Background:Mastitis seriously affects the mammary health of humans and animals.Studies have found that inflammation and oxidative stress play key roles in the occur-rence and development of mastitis.Therefore,in-depth...Background:Mastitis seriously affects the mammary health of humans and animals.Studies have found that inflammation and oxidative stress play key roles in the occur-rence and development of mastitis.Therefore,in-depth research on related molecular mechanisms is of great significance.Methods:Postpartum mice were anesthetized with pentobarbital and administered lipopolysaccharide to develop the mouse mastitis model.Proteomic analysis was per-formed to compare protein expression in mitochondria-associated endoplasmic retic-ulum membranes(MAM)from two mouse mammary gland groups.Western blot was used to detect the expression of MAM-related proteins in mitochondria.AlphaFold3 was used to predict the molecular structures of phosphofurin acidic cluster sorting protein 2(PACS2)and mitofusin 2(MFN2)and their interaction levels.The MFN2-PACS2 interaction was investigated using co-immunoprecipitation and small interfer-ing RNA.Results:The results showed that the inflammation level in the mammary gland tissue of mice with mastitis significantly increased,the total antioxidant capacity decreased,and the expression of MAM-related proteins MFN2 and PACS2 was significantly downregulated.In cell experiments,overexpression of MFN2 can inhibit inflamma-tion and oxidative stress responses,and promote the interaction between MFN2 and PACS2 to affect the formation of MAMs.Conclusion:In summary,this study suggests that mastitis can alter the expression of MAM-related proteins in mouse breast tissue.The interaction between MFN2 and PACS2 regulates the formation of MAMs.Overexpression of MFN2 can promote the formation of MAMs and inhibit inflammation and oxidative stress response in mam-mary epithelial cells.Our results provided a new theoretical basis and potential thera-peutic targets for the prevention and treatment of mastitis.展开更多
A multi-parameter signal sorting algorithm for interleaved radar pulses in dense emitter environment is presented.The algorithm includes two parts,pulse classification and pulse repetition interval(PRI)analysis.Firstl...A multi-parameter signal sorting algorithm for interleaved radar pulses in dense emitter environment is presented.The algorithm includes two parts,pulse classification and pulse repetition interval(PRI)analysis.Firstly,we propose the dynamic distance clustering(DDC)for classification.In the clustering algorithm,the multi-dimension features of radar pulse are used for reliable classification.The similarity threshold estimation method in DDC is derived,which contributes to the efficiency of the algorithm.However,DDC has large computation with many signal pulses.Then,in order to sort radar signals in real time,the improved DDC(IDDC)algorithm is proposed.Finally,PRI analysis is adopted to complete the process of sorting.The simulation experiments and hardware implementations show both algorithms are effective.展开更多
Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI present...Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.展开更多
In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above ...In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.展开更多
Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in...Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in time-independent Hamiltonians.Here,our work makes an effort to explore multi-parameter estimation with time-dependent Hamiltonians.In particular,we focus on the discrimination of two close frequencies of a magnetic field by using a single qubit.We optimize the quantum controls by employing both traditional optimization methods and reinforcement learning to improve the precision for estimating the frequencies of the two magnetic fields.In addition to the estimation precision,we also evaluate the robustness of the optimization schemes against the shift of the control parameters.The results demonstrate that the hybrid reinforcement learning approach achieves the highest estimation precision,and exhibits superior robustness.Moreover,a fundamental challenge in multi-parameter quantum estimation stems from the incompatibility of the optimal control strategies for different parameters.We demonstrate that the hybrid control strategies derived through numerical optimization remain effective in enhancing the precision of multi-parameter estimation in spite of the incompatibilities,thereby mitigating incompatibilities between control strategies on the estimation precision.Finally,we investigate the trade-offs in estimation precision among different parameters for different scenarios,revealing the inherent challenges in balancing the optimization of multiple parameters simultaneously and providing insights into the fundamental distinction between quantum single-parameter estimation and multi-parameter estimation.展开更多
Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).C...Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).Conventional battery monitoring technologies struggle to track multiple physicochemical parameters in real time,hindering early hazard detection.Embedded optical fiber sensors have gained prominence as a transformative solution for next-generation smart battery sensing,owing to their micrometer size,multiplexing capability,and electromagnetic immunity.However,comprehensive reviews focusing on their advancements in operando multi-parameter monitoring remain scarce,despite their critical importance for ensuring battery safety.To address this gap,this review first introduces a classification and the fundamental principles of advanced battery-oriented optical fiber sensors.Subsequently,it summarizes recent developments in single-parameter battery monitoring using optical fiber sensors.Building on this foundation,this review presents the first comprehensive analysis of multifunctional optical fiber sensing platforms capable of simultaneously tracking temperature,strain,pressure,refractive index,and monitoring battery aging.Targeted strategies are proposed to facilitate the practical development of this technology,including optimization of sensor integration techniques,minimizing sensor invasiveness,resolving the cross-sensitivity of fiber Bragg grating(FBG)through structural innovation,enhancing techno-economics,and combining with artificial intelligence(AI).By aligning academic research with industry requirements,this review provides a methodological roadmap for developing robust optical sensing systems to ensure battery safety in decarbonization-driven applications.展开更多
Under sensorless control, the position estimation error in interior permanent magnet(PM) synchronous machines will lead to parameter identification errors and a rank-deficiency issue. This paper proposes a parameter i...Under sensorless control, the position estimation error in interior permanent magnet(PM) synchronous machines will lead to parameter identification errors and a rank-deficiency issue. This paper proposes a parameter identification model that is independent of position error by combining the dq-axis voltage equations. Then, a novel dual signal alternate injection method is proposed to address the rank-deficiency issue, i.e., during one injection period, a zero, positive, and negative d-axis current injection together with a rotor position offset injection, to simultaneously identify the multi-parameters, including stator resistance, dq-axis inductances, and PM flux linkage. The proposed method is verified by experiments at different dq-axis current conditions.展开更多
The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Ti...The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Time-sensitive networking(TSN)is proposed by IEEE 802.1TSN working group.In order to achieve low latency,Cyclic queuing and forwarding(CQF)mechanism is introduced to schedule Timetriggered(TT)flows.In this paper,we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling.The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows.Firstly,by K-means algorithm,the flows are initially partitioned into subsets based on their correlations.Subsequently,the flows within each subset are sorted by a new special criteria extracted from multiple features of flow.Finally,a flow offset selecting method based on load balance is used for resource mapping,so as to complete the process of flow scheduling.Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.展开更多
The somatotopic representation of specific body parts is a well-established spatial organizational principle in the primary somatosensory and motor cortices.
The main function of electronic support measure system is to detect threating signals in order to take countermeasures against them. To accomplish this objective, a process of associating each interleaved pulse with i...The main function of electronic support measure system is to detect threating signals in order to take countermeasures against them. To accomplish this objective, a process of associating each interleaved pulse with its emitter must be done. This process is termed sorting or de-interleaving. A novel point symmetry based radar sorting (PSBRS) algorithm is addressed. In order to deal with all kinds of radar signals, the symmetry measure distance is used to cluster pulses instead of the conventional Euclidean distance. The reference points of the symmetrical clusters are initialized by the alternative fuzzy c-means (AFCM) algorithm to ameliorate the effects of noise and the false sorting. Besides, the density filtering (DF) algorithm is proposed to discard the noise pulses or clutter. The performance of the algorithm is evaluated under the effects of noise and missing pulses. It has been observed that the PSBRS algorithm can cope with a large number of noise pulses and it is completely independent of missing pulses. Finally, PSBRS is compared with some benchmark algorithms, and the simulation results reveal the feasibility and efficiency of the algorithm.展开更多
The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an exa...The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.展开更多
Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powe...Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy(BSE) index, time-space-magnitude independent information(TSMII) indices and timespace-magnitude compound information(TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW402102 of Hujiahe Coal Mine(China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk.展开更多
Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore ...Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.展开更多
A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing ch...A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting.展开更多
The deterministic lateral displacement (DLD) is an important method used to sort particles and cells of different sizes. In this paper, the flexible cell sorting with the DLD method is studied by using a numerical mod...The deterministic lateral displacement (DLD) is an important method used to sort particles and cells of different sizes. In this paper, the flexible cell sorting with the DLD method is studied by using a numerical model based on the immersed boundary-lattice Boltzmann method (IB-LBM). In this model, the fluid motion is solved by the LBM, and the cell membrane-fluid interaction is modeled with the LBM. The proposed model is validated by simulating the rigid particle sorted with the DLD method, and the results are found in good agreement with those measured in experiments. We first study the effect of flexibility on a single cell and multiple cells continuously going through a DLD device. It is found that the cell flexibility can significantly affect the cell path, which means the flexibility could have significant effects on the continuous cell sorting by the DLD method. The sorting characteristics of white blood cells and red blood cells are further studied by varying the spatial distribution of cylinder arrays and the initial cell-cell distance. The numerical results indicate that a well concentrated cell sorting can be obtained under a proper arrangement of cylinder arrays and a large enough initial cell-cell distance.展开更多
Common sorting method have low sorting rates and is sensitive to the Signal-to-Noise Ratio(SNR),wavelet characteristics of Wigner-Ville distribution are applied to sort unknown complicated radar signal,high sorting ac...Common sorting method have low sorting rates and is sensitive to the Signal-to-Noise Ratio(SNR),wavelet characteristics of Wigner-Ville distribution are applied to sort unknown complicated radar signal,high sorting accuracy can be got.The Wigner-Ville distribution of received signal is calculated,then it is predigested to two-dimensional characteristics.Using wavelet transformation to extract characteristics from two-dimensional of Wigner-Ville distribution,the best characteristics are selected to be used as sorting parameters.Experiment results demonstrated that the characteristics of eight typical radar emitter signals extracted by this method showed good performance of noise-resistance and clustering at large-scale SNR.展开更多
基金National Natural Science Foundation of China,Grant/Award Number:32302826 and 32372961Jilin Provincial Special Project for Health Research Talents,Grant/Award Number:2020SCZ40China Postdoctoral Science Foundation,Grant/Award Number:2023M740623。
文摘Background:Mastitis seriously affects the mammary health of humans and animals.Studies have found that inflammation and oxidative stress play key roles in the occur-rence and development of mastitis.Therefore,in-depth research on related molecular mechanisms is of great significance.Methods:Postpartum mice were anesthetized with pentobarbital and administered lipopolysaccharide to develop the mouse mastitis model.Proteomic analysis was per-formed to compare protein expression in mitochondria-associated endoplasmic retic-ulum membranes(MAM)from two mouse mammary gland groups.Western blot was used to detect the expression of MAM-related proteins in mitochondria.AlphaFold3 was used to predict the molecular structures of phosphofurin acidic cluster sorting protein 2(PACS2)and mitofusin 2(MFN2)and their interaction levels.The MFN2-PACS2 interaction was investigated using co-immunoprecipitation and small interfer-ing RNA.Results:The results showed that the inflammation level in the mammary gland tissue of mice with mastitis significantly increased,the total antioxidant capacity decreased,and the expression of MAM-related proteins MFN2 and PACS2 was significantly downregulated.In cell experiments,overexpression of MFN2 can inhibit inflamma-tion and oxidative stress responses,and promote the interaction between MFN2 and PACS2 to affect the formation of MAMs.Conclusion:In summary,this study suggests that mastitis can alter the expression of MAM-related proteins in mouse breast tissue.The interaction between MFN2 and PACS2 regulates the formation of MAMs.Overexpression of MFN2 can promote the formation of MAMs and inhibit inflammation and oxidative stress response in mam-mary epithelial cells.Our results provided a new theoretical basis and potential thera-peutic targets for the prevention and treatment of mastitis.
基金supported by the National Defense Pre-research Fund of China under Grant No 41101030401
文摘A multi-parameter signal sorting algorithm for interleaved radar pulses in dense emitter environment is presented.The algorithm includes two parts,pulse classification and pulse repetition interval(PRI)analysis.Firstly,we propose the dynamic distance clustering(DDC)for classification.In the clustering algorithm,the multi-dimension features of radar pulse are used for reliable classification.The similarity threshold estimation method in DDC is derived,which contributes to the efficiency of the algorithm.However,DDC has large computation with many signal pulses.Then,in order to sort radar signals in real time,the improved DDC(IDDC)algorithm is proposed.Finally,PRI analysis is adopted to complete the process of sorting.The simulation experiments and hardware implementations show both algorithms are effective.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001)the National Key R&D Program of China(Grant No.2019YFC0605503C)+2 种基金the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(Grant No.ZD2019-183-003)the National Outstanding Youth Science Foundation(Grant No.41922028)the National Innovation Group Project(Grant No.41821002).
文摘Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.
基金Supported by The National Undergraduate Innovation Training Program(Grant No.202310290069Z).
文摘In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.
基金supported by the National Natural Science Foundation of China(Grant No.12075323)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0300702).
文摘Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in time-independent Hamiltonians.Here,our work makes an effort to explore multi-parameter estimation with time-dependent Hamiltonians.In particular,we focus on the discrimination of two close frequencies of a magnetic field by using a single qubit.We optimize the quantum controls by employing both traditional optimization methods and reinforcement learning to improve the precision for estimating the frequencies of the two magnetic fields.In addition to the estimation precision,we also evaluate the robustness of the optimization schemes against the shift of the control parameters.The results demonstrate that the hybrid reinforcement learning approach achieves the highest estimation precision,and exhibits superior robustness.Moreover,a fundamental challenge in multi-parameter quantum estimation stems from the incompatibility of the optimal control strategies for different parameters.We demonstrate that the hybrid control strategies derived through numerical optimization remain effective in enhancing the precision of multi-parameter estimation in spite of the incompatibilities,thereby mitigating incompatibilities between control strategies on the estimation precision.Finally,we investigate the trade-offs in estimation precision among different parameters for different scenarios,revealing the inherent challenges in balancing the optimization of multiple parameters simultaneously and providing insights into the fundamental distinction between quantum single-parameter estimation and multi-parameter estimation.
基金the financial supports of the National Natural Science Foundation of China(No.52372200)a project supported by the State Key Laboratory of Mechanics and Control for Aerospace Structures(No.MCAS-S-0324G01)。
文摘Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).Conventional battery monitoring technologies struggle to track multiple physicochemical parameters in real time,hindering early hazard detection.Embedded optical fiber sensors have gained prominence as a transformative solution for next-generation smart battery sensing,owing to their micrometer size,multiplexing capability,and electromagnetic immunity.However,comprehensive reviews focusing on their advancements in operando multi-parameter monitoring remain scarce,despite their critical importance for ensuring battery safety.To address this gap,this review first introduces a classification and the fundamental principles of advanced battery-oriented optical fiber sensors.Subsequently,it summarizes recent developments in single-parameter battery monitoring using optical fiber sensors.Building on this foundation,this review presents the first comprehensive analysis of multifunctional optical fiber sensing platforms capable of simultaneously tracking temperature,strain,pressure,refractive index,and monitoring battery aging.Targeted strategies are proposed to facilitate the practical development of this technology,including optimization of sensor integration techniques,minimizing sensor invasiveness,resolving the cross-sensitivity of fiber Bragg grating(FBG)through structural innovation,enhancing techno-economics,and combining with artificial intelligence(AI).By aligning academic research with industry requirements,this review provides a methodological roadmap for developing robust optical sensing systems to ensure battery safety in decarbonization-driven applications.
文摘Under sensorless control, the position estimation error in interior permanent magnet(PM) synchronous machines will lead to parameter identification errors and a rank-deficiency issue. This paper proposes a parameter identification model that is independent of position error by combining the dq-axis voltage equations. Then, a novel dual signal alternate injection method is proposed to address the rank-deficiency issue, i.e., during one injection period, a zero, positive, and negative d-axis current injection together with a rotor position offset injection, to simultaneously identify the multi-parameters, including stator resistance, dq-axis inductances, and PM flux linkage. The proposed method is verified by experiments at different dq-axis current conditions.
基金supported by Science and Technology Project of State Grid Corporation Headquarters under Grant 5108-202218280A-2-170-XG(Development and Application of Power Time-Sensitive Network Switching Chip。
文摘The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving.To address these challenges,the concept of Time-sensitive networking(TSN)is proposed by IEEE 802.1TSN working group.In order to achieve low latency,Cyclic queuing and forwarding(CQF)mechanism is introduced to schedule Timetriggered(TT)flows.In this paper,we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling.The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows.Firstly,by K-means algorithm,the flows are initially partitioned into subsets based on their correlations.Subsequently,the flows within each subset are sorted by a new special criteria extracted from multiple features of flow.Finally,a flow offset selecting method based on load balance is used for resource mapping,so as to complete the process of flow scheduling.Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.
文摘The somatotopic representation of specific body parts is a well-established spatial organizational principle in the primary somatosensory and motor cortices.
基金supported by the National Natural Science Foundation of China(61172116)
文摘The main function of electronic support measure system is to detect threating signals in order to take countermeasures against them. To accomplish this objective, a process of associating each interleaved pulse with its emitter must be done. This process is termed sorting or de-interleaving. A novel point symmetry based radar sorting (PSBRS) algorithm is addressed. In order to deal with all kinds of radar signals, the symmetry measure distance is used to cluster pulses instead of the conventional Euclidean distance. The reference points of the symmetrical clusters are initialized by the alternative fuzzy c-means (AFCM) algorithm to ameliorate the effects of noise and the false sorting. Besides, the density filtering (DF) algorithm is proposed to discard the noise pulses or clutter. The performance of the algorithm is evaluated under the effects of noise and missing pulses. It has been observed that the PSBRS algorithm can cope with a large number of noise pulses and it is completely independent of missing pulses. Finally, PSBRS is compared with some benchmark algorithms, and the simulation results reveal the feasibility and efficiency of the algorithm.
基金Project supported by the National Basic Research Program of China (973 Program) (No. 2007CB714600)
文摘The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.
基金provided by the State Key Research Development Program of China (No.2016YFC0801403)Key Research Development Program of Jiangsu Provence (No.BE2015040)+1 种基金National Natural Science Foundation of China (Nos.51674253,51734009 and 51604270)Natural Science Foundation of Jiangsu Province (No.BK20171191)
文摘Rock bursts have become one of the most severe risks in underground coal mining and its early warning is an important component in the safety management. Microseismic(MS) monitoring is considered potentially as a powerful tool for the early warning of rock burst. In this study, an MS multi-parameter index system was established and the critical values of each index were estimated based on the normalized multi-information warning model of coal-rock dynamic failure. This index system includes bursting strain energy(BSE) index, time-space-magnitude independent information(TSMII) indices and timespace-magnitude compound information(TSMCI) indices. On the basis of this multi-parameter index system, a comprehensive analysis was conducted via introducing the R-value scoring method to calculate the weights of each index. To calibrate the multi-parameter index system and the associated comprehensive analysis, the weights of each index were first confirmed using historical MS data occurred in LW402102 of Hujiahe Coal Mine(China) over a period of four months. This calibrated comprehensive analysis of MS multi-parameter index system was then applied to pre-warn the occurrence of a subsequent rock burst incident in LW 402103. The results demonstrate that this multi-parameter index system combined with the comprehensive analysis are capable of quantitatively pre-warning rock burst risk.
基金supported by the National Science and Technology Support Program of China(No.2012BAC11B07)the Jiangxi Science and Technology Innovation Base Plan(No.20212BCD42017)。
文摘Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.
基金supported by the National Natural Science Foundation of China (60872108)the Postdoctoral Science Foundation of China(200902411+3 种基金20080430903)Heilongjiang Postdoctoral Financial Assistance (LBH-Z08129)the Scientific and Technological Creative Talents Special Research Foundation of Harbin Municipality (2008RFQXG030)Central University Basic Research Professional Expenses Special Fund Project
文摘A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting.
基金supported by the National Natural Science Foundation of China (Grant 81301291)the Beijing Higher Education Young Elite Teacher Project (Grant YETP1208)UNSW Special Research Grants Program
文摘The deterministic lateral displacement (DLD) is an important method used to sort particles and cells of different sizes. In this paper, the flexible cell sorting with the DLD method is studied by using a numerical model based on the immersed boundary-lattice Boltzmann method (IB-LBM). In this model, the fluid motion is solved by the LBM, and the cell membrane-fluid interaction is modeled with the LBM. The proposed model is validated by simulating the rigid particle sorted with the DLD method, and the results are found in good agreement with those measured in experiments. We first study the effect of flexibility on a single cell and multiple cells continuously going through a DLD device. It is found that the cell flexibility can significantly affect the cell path, which means the flexibility could have significant effects on the continuous cell sorting by the DLD method. The sorting characteristics of white blood cells and red blood cells are further studied by varying the spatial distribution of cylinder arrays and the initial cell-cell distance. The numerical results indicate that a well concentrated cell sorting can be obtained under a proper arrangement of cylinder arrays and a large enough initial cell-cell distance.
基金Supported by the National Science and Technology Supported Program of China(No.2011BAH24B05)
文摘Common sorting method have low sorting rates and is sensitive to the Signal-to-Noise Ratio(SNR),wavelet characteristics of Wigner-Ville distribution are applied to sort unknown complicated radar signal,high sorting accuracy can be got.The Wigner-Ville distribution of received signal is calculated,then it is predigested to two-dimensional characteristics.Using wavelet transformation to extract characteristics from two-dimensional of Wigner-Ville distribution,the best characteristics are selected to be used as sorting parameters.Experiment results demonstrated that the characteristics of eight typical radar emitter signals extracted by this method showed good performance of noise-resistance and clustering at large-scale SNR.
