by incorporating the demand response of an Energy Hub(EH).Taking into account the impact of stochastic wind-solar power and flexible loads on the EH,an interactive power model was developed to represent the EH’s operation under these influences.Additionally,an ADN security distance model,integrating an EH with flexible loads,was constructed to evaluate the effect of flexible load variations on the ADN’s security distance.By considering scenarios such as air conditioning(AC)load reduction and base station(BS)load transfer,the security distances of phases A,B,and C increased by 17.1%,17.2%,and 17.7%,respectively.Furthermore,a multi-objective optimal power flow model was formulated and solved using the Forward-Backward Power Flow Algorithm,the NSGA-II multi-objective optimization algo-rithm,and the maximum satisfaction method.The simulation results of the IEEE33 node system example demonstrate that after opti-mization,the total energy cost for one day is reduced by 0.026%,and the total security distance limit of the ADN’s three phases is improved by 0.1 MVA.This method effectively enhances the security distance,facilitates BS load transfer and AC load reduction,and contributes to the energy-saving,economical,and safe operation of the power system.展开更多
This paper provides a systematic review on the resilience analysis of active distribution networks(ADNs)against hazardous weather events,considering the underlying cyber-physical interdependencies.As cyber-physical sy...This paper provides a systematic review on the resilience analysis of active distribution networks(ADNs)against hazardous weather events,considering the underlying cyber-physical interdependencies.As cyber-physical systems,ADNs are characterized by widespread structural and functional interdependen-cies between cyber(communication,computing,and control)and physical(electric power)subsystems and thus present complex hazardous-weather-related resilience issues.To bridge current research gaps,this paper first classifies diverse hazardous weather events for ADNs according to different time spans and degrees of hazard,with model-based and data-driven methods being utilized to characterize weather evolutions.Then,the adverse impacts of hazardous weather on all aspects of ADNs’sources,physical/cyber networks,and loads are analyzed.This paper further emphasizes the importance of situational awareness and cyber-physical collaboration throughout hazardous weather events,as these enhance the implementation of preventive dispatches,corrective actions,and coordinated restorations.In addition,a generalized quantitative resilience evaluation process is proposed regarding additional considerations about cyber subsystems and cyber-physical connections.Finally,potential hazardous-weather-related resilience challenges for both physical and cyber subsystems are discussed.展开更多
Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing o...Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.展开更多
Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two tim...Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two time-division based distributed sensor scheduling schemes are proposed to deal with ISI by scheduling sensors periodically and adaptively respectively. Extended Kalman filter (EKF) is used as the tracking algorithm in distributed manner. Simulation results show that the adaptive sensor scheduling scheme can achieve superior tracking accuracy with faster tracking convergence speed.展开更多
The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This pap...The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This paper develops a vibration isolation system of micro-manufacturing platform. The brains of many kinds of birds can isolate vibrations well, such as woodpecker’s brain. When a woodpecker pecks the wood at the speed as 1.6 times as the velocity of sound, its brain will tolerate the wallop 1 500 times of the weight of itself without any damage. The isolation mechanics and organic texture of woodpecker’s brain that has good isolation characteristics were studied. A structure model of vibration isolation system for the micro-manufacturing platform is established based on the bionics of the bird’s brain vibration isolation mechanism. In order to isolate effectively the high frequency vibrations from the ground, a rubber layer is used to isolate vibrations passively between the micro-manufacturing platform’s pedestal and the ground. This layer corresponds to the cartilage and muscles in the outer meninges of the bird’s brain. The active vibration isolation technique is adopted to isolate vibrations between the micro-manufacturing platform and the pedestal. Air springs are used as elastic components, which correspond to the interspaces between the outer meninges and the encephala of the bird’s brain. Actuators are made of giant magnetostrictive material, and it corresponds to the nerves and neural muscles linking the meninges and the encephala. The actuators and air springs are arranged vertically in parallel to make use of the giant magnetostrictive actuators effectively. The air springs support almost all weight of the micro-manufacturing platform and the giant magnetostrictive actuators support almost no weight. In order to realize high performance to isolate complex micro-vibration, the control method using a three-layer neural network is presented. This vibration control system takes into account the floor disturbance and the direct disturbance acting on the micro-manufacturing platform. The absolute acceleration of the micro-manufacturing platform is used as the performance index of vibration control. The performance of the control system is tested by numerical simulation. Simulation results show that the active vibration isolation system has good isolation performance against the floor disturbance and the direct disturbance acting on the micro-manufacturing platform in all the frequency range.展开更多
Albiziae Flos(AF)has been experimentally proven to have an antidepressant effect.However,due to the complexity of botanical ingredients,the exact pharmacological mechanism of action of AF in depression has not been co...Albiziae Flos(AF)has been experimentally proven to have an antidepressant effect.However,due to the complexity of botanical ingredients,the exact pharmacological mechanism of action of AF in depression has not been completely deciphered.This study used the network pharmacology method to construct a component-target-pathway network to explore the active components and potential mechanisms of action of AF.The methods included collection and screening of chemical components,prediction of depression-associated targets of the active components,gene enrichment,and network construction and analysis.Quercetin and 4 other active components were found to exert an tidepressant effects mainly via monoaminergic neurotransmitters and cAMP signaling and neuroactive ligand・wceptor interaction pathways.DRD2,HTR1 A,and SLC6A4 were identified as important targets of the studied bioactive components of AF.This network pharmacology analysis provides guidance for further study of the antidepressant mechanism of AF.展开更多
Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid ...Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid of curvularin and purine.The structures and absolute configurations of compounds 1-9 were elucidated via nuclear magnetic resonance(NMR)spectroscopy,X-ray diffraction,electronic circular dichroism(ECD)calculations,^(13)C NMR calculation,modified Mosher's method,and chemical derivatization.Investigation of anti-inflammatory activities revealed that compounds 7-9,11,12,14,15,and 18 exhibited significant suppressive effects against lipopolysaccharide(LPS)-induced nitric oxide(NO)production in murine macrophage RAW264.7 cells,with IC_(50)values ranging from 0.44 to 4.40μmol·L^(-1).Furthermore,these bioactive compounds were found to suppress the expression of inflammation-related proteins,including inducible NO synthase(i NOS),cyclooxygenase-2(COX-2),NLR family pyrin domain-containing protein 3(NLRP3),and nuclear factor kappa-B(NF-κB).Additional studies demonstrated that the novel compound 7 possessed potent antiinflammatory activity by inhibiting the transcription of inflammation-related genes,downregulating the expression of inflammation-related proteins,and inhibiting the release of inflammatory cytokines,indicating its potential application in the treatment of inflammatory diseases.展开更多
The problem of guaranteed cost active fault-tolerant controller （AFTC） design for networked control systems （NCSs） with both packet dropout and transmission delay is studied in this paper. Considering the packet d...The problem of guaranteed cost active fault-tolerant controller （AFTC） design for networked control systems （NCSs） with both packet dropout and transmission delay is studied in this paper. Considering the packet dropout and transmission delay, a piecewise constant controller is adopted. With a guaranteed cost function, optimal controllers whose number is equal to the number of actuators are designed, and the design process is formulated as a convex optimal problem that can be solved by existing software. The control strategy is proposed as follows： when actuator failures appear, the fault detection and isolation unit sends out the information to the controller choosing strategy, and then the optimal stabilizing controller with the smallest guaranteed cost value is chosen. Two illustrative examples are given to demonstrate the effectiveness of the proposed approach. By comparing with the existing methods, it can be seen that our method has a better performance.展开更多
A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i...A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.展开更多
Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its ave...Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the pre- dominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theo- retical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.展开更多
An algorithm of traffic distribution called active multi-path routing(AMR)in active network is proposed.AMR adopts multi-path routing and applies nonlinear optimizeapproximate method to distribute network traffic amon...An algorithm of traffic distribution called active multi-path routing(AMR)in active network is proposed.AMR adopts multi-path routing and applies nonlinear optimizeapproximate method to distribute network traffic among multiple paths.It is combined to bandwidthresource allocation and the congestion restraint mechanism to avoid congestion happening and worsen.So network performance can be improved greatly.The frame of AMR includes adaptive trafficallocation model,the conception of supply bandwidth and its'allocation model,the principle ofcongestion restraint and its'model,and the implement of AMR based on multi-agents system in activenetwork.Through simulations,AMR has distinct effects on network performance.The results show AMRisa valid traffic regulation algorithm.展开更多
This paper presents a data processing strategy for GPS kinematic positioning by using a GPS active network to model the GPS errors in double difference observable.Firstly,the double difference residuals are estimated ...This paper presents a data processing strategy for GPS kinematic positioning by using a GPS active network to model the GPS errors in double difference observable.Firstly,the double difference residuals are estimated between the reference stations in the active network.Then the errors at a user station are predicted as the network corrections to user measurements,based on the location of the user.Finally conventional kinematic positioning algorithms can be applied to determine the position of the user station.As an example,continuous 24_hour GPS data in March 2001 has been processed by this method.It clearly demonstrates that,after applying these corrections to a user within the network,both the success rate for ambiguity resolution and the positioning accuracy have been significantly improved.展开更多
基金supported by the National Science and Technology Major Project(2022ZD0119901)the National Natural Science Foundation of China under Grant(U2141234,62463004 and U24A20260)+1 种基金the Hainan Province Science and Technology Special Fund(ZDYF2024GXJS003)the Scientific Research Fund of Hainan University(KYQD(ZR)23025).
文摘Recently,the zeroing neural network(ZNN)has demonstrated remarkable effectiveness in tackling time-varying problems,delivering robust performance across both noise-free and noisy environments.However,existing ZNN models are limited in their ability to actively suppress noise,which constrains their robustness and precision in solving time-varying problems.This paper introduces a novel active noise rejection ZNN(ANR-ZNN)design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour.Through rigorous theoretical analysis,we demonstrate that the proposed ANR-ZNN maintains robust convergence in computational error performance under environmental noise.As a case study,the ANR-ZNN model is specifically applied to time-varying matrix inversion.Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness,emphasising the proposed design's superiority and potential for solving time-varying problems.
基金supported in part by the National Natural Science Foundation of China(No.52177109)Key R&D Program of Hubei Province,China(No.2020BAB109).
文摘With the prevalence of renewable distributed energy resources(DERs)such as photovoltaics(PVs),modern active distribution networks(ADNs)suffer from voltage deviation and power quality issues.However,traditional voltage control methods often face a trade-off between efficiency and effectiveness,and rarely ensure robust voltage safety under typical state perturbations in practical distribution grids.In this paper,a robust model-free voltage regulation approach is proposed which simultaneously takes security and robustness into account.In this context,the voltage control problem is formulated as a constrained Markov decision process(CMDP).A safety-augmented multiagent deep deterministic policy gradient(MADDPG)algorithm is the trained to enable real-time collaborative optimization of ADNs,aiming to maintain nodal voltages within safe operational limits while minimizing total line losses.Moreover,a robust regulation loss is introduced to ensure reliable performance under various state perturbations in practical voltage controls.The proposed regulation algorithm effectively balance efficiency,safety,and robustness,and also demonstrates potential for generalizing these characteristics to other applications.Numerical studies vali-date the robustness of the proposed method under varying state perturbations on the IEEE test cases and the optimal integrated control performance when compared to other benchmarks.
基金supported in part by National Natural Science Foundation of China(Grant No.62273075).
文摘Addressing the critical detection range limitation in active electrosensing(AES)for underwater sensing,this study proposes an enhanced AES system via novel array optimization.While AES offers advantages like interference immunity,acoustic stealth detection,and low cost,its short range restricts applicability.A target perturbation model under differential signal acquisition reveals that signal strength increases with local electric field intensity,target size,differential channel spacing,and conductivity contrast,but decreases with target-electrode distance.To extend detection,novel array configurations were explored.Simulations demonstrate that both rectangular and offset arrays significantly outperform the traditional collinear layout.Specifically,an offset array(with 8 m transmitting–receiving spacing)achieved an effective detection range enhancement exceeding 83%under the same distortion threshold while maintaining simplified electrode structure.Experimental validation confirmed a 100%increase in maximum detection distance to 5 m under identical noise thresholds compared to the collinear array.Furthermore,a fully connected neural network-based localization model achieved a mean positioning error of 14.12 cm at 3.15 m in static scenarios.In dynamic scenarios within 1–3 m,mean errors were controlled between 13.19 cm and 27.56 cm.Mechanistic analysis indicates that increasing the array baseline enhances the signal-to-noise ratio by simultaneously suppressing near-field environmental noise and amplifying far-field signal reception.Structural innovations in array design enabled this study to significantly expand the detection range of AES systems without compromising cost efficiency.These advancements directly promote the engineering application of AES technology,offering critical technical support for underwater defense security monitoring,long-range early warning systems,and maritime rights protection.
基金supported by Integrated Distribution Network Planning and Operational Enhancement Using Flexibility Domains Under Deep Human-Vehicle-Charger-Road-Grid Coupling(U22B20105).
文摘Thermal storage electric heating(TSEH),as a prevalent variable load resource,offers significant potential for enhancing system flexibility when aggregated into a cluster.To address the uncertainties of renewable energy and load forecasting in active distribution networks(ADN),this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters.It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH,along with the rapid and gradual response characteristics of resources in active distribution grids,to develop a coordinated optimization dispatch mechanism for day-ahead,intraday,and real-time stages.It provides a coordinated optimized dispatch technique across several timescales for active distribution grids,taking into account the integration of TSEH clusters.The proposed method is validated on a modified IEEE 33-node system.Simulation results demonstrate that the participation of TSEH in collaborative optimization significantly reduces the total system operating cost by 8.71%compared to the scenario without TSEH.This cost reduction is attributed to a 10.84%decrease in interaction costs with the main grid and a 47.41%reduction in network loss costs,validating effective peak shaving and valley filling.The multi-timescale framework further enhances economic efficiency,with overall operating costs progressively decreasing by 3.91%(intraday)and 4.59%(real-time),and interaction costs further reduced by 5.34%and 9.25%,respectively.Moreover,the approach enhances system stability by effectively suppressing node voltage fluctuations and ensuring all voltages remain within safe operating limits during real-time operation.Therefore,the proposed approach achieves rational coordination of diverse resources,significantly improving the economic efficiency and stability of ADNs.
基金supported by the Jilin Science and Technology Development Talent Special Project,Nos.20240601086RC,23JQ08(all to ZH)YDZJ202502CXJD077+1 种基金JLARS-2025-0802-09YDZJ202501ZYTS706.
文摘Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-related diseases like Alzheimer’s disease,osteoporosis,low immunity,glucose and lipid metabolism disorders,and cardiovascular diseases.With the continuous increase of the elderly population,the pressure on the medical industry is increasing.To lower the burden on the medical industry and increase the average age of the elderly,it is vital to explore effective anti-aging materials.Ginseng Radix et Rhizoma(Renshen),as a traditional and precious Chinese medicinal herb,is known as the“king of all herbs”.It is famous for its effects of“tonifying Qi,restoring pulse”(helping with the generation of Qi(the fundamental,vital energy that continuously flows within the body)and the circulation of blood)and strengthening the body,nourishing the spleen and lungs,generating fluids and nourishing blood,calming the mind and improving intelligence.Recently,its anti-aging effect has received increasing attention from modern scientific research.This study summarizes the pharmacological effects of the main active ingredients of Renshen(ginsenosides,polysaccharides,etc.)on resisting aging,including preventing neuroaging,suppressing skin aging,mitigating ovarian aging,inhibiting osteoporosis and arthritis,enhancing the immune system of the elderly,protecting the cardiovascular system,resisting aging-induced fatigue and exerting the anti-tumor effects.Through network pharmacology and molecular docking,the anti-aging active ingredients of Renshen were screened,and the key targets and pathways of anti-aging active ingredients in Renshen were determined.Using network pharmacology,totally 106 drug targets and 3,479 disease targets were screened,and 79 common targets between aging and Renshen were identified.Three core targets were identified in the PPI network,including TNF,AKT1,and IL-1β.Molecular docking was used to obtain further verification.This study emphasizes the potential of Renshen as a source of anti-aging activity,which can be developed into a novel drug for the treatment of age-related diseases.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
基金supported in part by the National Nat-ural Science Foundation of China(No.51977012,No.52307080).
文摘This study proposes a method for analyzing the security distance of an Active Distribution Network(ADN)by incorporating the demand response of an Energy Hub(EH).Taking into account the impact of stochastic wind-solar power and flexible loads on the EH,an interactive power model was developed to represent the EH’s operation under these influences.Additionally,an ADN security distance model,integrating an EH with flexible loads,was constructed to evaluate the effect of flexible load variations on the ADN’s security distance.By considering scenarios such as air conditioning(AC)load reduction and base station(BS)load transfer,the security distances of phases A,B,and C increased by 17.1%,17.2%,and 17.7%,respectively.Furthermore,a multi-objective optimal power flow model was formulated and solved using the Forward-Backward Power Flow Algorithm,the NSGA-II multi-objective optimization algo-rithm,and the maximum satisfaction method.The simulation results of the IEEE33 node system example demonstrate that after opti-mization,the total energy cost for one day is reduced by 0.026%,and the total security distance limit of the ADN’s three phases is improved by 0.1 MVA.This method effectively enhances the security distance,facilitates BS load transfer and AC load reduction,and contributes to the energy-saving,economical,and safe operation of the power system.
基金supported by the National Natural Science Foundation of China(52477132 and U2066601).
文摘This paper provides a systematic review on the resilience analysis of active distribution networks(ADNs)against hazardous weather events,considering the underlying cyber-physical interdependencies.As cyber-physical systems,ADNs are characterized by widespread structural and functional interdependen-cies between cyber(communication,computing,and control)and physical(electric power)subsystems and thus present complex hazardous-weather-related resilience issues.To bridge current research gaps,this paper first classifies diverse hazardous weather events for ADNs according to different time spans and degrees of hazard,with model-based and data-driven methods being utilized to characterize weather evolutions.Then,the adverse impacts of hazardous weather on all aspects of ADNs’sources,physical/cyber networks,and loads are analyzed.This paper further emphasizes the importance of situational awareness and cyber-physical collaboration throughout hazardous weather events,as these enhance the implementation of preventive dispatches,corrective actions,and coordinated restorations.In addition,a generalized quantitative resilience evaluation process is proposed regarding additional considerations about cyber subsystems and cyber-physical connections.Finally,potential hazardous-weather-related resilience challenges for both physical and cyber subsystems are discussed.
基金supported in part by the National Natural Science Foundation of China under Grant 52307134the Fundamental Research Funds for the Central Universities(xzy012025022)。
文摘Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.
基金Supported by Science & Engineering Research Council of Singnpore (0521010037)
文摘Wireless sensor network (WSN) of active sensors suffers from serious inter-sensor interference (ISI) and imposes new design and implementation challenges. In this paper, based on the ultrasonic sensor network, two time-division based distributed sensor scheduling schemes are proposed to deal with ISI by scheduling sensors periodically and adaptively respectively. Extended Kalman filter (EKF) is used as the tracking algorithm in distributed manner. Simulation results show that the adaptive sensor scheduling scheme can achieve superior tracking accuracy with faster tracking convergence speed.
文摘The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This paper develops a vibration isolation system of micro-manufacturing platform. The brains of many kinds of birds can isolate vibrations well, such as woodpecker’s brain. When a woodpecker pecks the wood at the speed as 1.6 times as the velocity of sound, its brain will tolerate the wallop 1 500 times of the weight of itself without any damage. The isolation mechanics and organic texture of woodpecker’s brain that has good isolation characteristics were studied. A structure model of vibration isolation system for the micro-manufacturing platform is established based on the bionics of the bird’s brain vibration isolation mechanism. In order to isolate effectively the high frequency vibrations from the ground, a rubber layer is used to isolate vibrations passively between the micro-manufacturing platform’s pedestal and the ground. This layer corresponds to the cartilage and muscles in the outer meninges of the bird’s brain. The active vibration isolation technique is adopted to isolate vibrations between the micro-manufacturing platform and the pedestal. Air springs are used as elastic components, which correspond to the interspaces between the outer meninges and the encephala of the bird’s brain. Actuators are made of giant magnetostrictive material, and it corresponds to the nerves and neural muscles linking the meninges and the encephala. The actuators and air springs are arranged vertically in parallel to make use of the giant magnetostrictive actuators effectively. The air springs support almost all weight of the micro-manufacturing platform and the giant magnetostrictive actuators support almost no weight. In order to realize high performance to isolate complex micro-vibration, the control method using a three-layer neural network is presented. This vibration control system takes into account the floor disturbance and the direct disturbance acting on the micro-manufacturing platform. The absolute acceleration of the micro-manufacturing platform is used as the performance index of vibration control. The performance of the control system is tested by numerical simulation. Simulation results show that the active vibration isolation system has good isolation performance against the floor disturbance and the direct disturbance acting on the micro-manufacturing platform in all the frequency range.
基金the National Natural Science Foundation of China(No.31570343).
文摘Albiziae Flos(AF)has been experimentally proven to have an antidepressant effect.However,due to the complexity of botanical ingredients,the exact pharmacological mechanism of action of AF in depression has not been completely deciphered.This study used the network pharmacology method to construct a component-target-pathway network to explore the active components and potential mechanisms of action of AF.The methods included collection and screening of chemical components,prediction of depression-associated targets of the active components,gene enrichment,and network construction and analysis.Quercetin and 4 other active components were found to exert an tidepressant effects mainly via monoaminergic neurotransmitters and cAMP signaling and neuroactive ligand・wceptor interaction pathways.DRD2,HTR1 A,and SLC6A4 were identified as important targets of the studied bioactive components of AF.This network pharmacology analysis provides guidance for further study of the antidepressant mechanism of AF.
基金funded by the National Key Research and Development Program of China(No.2022YFC2804101)the Guangdong Provincial Key R&D Program(No.2023B1111050011)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515010432)the Guangzhou Basic and Applied Basic Research Foundation(No.202201010305)the High-Level Talents Special Program of Zhejiang(No.2022R52036)。
文摘Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid of curvularin and purine.The structures and absolute configurations of compounds 1-9 were elucidated via nuclear magnetic resonance(NMR)spectroscopy,X-ray diffraction,electronic circular dichroism(ECD)calculations,^(13)C NMR calculation,modified Mosher's method,and chemical derivatization.Investigation of anti-inflammatory activities revealed that compounds 7-9,11,12,14,15,and 18 exhibited significant suppressive effects against lipopolysaccharide(LPS)-induced nitric oxide(NO)production in murine macrophage RAW264.7 cells,with IC_(50)values ranging from 0.44 to 4.40μmol·L^(-1).Furthermore,these bioactive compounds were found to suppress the expression of inflammation-related proteins,including inducible NO synthase(i NOS),cyclooxygenase-2(COX-2),NLR family pyrin domain-containing protein 3(NLRP3),and nuclear factor kappa-B(NF-κB).Additional studies demonstrated that the novel compound 7 possessed potent antiinflammatory activity by inhibiting the transcription of inflammation-related genes,downregulating the expression of inflammation-related proteins,and inhibiting the release of inflammatory cytokines,indicating its potential application in the treatment of inflammatory diseases.
基金supported by National Outstanding Youth Foundation (No. 60525303)National Natural Science Foundation of China(No. 60704009)+1 种基金Key Project for Natural Science Research of Hebei Education Department (No. ZD200908)the Doctor Fund of YanShan University (No. B203)
文摘The problem of guaranteed cost active fault-tolerant controller （AFTC） design for networked control systems （NCSs） with both packet dropout and transmission delay is studied in this paper. Considering the packet dropout and transmission delay, a piecewise constant controller is adopted. With a guaranteed cost function, optimal controllers whose number is equal to the number of actuators are designed, and the design process is formulated as a convex optimal problem that can be solved by existing software. The control strategy is proposed as follows： when actuator failures appear, the fault detection and isolation unit sends out the information to the controller choosing strategy, and then the optimal stabilizing controller with the smallest guaranteed cost value is chosen. Two illustrative examples are given to demonstrate the effectiveness of the proposed approach. By comparing with the existing methods, it can be seen that our method has a better performance.
文摘A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.
基金Supported by the National High Technology Research and Development of China (863 Program) (No.2003AA121560)the High Technology Research and Development Program of Jiangsu Province (No.BEG2003001).
文摘Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the pre- dominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theo- retical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.
基金Supported by the National Natural Science Foun dation of China(90204008)
文摘An algorithm of traffic distribution called active multi-path routing(AMR)in active network is proposed.AMR adopts multi-path routing and applies nonlinear optimizeapproximate method to distribute network traffic among multiple paths.It is combined to bandwidthresource allocation and the congestion restraint mechanism to avoid congestion happening and worsen.So network performance can be improved greatly.The frame of AMR includes adaptive trafficallocation model,the conception of supply bandwidth and its'allocation model,the principle ofcongestion restraint and its'model,and the implement of AMR based on multi-agents system in activenetwork.Through simulations,AMR has distinct effects on network performance.The results show AMRisa valid traffic regulation algorithm.
文摘This paper presents a data processing strategy for GPS kinematic positioning by using a GPS active network to model the GPS errors in double difference observable.Firstly,the double difference residuals are estimated between the reference stations in the active network.Then the errors at a user station are predicted as the network corrections to user measurements,based on the location of the user.Finally conventional kinematic positioning algorithms can be applied to determine the position of the user station.As an example,continuous 24_hour GPS data in March 2001 has been processed by this method.It clearly demonstrates that,after applying these corrections to a user within the network,both the success rate for ambiguity resolution and the positioning accuracy have been significantly improved.
