We propose a new image encryption algorithm on the basis of the fractional-order hyperchaotic Lorenz system. While in the process of generating a key stream, the system parameters and the derivative order are embedded...We propose a new image encryption algorithm on the basis of the fractional-order hyperchaotic Lorenz system. While in the process of generating a key stream, the system parameters and the derivative order are embedded in the proposed algorithm to enhance the security. Such an algorithm is detailed in terms of security analyses, including correlation analysis, information entropy analysis, run statistic analysis, mean-variance gray value analysis, and key sensitivity analysis. The experimental results demonstrate that the proposed image encryption scheme has the advantages of large key space and high security for practical image encryption.展开更多
This article proposes a linear parameter varying （LPV） switching tracking control scheme for a flexible air-breathing hypersonic vehicle （FAHV）. First, a polytopic LPV model is constructed to represent the complex...This article proposes a linear parameter varying （LPV） switching tracking control scheme for a flexible air-breathing hypersonic vehicle （FAHV）. First, a polytopic LPV model is constructed to represent the complex nonlinear longitudinal model of the FAHV by using Jacobian linearization and tensor-product （T-P） model transformation approach. Second, for less conservative controller design purpose, the flight envelope is divided into four sub-regions and a non-fragile LPV controller is designed for each parameter sub-region. These non-fragile LPV controllers are then switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a specified performance criterion. The desired non-fragile LPV switching controller is found by solving a convex constraint problem which can be efficiently solved using available linear matrix inequality （LMI） techniques, and robust stability analysis of the closed-loop FAHV system is verified based on multiple Lypapunov functions （MLFs）. Finally, numerical simulations have demonstrated the effectiveness of the proposed approach.展开更多
The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment innovation.Insulating material,as the core of electrical power equipm...The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment innovation.Insulating material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and opportunities.The goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first discussed.The engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are investigated.Insulating materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being discussed.In the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material level.It is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality rules.The authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.展开更多
The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers.To address this issue,an incentive-based demand response(DR)model involving the utility and elastici...The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers.To address this issue,an incentive-based demand response(DR)model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers.The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions.The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price.Then,the sensitivity of incentive price on retailers’benefits is analyzed and the optimal incentive price is obtained according to the DR model.The case study verifies the effectiveness of the proposed model.展开更多
The incorporation of hydroxyapatite(HAP)into poly-L-lactic acid(PLLA)matrix serving as bone scaffold is expected to exhibit bioactivity and osteoconductivity to those of the living bone.While too low degradation rate ...The incorporation of hydroxyapatite(HAP)into poly-L-lactic acid(PLLA)matrix serving as bone scaffold is expected to exhibit bioactivity and osteoconductivity to those of the living bone.While too low degradation rate of HAP/PLLA scaffold hinders the activity because the embedded HAP in the PLLA matrix is difficult to contact and exchange ions with body fluid.In this study,biodegradable polymer poly(glycolic acid)(PGA)was blended into the HAP/PLLA scaffold fabricated by laser 3D printing to accelerate the degradation.The results indicated that the incorporation of PGA enhanced the degradation rate of scaffold as indicated by the weight loss increasing from 3.3%to 25.0%after immersion for 28 days,owing to the degradation of high hydrophilic PGA and the subsequent accelerated hydrolysis of PLLA chains.Moreover,a lot of pores produced by the degradation of the scaffold promoted the exposure of HAP from the matrix,which not only activated the deposition of bone like apatite on scaffold but also accelerated apatite growth.Cytocompatibility tests exhibited a good osteoblast adhesion,spreading and proliferation,suggesting the scaffold provided a suitable environment for cell cultivation.Furthermore,the scaffold displayed excellent bone defect repair capacity with the formation of abundant new bone tissue and blood vessel tissue,and both ends of defect region were bridged after 8 weeks of implantation.展开更多
Electric and chemical synapse play important role in connecting neurons and thus signal propagation can be realized between neurons. External electric stimulus can change the excitability of neuron and then the electr...Electric and chemical synapse play important role in connecting neurons and thus signal propagation can be realized between neurons. External electric stimulus can change the excitability of neuron and then the electrical activities can be modulated completely. Continuous fluctuation of ion concentration in cell can induce complex time-varying electromagnetic field during the exchange of charged ions across the membrane of neuron. Polarization and magnetization in the media(and neuron), which exposed to electromagnetic radiation, can modulate the dynamical response and mode transition in electrical activities of neurons. In this paper, magnetic flux is used to describe the effect of electromagnetic field, and the three-variable HindmarshRose neuron model is updated to propose a four-variable neuron model that the effect of electromagnetic induction and radiation can be explained. Based on the physical law of electromagnetic induction, exchange of charged ions and flow of ion currents will change the distribution of electromagnetic filed in cell, and each neuron will be exposed to the superimposed field triggered by other neurons. Therefore, signal exchange could occur even synapse coupling between neurons is removed in the case of field coupling. A chain network is proposed to investigate the signal exchange between neurons under field coupling when synapse coupling is not available. It is found that field coupling between neurons can change the collective behaviors in electrical activities. A statistical factor of synchronization and spatial patterns are calculated, these results confirmed that field coupling is effective for signal communication between neurons. In the end, open problems are suggested for readers' extensive guidance in this field.展开更多
Ingestive-related behaviors including feeding and ruminating are important indexes to measure the health and welfare of dairy cows.The purpose of this study is to develop a method based on triaxial acceleration to aut...Ingestive-related behaviors including feeding and ruminating are important indexes to measure the health and welfare of dairy cows.The purpose of this study is to develop a method based on triaxial acceleration to automatically recognize feeding and ruminating of dairy cows.During the experiment,five diary cows raised in a barn were used as experimental subjects.A triaxial acceleration sensor was used as the device to collect jawmovement data of dairy cows,and the behaviors of dairy cows were classified into three categories:feeding,ruminating and other behavior.The features of time-domain and frequency-domain were extracted from the raw acceleration data.Three machine learning algorithms including k-nearest neighbor,support vector machine and probabilistic neural network were used for the classification and the results based on four different data segment lengths were compared.The results show that the three algorithms can be used for recognition of feeding and ruminating with high accuracy.Under the condition that the sampling frequency of the sensor is 5 Hz,the combination of data segment length of 256 and k-nearest neighbor algorithm is the best scheme for recognition of feeding and ruminating in this study.The precision and recall of recognition for feeding were 92.8%and 95.6%respectively,and those of recognition for ruminating were 93.7%and 94.3%respectively.The specificity and AUC of recognition for feeding were 96.1%and 0.959 respectively,and those of recognition for ruminating were 97.5%and 0.959 respectively.This will provide an effective method for real-time monitoring of ingestive-related behaviors of dairy cows and lay a foundation for prediction of dairy cows’health status and welfare to further achieve the purpose of disease prediction and adjusting feeding and management methods.展开更多
Regenerative braking was the process of converting the kinetic energy and potential energy, which were stored in the vehicle body when vehicle braked or went downhill, into electrical energy and storing it into batter...Regenerative braking was the process of converting the kinetic energy and potential energy, which were stored in the vehicle body when vehicle braked or went downhill, into electrical energy and storing it into battery. The problem on how to distribute braking forces of front wheel and rear wheel for electric vehicles with four-wheel drive was more complex than that for electric vehicles with front-wheel drive or rear-wheel drive. In this work, the frictional braking forces distribution curve of front wheel and rear wheel is determined by optimizing the braking force distribution curve of hydraulic proportional-adjustable valve, and then the safety brake range is obtained correspondingly. A new braking force distribution strategy based on regenerative braking strength continuity is proposed to solve the braking force distribution problem for electric vehicles with four-wheel drive. Highway fuel economy test(HWFET) driving condition is used to provide the speed signals, the braking force equations of front wheel and rear wheel are expressed with linear equations. The feasibility, effectiveness, and practicality of the new braking force distribution strategy based on regenerative braking strength continuity are verified by regenerative braking strength simulation curve and braking force distribution simulation curves of front wheel and rear wheel. The proposed strategy is simple in structure, easy to be implemented and worthy being spread.展开更多
The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index...The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index),leaf chlorophyll content(C_(ab)),canopy water content(C_(w)),and dry matter content(C_(dm))of rice was inversed based on the hyperspectral remote sensing technology of an unmanned aerial vehicle(UAV).The improved Sobol global sensitivity analysis(GSA)method was used to analyze the input parameters of the PROSAIL model in the spectral band range of 400-1100 nm,which was obtained by hyperspectral remote sensing by the UAV.The results show that C_(ab) mainly affects the spectrum on 400-780 nm band,C_(dm) on 760-1000 nm band,C_(w) on 900-1100 nm band,and LAI on the entire band.The hyperspectral data of the 400-1100 nm band of the rice canopy were acquired by using the M600 UAV remote sensing platform,and the radiance calibration was converted to the canopy emission rate.In combination with the PROSAIL model,the particle swarm optimization algorithm was used to retrieve rice phenotyping information by constructing the cost function.The results showed the following:(1)an accuracy of R^(2)=0.833 and RMSE=0.0969,where RMSE denotes root-mean-square error,was obtained for C_(ab) retrieval;R^(2)=0.816 and RMSE=0.1012 for LAI inversion;R^(2)=0.793 and RMSE=0.1084 for C_(dm);and R^(2)=0.665 and RMSE=0.1325 for C_(w).The C_(w) inversion accuracy was not particularly high.(2)The same band will be affected by multiple parameters at the same time.(3)This study adopted the rice phenotyping information inversion method to expand the rice hyperspectral information acquisition field of a UAV based on the phenotypic information retrieval accuracy using a high level of field spectral radiometric accuracy.The inversion method featured a good mechanism,high universality,and easy implementation,which can provide a reference for nondestructive and rapid inversion of rice biochemical parameters using UAV hyperspectral remote 展开更多
Perovskite solar cells(PSCs)emerging as a promising photovoltaic technology with high efficiency and low manufacturing cost have attracted the attention from all over the world.Both the efficiency and stability of PSC...Perovskite solar cells(PSCs)emerging as a promising photovoltaic technology with high efficiency and low manufacturing cost have attracted the attention from all over the world.Both the efficiency and stability of PSCs have increased steadily in recent years,and the research on reducing lead leakage and developing eco-friendly lead-free perovskites pushes forward the commercialization of PSCs step by step.This review summarizes the main progress of PSCs in 2020 and 2021 from the aspects of efficiency,stability,perovskite-based tandem devices,and lead-free PSCs.Moreover,a brief discussion on the development of PSC modules and its challenges toward practical application is provided.展开更多
Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) ...Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.展开更多
Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under...Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions.This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer(DGWO).It dynamically adjusts the parameters of the MPPT controller,specifically the duty cycle of the SEPIC converter,to efficiently track the Maximum Power Point(MPP).The proposed system aims to enhance the energy harvesting capability of solar PV systems by optimizing their performance under varying solar irradiance,temperature and shading conditions.Simulation results demonstrate the effectiveness of the DGWO-based MPPT system in maximizing the power output of solar PV installations compared to conventional MPPT methods.This research contributes to the development of advanced MPPT techniques for improving the efficiency and reliability of solar energy systems.展开更多
Coughing is an obvious respiratory disease symptom,which affects the airways and lungs of pigs.In pig houses,continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease ea...Coughing is an obvious respiratory disease symptom,which affects the airways and lungs of pigs.In pig houses,continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease early detection.Owing to complicated interferences in piggery,recognition of pig cough sound becomes difficult.Although a lot of algorithms have been proposed to recognize the pig cough sounds,the recognition accuracy in field sit-uations still needs enhancement.The purpose of this research is to provide a highly accu-rate pig cough recognition method for the respiratory disease alarm system.We propose a classification algorithm based on the fine-tuned AlexNet model and feature of the spectro-gram.With the advantages of the convolutional neural network in image recognition,the sound signals are converted into spectrogram images for recognition,to enhance the accu-racy.We compare the proposed algorithm’s performance with the probabilistic neural net-work classifier and some existing algorithms.The results reveal that the proposed algorithm significantly outperforms the other algorithms-cough and overall recognition accuracies reach to 96.8%and 95.4%,respectively,with 96.2%F1-score achieved.展开更多
The physics principle of pulse flight positioning is the main theoretical bottleneck that restricts the spatial resolution of the existing Raman distributed optical fiber sensing scheme.Owing to the pulse width of ten...The physics principle of pulse flight positioning is the main theoretical bottleneck that restricts the spatial resolution of the existing Raman distributed optical fiber sensing scheme.Owing to the pulse width of tens of nanoseconds,the spatial resolution of the existing Raman distributed optical fiber sensing scheme with kilometer-level sensing distance is limited to the meter level,which seriously restricts the development of the optical time-domain reflection system.In this paper,a chaos laser is proposed in the context of the physical principle of the Raman scattering effect,and a novel theory of chaos Raman distributed optical fiber sensing scheme is presented.The scheme reveals the characteristics of chaos Raman scattering light excited by a chaotic signal on the sensing fiber.Further,the chaos time-domain compression demodulation mechanism between the temperature variation information and chaos correlation peak is demonstrated.Then,the position of the temperature variation signal is precisely located using the delay time of the chaos correlation peak combined with the chaos pulse flight time.Based on this novel optical sensing mechanism,an experiment with 10 cm spatial resolution and 1.4 km sensing distance was conducted,and the spatial resolution was found to be independent of the sensing distance.Within the limit of the existing spatial resolution theory,the spatial resolution of the proposed scheme is 50 times higher than that of the traditional scheme.The scheme also provides a new research direction for optical chaos and optical fiber sensing.展开更多
Dear editor,This letter presents a practical industrial process identification scheme.More specifically,to improve the identification accuracy of practical process,a decoupled identification scheme is developed based ...Dear editor,This letter presents a practical industrial process identification scheme.More specifically,to improve the identification accuracy of practical process,a decoupled identification scheme is developed based on neural fuzzy network and autoregressive exogenous(ARX)model,which is based on multi-signal sources.The multiple signal sources include binary signals and random signals.Experimental results of pH neutralization process show that developed identification scheme can provide accurate identification accuracy.展开更多
Microbial fuel cell(MFC)is a kind of promising clean power supply energy equipment,but serious nonlinearities and disturbances exist when the MFC runs,and it is an important topic to guarantee that the output voltage ...Microbial fuel cell(MFC)is a kind of promising clean power supply energy equipment,but serious nonlinearities and disturbances exist when the MFC runs,and it is an important topic to guarantee that the output voltage reaches the setting value quickly and smoothly.Regulating the feeding flow is an effective way to achieve this goal,and especially,the satisfactory results can be achieved by regulating anode feeding flow.In this work,a feedforward fuzzy logic PID algorithm is proposed.The fuzzy logic system is introduced to deal with the non-linear dynamics of MFC,and corresponding PID parameters are calculated according to defuzzification.The magnitude value of the current density is used to simulate the value of the external load.The simulation results indicate that the MFC output voltage can track the setting value quickly and smoothly with the proposed feedforward fuzzy logic PID algorithm.The proposed algorithm is more efficient and robust with respect to anti-disturbance performance and tracking accuracy than other three control methods.展开更多
The H_∞ performance analysis and controller design for linear networked control systems(NCSs) are presented.The NCSs are considered a linear continuous system with time-varying interval input delay by assuming that t...The H_∞ performance analysis and controller design for linear networked control systems(NCSs) are presented.The NCSs are considered a linear continuous system with time-varying interval input delay by assuming that the sensor is time-driven and the logic Zero-order-holder(ZOH) and controller are event-driven.Based on this model,the delay interval is divided into two equal subintervals for H_∞ performance analysis.An improved H_∞ stabilization condition is obtained in linear matrix inequalities(LMIs) framework by adequately considering the information about the bounds of the input delay to construct novel Lyapunov–Krasovskii functionals(LKFs).For the purpose of reducing the conservatism of the proposed results,the bounds of the LKFs differential cross terms are properly estimated without introducing any slack matrix variables.Moreover,the H_∞ controller is reasonably designed to guarantee the robust asymptotic stability for the linear NCSs with an H_∞ performance level γ.Numerical simulation examples are included to validate the reduced conservatism and effectiveness of our proposed method.展开更多
Metasurfaces are subwavelength structured thin films consisting of arrays of units that allow the control of polarization,phase,and amplitude of light over a subwavelength thickness.Recent developments in topological ...Metasurfaces are subwavelength structured thin films consisting of arrays of units that allow the control of polarization,phase,and amplitude of light over a subwavelength thickness.Recent developments in topological photonics have greatly broadened the horizon in designing metasurfaces for novel functional applications.In this review,we summarize recent progress in the research field of topological metasurfaces,first from the perspectives of passive and active in the classical regime,and then in the quantum regime.More specifically,we begin by examining the passive topological phenomena in two-dimensional photonic systems,including both time-reversal broken systems and time-reversal preserved systems.Subsequently,we discuss the cutting-edge studies of active topological metasurfaces,including nonlinear topological metasurfaces and reconfigurable topological metasurfaces.After overviewing topological metasurfaces in the classical regime,we show how they could provide a new platform for quantum information and quantum many-body physics.Finally,we conclude and describe some challenges and future directions of this fast-evolving field.展开更多
Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method f...Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method for vegetable images classification based on deep learning framework.The AlexNet network model in Caffe was used to train the vegetable image data set.The vegetable image data set was obtained from ImageNet and divided into training data set and test data set.The output function of the AlexNet network adopted the Rectified Linear Units(ReLU)instead of the traditional sigmoid function and the tanh function,which can speed up the training of the deep learning network.The dropout technology was used to improve the generalization of the model.The image data extension method was used to reduce overfitting in the learning process.With AlexNet network model used for training different number of vegetable image data set,the experimental results showed that the classification accuracy decreases as the number of data set decreases.The experimental verification indicated that the accuracy rate of the deep learning method in the test data set reached as high as 92.1%,which was greatly improved compared with BP neural network(78%)and SVM classifier(80.5%)methods.展开更多
At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns st...At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61004078 and 60971022)the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2009GQ009 and ZR2009GM005)+1 种基金the China Postdoctoral Science Foundation (Grant No. 20100481293)the Special Funds for Postdoctoral Innovative Projects of Shandong Province, China (Grant No. 201003037)
文摘We propose a new image encryption algorithm on the basis of the fractional-order hyperchaotic Lorenz system. While in the process of generating a key stream, the system parameters and the derivative order are embedded in the proposed algorithm to enhance the security. Such an algorithm is detailed in terms of security analyses, including correlation analysis, information entropy analysis, run statistic analysis, mean-variance gray value analysis, and key sensitivity analysis. The experimental results demonstrate that the proposed image encryption scheme has the advantages of large key space and high security for practical image encryption.
基金co-supported by National Outstanding Youth Science Foundation(No.61125306)National Natural Science Foundation of Major Research Plan(Nos.91016004,61034002)+1 种基金Research Fund for the Doctoral Program of Higher Education of China(No.20110092110020)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1103)
文摘This article proposes a linear parameter varying （LPV） switching tracking control scheme for a flexible air-breathing hypersonic vehicle （FAHV）. First, a polytopic LPV model is constructed to represent the complex nonlinear longitudinal model of the FAHV by using Jacobian linearization and tensor-product （T-P） model transformation approach. Second, for less conservative controller design purpose, the flight envelope is divided into four sub-regions and a non-fragile LPV controller is designed for each parameter sub-region. These non-fragile LPV controllers are then switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a specified performance criterion. The desired non-fragile LPV switching controller is found by solving a convex constraint problem which can be efficiently solved using available linear matrix inequality （LMI） techniques, and robust stability analysis of the closed-loop FAHV system is verified based on multiple Lypapunov functions （MLFs）. Finally, numerical simulations have demonstrated the effectiveness of the proposed approach.
文摘The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment innovation.Insulating material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and opportunities.The goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first discussed.The engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are investigated.Insulating materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being discussed.In the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material level.It is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality rules.The authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.
基金supported in part by the National Natural Science Foundation of China(No.51807127)in part by the Fundamental Research Funds for the Central Universities of China opment Program of China(No.2018YFB0905200).(No.YJ201654)in part by the National Key Research and Development Program of China(No.2018YFB0905200).
文摘The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers.To address this issue,an incentive-based demand response(DR)model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers.The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions.The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price.Then,the sensitivity of incentive price on retailers’benefits is analyzed and the optimal incentive price is obtained according to the DR model.The case study verifies the effectiveness of the proposed model.
基金This work was supported by the following funds:(1)The Natural Science Foundation of China(51905553,51935014,81871494,81871498)(2)Hunan Provincial Natural Science Foundation of China(2019JJ50774,2019JJ50588)+5 种基金(3)The Provincial Key R&D Projects of Jiangxi(20201BBE51012)(4)JiangXi Provincial Natural Science Foundation of China(20192ACB20005)(5)Guangdong Province Higher Vocational Colleges&Schools Pearl River Scholar Funded Scheme(2018)(6)The Project of Hunan Provincial Science and Technology Plan(2017RS3008)(7)The Project of State Key Laboratory of High Performance Complex Manufacturing,Central South University(8)Shenzhen Science and Technology Plan Project(JCYJ20170817112445033).
文摘The incorporation of hydroxyapatite(HAP)into poly-L-lactic acid(PLLA)matrix serving as bone scaffold is expected to exhibit bioactivity and osteoconductivity to those of the living bone.While too low degradation rate of HAP/PLLA scaffold hinders the activity because the embedded HAP in the PLLA matrix is difficult to contact and exchange ions with body fluid.In this study,biodegradable polymer poly(glycolic acid)(PGA)was blended into the HAP/PLLA scaffold fabricated by laser 3D printing to accelerate the degradation.The results indicated that the incorporation of PGA enhanced the degradation rate of scaffold as indicated by the weight loss increasing from 3.3%to 25.0%after immersion for 28 days,owing to the degradation of high hydrophilic PGA and the subsequent accelerated hydrolysis of PLLA chains.Moreover,a lot of pores produced by the degradation of the scaffold promoted the exposure of HAP from the matrix,which not only activated the deposition of bone like apatite on scaffold but also accelerated apatite growth.Cytocompatibility tests exhibited a good osteoblast adhesion,spreading and proliferation,suggesting the scaffold provided a suitable environment for cell cultivation.Furthermore,the scaffold displayed excellent bone defect repair capacity with the formation of abundant new bone tissue and blood vessel tissue,and both ends of defect region were bridged after 8 weeks of implantation.
基金supported by the National Natural Science Foundation of China(Grant Nos.11672122&11765011)
文摘Electric and chemical synapse play important role in connecting neurons and thus signal propagation can be realized between neurons. External electric stimulus can change the excitability of neuron and then the electrical activities can be modulated completely. Continuous fluctuation of ion concentration in cell can induce complex time-varying electromagnetic field during the exchange of charged ions across the membrane of neuron. Polarization and magnetization in the media(and neuron), which exposed to electromagnetic radiation, can modulate the dynamical response and mode transition in electrical activities of neurons. In this paper, magnetic flux is used to describe the effect of electromagnetic field, and the three-variable HindmarshRose neuron model is updated to propose a four-variable neuron model that the effect of electromagnetic induction and radiation can be explained. Based on the physical law of electromagnetic induction, exchange of charged ions and flow of ion currents will change the distribution of electromagnetic filed in cell, and each neuron will be exposed to the superimposed field triggered by other neurons. Therefore, signal exchange could occur even synapse coupling between neurons is removed in the case of field coupling. A chain network is proposed to investigate the signal exchange between neurons under field coupling when synapse coupling is not available. It is found that field coupling between neurons can change the collective behaviors in electrical activities. A statistical factor of synchronization and spatial patterns are calculated, these results confirmed that field coupling is effective for signal communication between neurons. In the end, open problems are suggested for readers' extensive guidance in this field.
基金This research is financially supported by National Key Research and Development Program of China(2016YFD0700204-02)Research on Intelligent Non-contact Monitoring of Ruminating and Feeding Behavior of Dairy Cows,Heilongjiang Natural Science Foundation(LH2019C025)+4 种基金The“Young Talents”Project of Northeast Agricultural University(17QC19)China Postdoctoral Science Foundation(2017M611346)The Earmarked Fund for China Agriculture Research System(No.CARS-36)The University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant(UNPYSCT-2018143)The authors are grateful to anonymous reviewers for their comments.
文摘Ingestive-related behaviors including feeding and ruminating are important indexes to measure the health and welfare of dairy cows.The purpose of this study is to develop a method based on triaxial acceleration to automatically recognize feeding and ruminating of dairy cows.During the experiment,five diary cows raised in a barn were used as experimental subjects.A triaxial acceleration sensor was used as the device to collect jawmovement data of dairy cows,and the behaviors of dairy cows were classified into three categories:feeding,ruminating and other behavior.The features of time-domain and frequency-domain were extracted from the raw acceleration data.Three machine learning algorithms including k-nearest neighbor,support vector machine and probabilistic neural network were used for the classification and the results based on four different data segment lengths were compared.The results show that the three algorithms can be used for recognition of feeding and ruminating with high accuracy.Under the condition that the sampling frequency of the sensor is 5 Hz,the combination of data segment length of 256 and k-nearest neighbor algorithm is the best scheme for recognition of feeding and ruminating in this study.The precision and recall of recognition for feeding were 92.8%and 95.6%respectively,and those of recognition for ruminating were 93.7%and 94.3%respectively.The specificity and AUC of recognition for feeding were 96.1%and 0.959 respectively,and those of recognition for ruminating were 97.5%and 0.959 respectively.This will provide an effective method for real-time monitoring of ingestive-related behaviors of dairy cows and lay a foundation for prediction of dairy cows’health status and welfare to further achieve the purpose of disease prediction and adjusting feeding and management methods.
基金Project(JS-102)supported by the National Key Science and Technological Program of China for Electric VehiclesProject supported by Jilin University "985 Project" Engineering Bionic Technology Innovation Platform,China
文摘Regenerative braking was the process of converting the kinetic energy and potential energy, which were stored in the vehicle body when vehicle braked or went downhill, into electrical energy and storing it into battery. The problem on how to distribute braking forces of front wheel and rear wheel for electric vehicles with four-wheel drive was more complex than that for electric vehicles with front-wheel drive or rear-wheel drive. In this work, the frictional braking forces distribution curve of front wheel and rear wheel is determined by optimizing the braking force distribution curve of hydraulic proportional-adjustable valve, and then the safety brake range is obtained correspondingly. A new braking force distribution strategy based on regenerative braking strength continuity is proposed to solve the braking force distribution problem for electric vehicles with four-wheel drive. Highway fuel economy test(HWFET) driving condition is used to provide the speed signals, the braking force equations of front wheel and rear wheel are expressed with linear equations. The feasibility, effectiveness, and practicality of the new braking force distribution strategy based on regenerative braking strength continuity are verified by regenerative braking strength simulation curve and braking force distribution simulation curves of front wheel and rear wheel. The proposed strategy is simple in structure, easy to be implemented and worthy being spread.
基金support of the National Key Research and Development Plan of China(Grant No.2016YFD020060307)Key Project of Education Department of Liaoning province(LSNZD201605).
文摘The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index),leaf chlorophyll content(C_(ab)),canopy water content(C_(w)),and dry matter content(C_(dm))of rice was inversed based on the hyperspectral remote sensing technology of an unmanned aerial vehicle(UAV).The improved Sobol global sensitivity analysis(GSA)method was used to analyze the input parameters of the PROSAIL model in the spectral band range of 400-1100 nm,which was obtained by hyperspectral remote sensing by the UAV.The results show that C_(ab) mainly affects the spectrum on 400-780 nm band,C_(dm) on 760-1000 nm band,C_(w) on 900-1100 nm band,and LAI on the entire band.The hyperspectral data of the 400-1100 nm band of the rice canopy were acquired by using the M600 UAV remote sensing platform,and the radiance calibration was converted to the canopy emission rate.In combination with the PROSAIL model,the particle swarm optimization algorithm was used to retrieve rice phenotyping information by constructing the cost function.The results showed the following:(1)an accuracy of R^(2)=0.833 and RMSE=0.0969,where RMSE denotes root-mean-square error,was obtained for C_(ab) retrieval;R^(2)=0.816 and RMSE=0.1012 for LAI inversion;R^(2)=0.793 and RMSE=0.1084 for C_(dm);and R^(2)=0.665 and RMSE=0.1325 for C_(w).The C_(w) inversion accuracy was not particularly high.(2)The same band will be affected by multiple parameters at the same time.(3)This study adopted the rice phenotyping information inversion method to expand the rice hyperspectral information acquisition field of a UAV based on the phenotypic information retrieval accuracy using a high level of field spectral radiometric accuracy.The inversion method featured a good mechanism,high universality,and easy implementation,which can provide a reference for nondestructive and rapid inversion of rice biochemical parameters using UAV hyperspectral remote
基金supported by the National Natural Science Foundation of China(Grant Nos.11834011 and 12074245)the support from the Energy Materials and Surface Sciences Unit of the Okinawa Institute of Science and Technology Graduate University。
文摘Perovskite solar cells(PSCs)emerging as a promising photovoltaic technology with high efficiency and low manufacturing cost have attracted the attention from all over the world.Both the efficiency and stability of PSCs have increased steadily in recent years,and the research on reducing lead leakage and developing eco-friendly lead-free perovskites pushes forward the commercialization of PSCs step by step.This review summarizes the main progress of PSCs in 2020 and 2021 from the aspects of efficiency,stability,perovskite-based tandem devices,and lead-free PSCs.Moreover,a brief discussion on the development of PSC modules and its challenges toward practical application is provided.
基金the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Grant No.GRANT5,208).
文摘Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.
文摘Maximum Power Point Tracking(MPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions.This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer(DGWO).It dynamically adjusts the parameters of the MPPT controller,specifically the duty cycle of the SEPIC converter,to efficiently track the Maximum Power Point(MPP).The proposed system aims to enhance the energy harvesting capability of solar PV systems by optimizing their performance under varying solar irradiance,temperature and shading conditions.Simulation results demonstrate the effectiveness of the DGWO-based MPPT system in maximizing the power output of solar PV installations compared to conventional MPPT methods.This research contributes to the development of advanced MPPT techniques for improving the efficiency and reliability of solar energy systems.
基金This work was supported by the grant from the National Key Research and Development Program of China under Grant 2016YFD0700204-02the Earmarked Fund for China Agricul-ture Research System under Grant CARS-35+2 种基金the"Young Talents"Project of Northeast Agricultural University under Grant 17QC20the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant UNPYSCT-2020092 and UNPYSCT-2018142and the Hei-longjiang Post-doctoral Subsidy Project of China under Grant LBH-Z17020.
文摘Coughing is an obvious respiratory disease symptom,which affects the airways and lungs of pigs.In pig houses,continuous online monitoring of cough sounds can be used to build an intelligent alarm system for disease early detection.Owing to complicated interferences in piggery,recognition of pig cough sound becomes difficult.Although a lot of algorithms have been proposed to recognize the pig cough sounds,the recognition accuracy in field sit-uations still needs enhancement.The purpose of this research is to provide a highly accu-rate pig cough recognition method for the respiratory disease alarm system.We propose a classification algorithm based on the fine-tuned AlexNet model and feature of the spectro-gram.With the advantages of the convolutional neural network in image recognition,the sound signals are converted into spectrogram images for recognition,to enhance the accu-racy.We compare the proposed algorithm’s performance with the probabilistic neural net-work classifier and some existing algorithms.The results reveal that the proposed algorithm significantly outperforms the other algorithms-cough and overall recognition accuracies reach to 96.8%and 95.4%,respectively,with 96.2%F1-score achieved.
基金Supported by National Natural Science Foundation of China(NSFC)under Grants(62075151,62205234,62105234)National Key Research and Development Program of China(2022YFA1404201)+2 种基金Supported by Fundamental Research Program of Shanxi Province(202103021223042)Supported by Scientific and Technological Innovation Programs of Higher Education Institutions in ShanxiSupported by Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering.
文摘The physics principle of pulse flight positioning is the main theoretical bottleneck that restricts the spatial resolution of the existing Raman distributed optical fiber sensing scheme.Owing to the pulse width of tens of nanoseconds,the spatial resolution of the existing Raman distributed optical fiber sensing scheme with kilometer-level sensing distance is limited to the meter level,which seriously restricts the development of the optical time-domain reflection system.In this paper,a chaos laser is proposed in the context of the physical principle of the Raman scattering effect,and a novel theory of chaos Raman distributed optical fiber sensing scheme is presented.The scheme reveals the characteristics of chaos Raman scattering light excited by a chaotic signal on the sensing fiber.Further,the chaos time-domain compression demodulation mechanism between the temperature variation information and chaos correlation peak is demonstrated.Then,the position of the temperature variation signal is precisely located using the delay time of the chaos correlation peak combined with the chaos pulse flight time.Based on this novel optical sensing mechanism,an experiment with 10 cm spatial resolution and 1.4 km sensing distance was conducted,and the spatial resolution was found to be independent of the sensing distance.Within the limit of the existing spatial resolution theory,the spatial resolution of the proposed scheme is 50 times higher than that of the traditional scheme.The scheme also provides a new research direction for optical chaos and optical fiber sensing.
基金the National Natural Science Foundation of China(62003151)the Natural Science Foundation of Jiangsu Province(BK20191035)the Changzhou Sci&Tech Program(CJ20220065)。
文摘Dear editor,This letter presents a practical industrial process identification scheme.More specifically,to improve the identification accuracy of practical process,a decoupled identification scheme is developed based on neural fuzzy network and autoregressive exogenous(ARX)model,which is based on multi-signal sources.The multiple signal sources include binary signals and random signals.Experimental results of pH neutralization process show that developed identification scheme can provide accurate identification accuracy.
基金Project(61563032)supported by the National Natural Science Foundation of ChinaProject(18JR3RA133)supported by Gansu Basic Research Innovation Group,China
文摘Microbial fuel cell(MFC)is a kind of promising clean power supply energy equipment,but serious nonlinearities and disturbances exist when the MFC runs,and it is an important topic to guarantee that the output voltage reaches the setting value quickly and smoothly.Regulating the feeding flow is an effective way to achieve this goal,and especially,the satisfactory results can be achieved by regulating anode feeding flow.In this work,a feedforward fuzzy logic PID algorithm is proposed.The fuzzy logic system is introduced to deal with the non-linear dynamics of MFC,and corresponding PID parameters are calculated according to defuzzification.The magnitude value of the current density is used to simulate the value of the external load.The simulation results indicate that the MFC output voltage can track the setting value quickly and smoothly with the proposed feedforward fuzzy logic PID algorithm.The proposed algorithm is more efficient and robust with respect to anti-disturbance performance and tracking accuracy than other three control methods.
基金Project (61304046) supported by the National Natural Science Funds for Young Scholar of ChinaProject (F201242) supported by Natural Science Foundation of Heilongjiang Province,China
文摘The H_∞ performance analysis and controller design for linear networked control systems(NCSs) are presented.The NCSs are considered a linear continuous system with time-varying interval input delay by assuming that the sensor is time-driven and the logic Zero-order-holder(ZOH) and controller are event-driven.Based on this model,the delay interval is divided into two equal subintervals for H_∞ performance analysis.An improved H_∞ stabilization condition is obtained in linear matrix inequalities(LMIs) framework by adequately considering the information about the bounds of the input delay to construct novel Lyapunov–Krasovskii functionals(LKFs).For the purpose of reducing the conservatism of the proposed results,the bounds of the LKFs differential cross terms are properly estimated without introducing any slack matrix variables.Moreover,the H_∞ controller is reasonably designed to guarantee the robust asymptotic stability for the linear NCSs with an H_∞ performance level γ.Numerical simulation examples are included to validate the reduced conservatism and effectiveness of our proposed method.
基金National Natural Science Foundation of China(62201136,62175215,62101124,62171406)Basic Scientific Center of Information Metamaterials of the National Natural Science Foundation of China(62288101)+4 种基金National Key Research and Development Program of China(2017YFA0700201,2017YFA0700202,2017YFA0700203,2022YFA1404902)Natural Science Foundation of Jiangsu Province(BK20210209,BK20212002)111 Project(111-2-05)Natural Science Foundation of Zhejiang Province(Z20F010018)Fundamental Research Funds for the Central Universities。
文摘Metasurfaces are subwavelength structured thin films consisting of arrays of units that allow the control of polarization,phase,and amplitude of light over a subwavelength thickness.Recent developments in topological photonics have greatly broadened the horizon in designing metasurfaces for novel functional applications.In this review,we summarize recent progress in the research field of topological metasurfaces,first from the perspectives of passive and active in the classical regime,and then in the quantum regime.More specifically,we begin by examining the passive topological phenomena in two-dimensional photonic systems,including both time-reversal broken systems and time-reversal preserved systems.Subsequently,we discuss the cutting-edge studies of active topological metasurfaces,including nonlinear topological metasurfaces and reconfigurable topological metasurfaces.After overviewing topological metasurfaces in the classical regime,we show how they could provide a new platform for quantum information and quantum many-body physics.Finally,we conclude and describe some challenges and future directions of this fast-evolving field.
基金This research was financially supported by the International Science&Technology Cooperation Program of China(2015DFA00530)Key Research and Development Plan Project of Shandong Province(2016CYJS03A02).
文摘Deep learning techniques can automatically learn features from a large number of image data set.Automatic vegetable image classification is the base of many applications.This paper proposed a high performance method for vegetable images classification based on deep learning framework.The AlexNet network model in Caffe was used to train the vegetable image data set.The vegetable image data set was obtained from ImageNet and divided into training data set and test data set.The output function of the AlexNet network adopted the Rectified Linear Units(ReLU)instead of the traditional sigmoid function and the tanh function,which can speed up the training of the deep learning network.The dropout technology was used to improve the generalization of the model.The image data extension method was used to reduce overfitting in the learning process.With AlexNet network model used for training different number of vegetable image data set,the experimental results showed that the classification accuracy decreases as the number of data set decreases.The experimental verification indicated that the accuracy rate of the deep learning method in the test data set reached as high as 92.1%,which was greatly improved compared with BP neural network(78%)and SVM classifier(80.5%)methods.
基金supported by Project No.R-2023-23 of the Deanship of Scientific Research at Majmaah University.
文摘At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.
