The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fau...The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.展开更多
In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs a...In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.展开更多
Patent transfer has been regarded as an important channel for the nations and regions to acquire external technology,and also a direct research object to depict the relationship between supply and demand of technology...Patent transfer has been regarded as an important channel for the nations and regions to acquire external technology,and also a direct research object to depict the relationship between supply and demand of technology flow.Therefore,based on traceable patent transfer data,this article has established a dual-pipeline theoretical framework of transnational-domestic technology transfer from the interaction of the global and local(glocal)perspective,and combines social networks,GIS spatial analysis as well as spatial econometric model to discover the spatial evolution of China’s transnational technology channels and its determinant factors.It is found that:(1)The spatial heterogeneity of the overall network is significant while gradually weakened over time.(2)The eastward shift of the core cities involved in transnational technology channels is accelerating,from the hubs in North America(New York Bay Area,Silicon Valley,Caribbean offshore financial center,etc.)and West Europe(London offshore financial center etc.)to East Asia(Tokyo and Seoul)and Southeast Asia(Singapore),which illustrates China has decreased reliance on the technology from the USA and West Europe.(3)The four major innovation clusters:Beijing-Tianjin-Hebei region(Beijing as the hub),Yangtze River Delta(Shanghai as the hub),The Greater Bay Area(Shenzhen and Hong Kong as the hubs)and north Taiwan(Taipei and Hsinchu as the hubs),are regarded as global technology innovation hubs and China’s distribution centers in transnational technology flow.Among those,Chinese Hong Kong’s betweenness role of technology is strengthened due to linkage of transnational corporations and their branches,and low tax coverage of offshore finance,thus becoming the top city for technology transfer.Meanwhile,Chinese Taiwan’s core position is diminishing.(4)The breadth,intensity,and closeness of domestic technology transfer are conducive to the expansion of transnational technology import channels.Additionally,local economic level has positive effect on transnational technology transfer channels while technology strength and external economic linkage have multifaceted influences.展开更多
To enhance the fidelity and accuracy of the simulation of communication networks,hardware-in-the-loop（HITL） simulation was employed.HITL simulation methods was classified into three categories,of which the merits an...To enhance the fidelity and accuracy of the simulation of communication networks,hardware-in-the-loop（HITL） simulation was employed.HITL simulation methods was classified into three categories,of which the merits and shortages were compared.Combing system-in-the-loop（SITL） simulation principle with high level architecture（HLA）,an HITL simulation model of asynchronous transfer mode（ATM） network was constructed.The throughput and end-to-end delay of all-digital simulation and HITL simulation was analyzed,which showed that HITL simulation was more reliable and effectively improved the simulation credibility of communication network.Meanwhile,HLA-SITL method was fast and easy to achieve and low-cost during design lifecycle.Thus,it was a feasible way to research and analyze the large-scale network.展开更多
In this paper, an extended Kendall model for the priority scheduling input-line group output with multi-channel in Asynchronous Transfer Mode （ATM） exchange system is proposed and then the mean method is used to mod...In this paper, an extended Kendall model for the priority scheduling input-line group output with multi-channel in Asynchronous Transfer Mode （ATM） exchange system is proposed and then the mean method is used to model mathematically the non-typical non-anticipative PRiority service （PR） model. Compared with the typical and non-anticipative PR model, it expresses the characteristics of the priority scheduling input-line group output with multi-channel in ATM exchange system. The simulation experiment shows that this model can improve the HOL block and the performance of input-queued ATM switch network dramatically. This model has a better developing prospect in ATM exchange system.展开更多
Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
Recent efforts to add new services to the wide-band code division multiple accesses （WCDMA） system have increased interest in network processor （NP）-based routers that are easy to extend and evolve. In this paper,...Recent efforts to add new services to the wide-band code division multiple accesses （WCDMA） system have increased interest in network processor （NP）-based routers that are easy to extend and evolve. In this paper, an application of NPs in routing engine module （REM） of radio network controller （RNC） in WCDMA system is proposed. The measuring results show that NPs have good performance and efficiency in routing traffic of the communication network and the simulation verifies the fast forwarding function of NPs.展开更多
The conventional transfer matrix models of fluid elements were modified and a convenient method of dealing with junction boundary conditions was introduced. A large scale fluid network was modeled by standard procedur...The conventional transfer matrix models of fluid elements were modified and a convenient method of dealing with junction boundary conditions was introduced. A large scale fluid network was modeled by standard procedures, and a network was expressed with characteristic matrix and boundary condition matrix. By simple operation of matrix, the dynamic characteristics of a large scale fluid network was simulated in frequency domain. Validation test on a large scale pipeline network showed that the proposed method is accurate and practical.[展开更多
With the increase of Beijing urban rail transport network, the structure of the road network is becoming more complex, and passengers have more travel options. Together with the complex paths and different timetables,...With the increase of Beijing urban rail transport network, the structure of the road network is becoming more complex, and passengers have more travel options. Together with the complex paths and different timetables, taking the last train is becoming much more difficult and unsuccessful. To avoid losses, we propose feasible suggestions to the last train with reasonable selling tickets system.展开更多
Lanthanide-doped upconversion nanoparticles enable upconversion stimulated emission depletion microscopy with high photostability and low-intensity near-infrared continuous-wave lasers.Controlling energy transfer dyna...Lanthanide-doped upconversion nanoparticles enable upconversion stimulated emission depletion microscopy with high photostability and low-intensity near-infrared continuous-wave lasers.Controlling energy transfer dynamics in these nanoparticles is crucial for super-resolution microscopy with minimal laser intensities and high photon budgets.However,traditional methods neglect the spatial distribution of lanthanide ions and its effect on energy transfer dynamics.Here,we introduce topology-driven energy transfer networks in lanthanide-doped upconversion nanoparticles for upconversion stimulated emission depletion microscopy with reduced laser intensities,maintaining a high photon budget.Spatial separation of Yb^(3+)sensitizers and Tm^(3+)emitters in 50-nm core-shell nanoparticles enhance energy transfer dynamics for super-resolution microscopy.Topology-dependent energy migration produces strong 450-nm upconversion luminescence under low-power 980-nm excitation.Enhanced cross-relaxation improves optical switching efficiency,achieving a saturation intensity of 0.06 MW cm^(−2) under excitation at 980 nm and depletion at 808 nm.Super-resolution imaging with a 65-nm lateral resolution is achieved using intensities of 0.03 MW cm^(−2) for a Gaussian-shaped excitation laser at 980 nm and 1 MW cm^(−2) for a donut-shaped depletion laser at 808 nm,representing a 10-fold reduction in excitation intensity and a 3-fold reduction in depletion intensity compared to conventional methods.These findings demonstrate the potential of harnessing topology-dependent energy transfer dynamics in upconversion nanoparticles for advancing low-power super-resolution applications.展开更多
文摘The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.
基金Supported by National Natural Science Foundation of P. R. China （60574083）, Key Laboratory of Process Industry Automation, State Education Ministry of China （PAL200514）
文摘In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.
基金Major Project of National Social Science Foundation of China,No.21ZDA011。
文摘Patent transfer has been regarded as an important channel for the nations and regions to acquire external technology,and also a direct research object to depict the relationship between supply and demand of technology flow.Therefore,based on traceable patent transfer data,this article has established a dual-pipeline theoretical framework of transnational-domestic technology transfer from the interaction of the global and local(glocal)perspective,and combines social networks,GIS spatial analysis as well as spatial econometric model to discover the spatial evolution of China’s transnational technology channels and its determinant factors.It is found that:(1)The spatial heterogeneity of the overall network is significant while gradually weakened over time.(2)The eastward shift of the core cities involved in transnational technology channels is accelerating,from the hubs in North America(New York Bay Area,Silicon Valley,Caribbean offshore financial center,etc.)and West Europe(London offshore financial center etc.)to East Asia(Tokyo and Seoul)and Southeast Asia(Singapore),which illustrates China has decreased reliance on the technology from the USA and West Europe.(3)The four major innovation clusters:Beijing-Tianjin-Hebei region(Beijing as the hub),Yangtze River Delta(Shanghai as the hub),The Greater Bay Area(Shenzhen and Hong Kong as the hubs)and north Taiwan(Taipei and Hsinchu as the hubs),are regarded as global technology innovation hubs and China’s distribution centers in transnational technology flow.Among those,Chinese Hong Kong’s betweenness role of technology is strengthened due to linkage of transnational corporations and their branches,and low tax coverage of offshore finance,thus becoming the top city for technology transfer.Meanwhile,Chinese Taiwan’s core position is diminishing.(4)The breadth,intensity,and closeness of domestic technology transfer are conducive to the expansion of transnational technology import channels.Additionally,local economic level has positive effect on transnational technology transfer channels while technology strength and external economic linkage have multifaceted influences.
基金Supported by the National Natural Science Foundation of China (61101129)Specialized Research Fund for the Doctoral Program of Higher Education(20091101110019)
文摘To enhance the fidelity and accuracy of the simulation of communication networks,hardware-in-the-loop（HITL） simulation was employed.HITL simulation methods was classified into three categories,of which the merits and shortages were compared.Combing system-in-the-loop（SITL） simulation principle with high level architecture（HLA）,an HITL simulation model of asynchronous transfer mode（ATM） network was constructed.The throughput and end-to-end delay of all-digital simulation and HITL simulation was analyzed,which showed that HITL simulation was more reliable and effectively improved the simulation credibility of communication network.Meanwhile,HLA-SITL method was fast and easy to achieve and low-cost during design lifecycle.Thus,it was a feasible way to research and analyze the large-scale network.
文摘In this paper, an extended Kendall model for the priority scheduling input-line group output with multi-channel in Asynchronous Transfer Mode （ATM） exchange system is proposed and then the mean method is used to model mathematically the non-typical non-anticipative PRiority service （PR） model. Compared with the typical and non-anticipative PR model, it expresses the characteristics of the priority scheduling input-line group output with multi-channel in ATM exchange system. The simulation experiment shows that this model can improve the HOL block and the performance of input-queued ATM switch network dramatically. This model has a better developing prospect in ATM exchange system.
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
文摘Recent efforts to add new services to the wide-band code division multiple accesses （WCDMA） system have increased interest in network processor （NP）-based routers that are easy to extend and evolve. In this paper, an application of NPs in routing engine module （REM） of radio network controller （RNC） in WCDMA system is proposed. The measuring results show that NPs have good performance and efficiency in routing traffic of the communication network and the simulation verifies the fast forwarding function of NPs.
文摘The conventional transfer matrix models of fluid elements were modified and a convenient method of dealing with junction boundary conditions was introduced. A large scale fluid network was modeled by standard procedures, and a network was expressed with characteristic matrix and boundary condition matrix. By simple operation of matrix, the dynamic characteristics of a large scale fluid network was simulated in frequency domain. Validation test on a large scale pipeline network showed that the proposed method is accurate and practical.[
文摘With the increase of Beijing urban rail transport network, the structure of the road network is becoming more complex, and passengers have more travel options. Together with the complex paths and different timetables, taking the last train is becoming much more difficult and unsuccessful. To avoid losses, we propose feasible suggestions to the last train with reasonable selling tickets system.
基金supported by the National Key Research and Development program of China(Grant No.2022YFB2804301 and Grant No.2021YFB2802000)the Science and Technology Commission of Shanghai Municipality(Grant No.21DZ1100500)+3 种基金the Shanghai Municipal Science and Technology Major Project,the Shanghai Frontiers Science Center Program(2021-2025 No.20)the National Natural Science Foundation of China(Grant No.61975123 and Grant No.62205208)the China Postdoctoral Science Foundation(3722904001,3722904006)the Shanghai Super Postdoctoral Incentive Scheme(5B22904002,5B22904006).
文摘Lanthanide-doped upconversion nanoparticles enable upconversion stimulated emission depletion microscopy with high photostability and low-intensity near-infrared continuous-wave lasers.Controlling energy transfer dynamics in these nanoparticles is crucial for super-resolution microscopy with minimal laser intensities and high photon budgets.However,traditional methods neglect the spatial distribution of lanthanide ions and its effect on energy transfer dynamics.Here,we introduce topology-driven energy transfer networks in lanthanide-doped upconversion nanoparticles for upconversion stimulated emission depletion microscopy with reduced laser intensities,maintaining a high photon budget.Spatial separation of Yb^(3+)sensitizers and Tm^(3+)emitters in 50-nm core-shell nanoparticles enhance energy transfer dynamics for super-resolution microscopy.Topology-dependent energy migration produces strong 450-nm upconversion luminescence under low-power 980-nm excitation.Enhanced cross-relaxation improves optical switching efficiency,achieving a saturation intensity of 0.06 MW cm^(−2) under excitation at 980 nm and depletion at 808 nm.Super-resolution imaging with a 65-nm lateral resolution is achieved using intensities of 0.03 MW cm^(−2) for a Gaussian-shaped excitation laser at 980 nm and 1 MW cm^(−2) for a donut-shaped depletion laser at 808 nm,representing a 10-fold reduction in excitation intensity and a 3-fold reduction in depletion intensity compared to conventional methods.These findings demonstrate the potential of harnessing topology-dependent energy transfer dynamics in upconversion nanoparticles for advancing low-power super-resolution applications.
