In order to improve the comprehensive defense capability of data security in digital twins(DTs),an information security interaction architecture is proposed in this paper to solve the inadequacy of data protection and...In order to improve the comprehensive defense capability of data security in digital twins(DTs),an information security interaction architecture is proposed in this paper to solve the inadequacy of data protection and transmission mechanism at present.Firstly,based on the advanced encryption standard(AES)encryption,we use the keystore to expand the traditional key,and use the digital pointer to avoid the key transmission in a wireless channel.Secondly,the identity authentication technology is adopted to ensure the data integrity,and an automatic retransmission mechanism is added for the endogenous properties of the wireless channel.Finally,the software defined radio(SDR)platform composed of universal software radio peripheral(USRP)and GNU radio is used to simulate the data interaction between the physical entity and the virtual entity.The numerical results show that the DTs architecture can guarantee the encrypted data transmitted completely and decrypted accurately with high efficiency and reliability,thus providing a basis for intelligent and secure information interaction for DTs in the future.展开更多
This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-repor...This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-reported scores. The ARIMA and LSTM models were trained using Wordle data from Twitter between 7th January 2022 and 31st December 2022. User-reported scores were predicted using evaluation metrics such as MSE, RMSE, R2, and MAE. Various regression models, including XG-Boost and Random Forest, were used to conduct comparison experiments. The MSE, RMSE, R2, and MAE values for the ARIMA(0,1,1) and LSTM models are 0.000, 0.010, 0.998, and 0.006, and 0.000, 0.024, 0.987, and 0.013, respectively. The results indicate that the ARIMA model is more suitable for predicting Wordle user scores than the LSTM model.展开更多
Search engine is an effective approach to promote the service quality of the World Wide Web. On terms of the analysis of search engines at home and abroad, the developing principle of search engines is given according...Search engine is an effective approach to promote the service quality of the World Wide Web. On terms of the analysis of search engines at home and abroad, the developing principle of search engines is given according to the requirement of Web information for chemical fiber engineering. The implementation method for the communication and dynamic refreshment of information on home page of the search engines are elaborated by using programming technology of Active Server Page 3.0 (ASP3.0). The query of chemical fiber information and automatic linking of chemical fiber Web sites can be easily realized by the developed search engine under Internet environment according to users' requirement.展开更多
In a network described by a graph,only topological structure information is considered to determine how the nodes are connected by edges.Non-topological information denotes that which cannot be determined directly fro...In a network described by a graph,only topological structure information is considered to determine how the nodes are connected by edges.Non-topological information denotes that which cannot be determined directly from topological information.This paper shows,by a simple example where scientists in three research groups and one external group form four communities,that in some real world networks non-topological information (in this example,the research group affiliation) dominates community division.If the information has some influence on the network topological structure,the question arises as to how to find a suitable algorithm to identify the communities based only on the network topology.We show that weighted Newman algorithm may be the best choice for this example.We believe that this idea is general for real-world complex networks.展开更多
Exploiting the source-to-relay channel phase information at the relays can increase the rate upper-bound of distributed orthogonal space-time block codes(STBC)from 2/K to 1/2,where Kis the number of relays.This techni...Exploiting the source-to-relay channel phase information at the relays can increase the rate upper-bound of distributed orthogonal space-time block codes(STBC)from 2/K to 1/2,where Kis the number of relays.This technique is known as distributed orthogonal space-time block codes with channel phase information(DOSTBC-CPI).However,the decoding delay of existing DOSTBC-CPIs is not optimal.Therefore,based on the rate of 1/2 balanced complex orthogonal design(COD),an algorithm is provided to construct a maximal rate DOSTBC-CPI with only half the decoding delay of existing DOSTBC-CPI.Simulation results show that the proposed method exhibits lower symbol error rate than the existing DOSTBC-CPIs.展开更多
The student-oriented concept is a kind of people orientation. It emphasizes respect and support for students. It is student-centered,and is for all students. At the school level,the guiding principles of student orien...The student-oriented concept is a kind of people orientation. It emphasizes respect and support for students. It is student-centered,and is for all students. At the school level,the guiding principles of student orientation and respecting teachers and teaching must be adhered to,and corresponding reforms in the school's system,policies and environment needs to be carried out. At the university teacher level,the concept of student orientation must be adhered to in teaching,and it is regarded as the guiding concept of teaching and education and is implemented in classroom teaching and practical teaching. At the level of college administrator,the concept of student orientation must be adhered in the course of daily management and service to do better management,service and education work. In order to better implement the student-oriented concept and promote the growth and development of college students,we needs to build a mentor team for college students' growth based on political workers,establish a team of academic tutors for college students based on professional teachers and build a platform for cooperation between schools and enterprises. The innovative training mode can promote students to grow into talents.展开更多
In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in ord...In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in order to avoid directly solving a large-scale nonlinear optimization problem.We select photovoltaic inverters as agents to adjust system voltage in a distribution network,taking the reactive power output of inverters as action variables.An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment.OPENDSS is used to output system node voltage and network loss.This method realizes the goal of optimal VVC in distribution network.The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm.Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.展开更多
The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open ...The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.展开更多
Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with diffe...Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The results suggested that 1)albacore tuna fishing ground can be predicted by LSTM;2)the VMS records the data in detail and can be used scientifically to calculate the CPUE;3)correlation analysis,and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model;4)the spatial resolution should be 1°×1°in the forecast of albacore tuna fishing ground in waters near the Cook Islands.展开更多
Magnetic skyrmions, with topologically protected particle-like magnetization configurations, are promising information carriers for future spintronics devices with ultralow energy consumption. Generally, during motion...Magnetic skyrmions, with topologically protected particle-like magnetization configurations, are promising information carriers for future spintronics devices with ultralow energy consumption. Generally, during motion, skyrmions suffer from the skyrmion Hall effect(Sk HE) wherein the skyrmions deflect away from the intended path of the driving force.Numerous methods have been proposed to avoid this detrimental effect. In this study, we propose controllable alternating current(AC)-driven skyrmion propagation in a ferromagnetic nanowire based on combination of gate-voltage-controlled magnetic anisotropy(VCMA) and Sk HE. Micromagnetic simulations show that a skyrmion oscillatory closed-loop-like in situ motion driven by AC can be transformed into directional ratchet-like propagation along the nanotrack by creating a VCMA-gate barrier. Additionally, we show that the skyrmion propagation conditions depend on the gate barrier potential and driving AC parameters, and they can be used for the optimal design of nanotrack devices. Moreover, this mechanism could be used to control skyrmion macroscopic propagation directions by dynamically alternating the voltage of another series of gates. We further show the dynamic control of the long-distance propagation of skyrmions along with the pinning state. The study results provide a promising route for designing future skyrmion-based spintronics logical and memory devices.展开更多
With the rapid and wide deployment of renewable energy,the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance.The output power of renewable energy is...With the rapid and wide deployment of renewable energy,the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance.The output power of renewable energy is uncertain,and thus flexible regulation for the power balance is highly demanded.Considering the multi-timescale output characteristics of renewable energy,a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper.Through the multi-timescale decomposition algorithm on the basis of mathematical morphology,the multi-timescale components are separated to determine the flexibility requirements on different timescales.Based on the obtained flexibility requirements,a multi-timescale energy resources deployment model based on bi-level optimization is established considering the economic performance and the flexibility of system operation.This optimization model can allocate corresponding flexibility resources according to the economy,flexibility and reliability requirements of the power system,and achieve the trade-off between them.Finally,case studies demonstrate the effectiveness of our model and method.展开更多
As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data s...As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain.Given the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope.Based on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’perspectives.The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other issues.After the upload test,the average upload speed reaches 80.6 M/s.The average download speed of the system is 88.7 M/s after the download test.The multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 ms.The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.展开更多
Experimental and theoretical studies have reported that the precise firing of neurons is crucial for sensory representation.Autapse serves as a special synapse connecting neuron and itself,which has also been found to...Experimental and theoretical studies have reported that the precise firing of neurons is crucial for sensory representation.Autapse serves as a special synapse connecting neuron and itself,which has also been found to improve the accuracy of neuronal response.In current work,the effect of autaptic delay signal on the spike-timing precision is investigated on a single autaptic Hodgkin–Huxley neuron in the present of noise.The simulation results show that both excitatory and inhibitory autaptic signals can effectively adjust the precise spike time of neurons with noise by choosing the appropriate coupling strength g and time delay of autaptic signalτ.The g–τparameter space is divided into two regions:one is the region where the spike-timing precision is effectively regulated;the other is the region where the neuronal firing is almost not regulated.For the excitatory and inhibitory autapse,the range of parameters causing the accuracy of neuronal firing is different.Moreover,it is also found that the mechanisms of the spike-timing precision regulation are different for the two kinds of autaptic signals.展开更多
An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering met...An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.展开更多
In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a cust...In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a customer churn prediction framework based on situational awareness and integrating customer attributes,the impact of project hotspots on customer interests,and customer satisfaction with the project has been built.This framework introduces the background factors in the financial customer environment,and further discusses the relationship between customers,the background of customers and the characteristics of pre-lost customers.The improved Singular Value Decomposition(SVD)algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers,and the key index combination is screened to obtain the data of potential lost customers.The framework will change with time according to the customer’s interest,adding the time factor to the customer churn prediction,and improving the dimensionality reduction and prediction generalization ability in feature selection.Logistic regression,naive Bayes and decision tree are used to establish a prediction model in the experiment,and it is compared with the financial customer churn prediction framework under situational awareness.The prediction results of the framework are evaluated from four aspects:accuracy,accuracy,recall rate and F-measure.The experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends,so as to obtain potential customer data with high churn probability,and then these data can be transmitted to the company’s customer service department in time,so as to improve customer churn rate and customer loyalty through accurate service.展开更多
Apple disease samples were collected from the southern Xinjiang and annotated to design a convolutional neural network model based on deep learning.The accuracy and robustness of the model was improved through trainin...Apple disease samples were collected from the southern Xinjiang and annotated to design a convolutional neural network model based on deep learning.The accuracy and robustness of the model was improved through training and optimization algorithms,and a complete apple disease identification system was developed with the model as the core,and evaluated for its performance in terms of accuracy,recall rate and speed.This study provides a reliable AI-based apple disease diagnosis solution for the apple planting industry in the southern Xinjiang,hoping to help farmers better manage and protect crop health.展开更多
Mach number is a key metric in the evaluation of wind tunnel flow field performance.This complex process of wind tunnel test mainly has the problems of nonlinearity and time lag.In order to overcome the problems and c...Mach number is a key metric in the evaluation of wind tunnel flow field performance.This complex process of wind tunnel test mainly has the problems of nonlinearity and time lag.In order to overcome the problems and control the Mach number stability,this paper proposes a new method of Mach number prediction based on a nonlinear autoregressive exogenous-genetic algorithm-Elman(NARX-GA-Elman)model,which adopts NARX as the basic framework,determines the order of the input variables by using the false nearest neighbor(FNN),and uses the dynamic nonlinear network Elman to fit the model,and finally uses the global optimization algorithm GA to optimize the weight thresholds in the model to establish the Mach number prediction model with optimal performance under single working condition.By comparing with the traditional algorithm,the prediction accuracy of the model is improved by 61.5%,and the control accuracy is improved by 55.7%,which demonstrates that the model has very high prediction accuracy and good stability performance.展开更多
Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images wit...Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.展开更多
With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This ...With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection(Skip-DSCGAN)for fall detection.The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data.A semisupervised learning approach is adopted to train the model using only activities of daily living(ADL)data,which can avoid data imbalance problems.Furthermore,a quantile-based approach is employed to determine the fall threshold,which makes the fall detection frameworkmore robust.This proposed fall detection framework is evaluated against four other generative adversarial network(GAN)models with superior anomaly detection performance using two fall public datasets(SisFall&MobiAct).The test results show that the proposed method achieves better results,reaching 96.93% and 92.75% accuracy on the above two test datasets,respectively.At the same time,the proposed method also achieves satisfactory results in terms ofmodel size and inference delay time,making it suitable for deployment on wearable devices with limited resources.In addition,this paper also compares GAN-based semisupervised learning methods with supervised learning methods commonly used in fall detection.It clarifies the advantages of GAN-based semisupervised learning methods in fall detection.展开更多
基金supported in part by the Intergovernmental International Cooperation in Science and Technology Innovation Program under Grants 2019YFE0111600in part by National Natural Science Foundation of China under Grants 62122069,62072490,62201507,and 62071431+2 种基金in part by Science and Technology Development Fund of Macao SAR under Grants 0060/2019/A1 and 0162/2019/A3in part by FDCT-MOST Joint Project under Grant 0066/2019/AMJin part by Research Grant of University of Macao under Grant MYRG2020-00107IOTSC。
文摘In order to improve the comprehensive defense capability of data security in digital twins(DTs),an information security interaction architecture is proposed in this paper to solve the inadequacy of data protection and transmission mechanism at present.Firstly,based on the advanced encryption standard(AES)encryption,we use the keystore to expand the traditional key,and use the digital pointer to avoid the key transmission in a wireless channel.Secondly,the identity authentication technology is adopted to ensure the data integrity,and an automatic retransmission mechanism is added for the endogenous properties of the wireless channel.Finally,the software defined radio(SDR)platform composed of universal software radio peripheral(USRP)and GNU radio is used to simulate the data interaction between the physical entity and the virtual entity.The numerical results show that the DTs architecture can guarantee the encrypted data transmitted completely and decrypted accurately with high efficiency and reliability,thus providing a basis for intelligent and secure information interaction for DTs in the future.
文摘This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-reported scores. The ARIMA and LSTM models were trained using Wordle data from Twitter between 7th January 2022 and 31st December 2022. User-reported scores were predicted using evaluation metrics such as MSE, RMSE, R2, and MAE. Various regression models, including XG-Boost and Random Forest, were used to conduct comparison experiments. The MSE, RMSE, R2, and MAE values for the ARIMA(0,1,1) and LSTM models are 0.000, 0.010, 0.998, and 0.006, and 0.000, 0.024, 0.987, and 0.013, respectively. The results indicate that the ARIMA model is more suitable for predicting Wordle user scores than the LSTM model.
基金Sponsored by China postdoctoral Science Foundation(No:200031)
文摘Search engine is an effective approach to promote the service quality of the World Wide Web. On terms of the analysis of search engines at home and abroad, the developing principle of search engines is given according to the requirement of Web information for chemical fiber engineering. The implementation method for the communication and dynamic refreshment of information on home page of the search engines are elaborated by using programming technology of Active Server Page 3.0 (ASP3.0). The query of chemical fiber information and automatic linking of chemical fiber Web sites can be easily realized by the developed search engine under Internet environment according to users' requirement.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.70671089 and 10635040)
文摘In a network described by a graph,only topological structure information is considered to determine how the nodes are connected by edges.Non-topological information denotes that which cannot be determined directly from topological information.This paper shows,by a simple example where scientists in three research groups and one external group form four communities,that in some real world networks non-topological information (in this example,the research group affiliation) dominates community division.If the information has some influence on the network topological structure,the question arises as to how to find a suitable algorithm to identify the communities based only on the network topology.We show that weighted Newman algorithm may be the best choice for this example.We believe that this idea is general for real-world complex networks.
基金supported in part by the National Natural Science Foundation of China(Nos.61271230,61472190)the National Mobile Communications Research Laboratory,Southeast University(No.2013D02)
文摘Exploiting the source-to-relay channel phase information at the relays can increase the rate upper-bound of distributed orthogonal space-time block codes(STBC)from 2/K to 1/2,where Kis the number of relays.This technique is known as distributed orthogonal space-time block codes with channel phase information(DOSTBC-CPI).However,the decoding delay of existing DOSTBC-CPIs is not optimal.Therefore,based on the rate of 1/2 balanced complex orthogonal design(COD),an algorithm is provided to construct a maximal rate DOSTBC-CPI with only half the decoding delay of existing DOSTBC-CPI.Simulation results show that the proposed method exhibits lower symbol error rate than the existing DOSTBC-CPIs.
文摘The student-oriented concept is a kind of people orientation. It emphasizes respect and support for students. It is student-centered,and is for all students. At the school level,the guiding principles of student orientation and respecting teachers and teaching must be adhered to,and corresponding reforms in the school's system,policies and environment needs to be carried out. At the university teacher level,the concept of student orientation must be adhered to in teaching,and it is regarded as the guiding concept of teaching and education and is implemented in classroom teaching and practical teaching. At the level of college administrator,the concept of student orientation must be adhered in the course of daily management and service to do better management,service and education work. In order to better implement the student-oriented concept and promote the growth and development of college students,we needs to build a mentor team for college students' growth based on political workers,establish a team of academic tutors for college students based on professional teachers and build a platform for cooperation between schools and enterprises. The innovative training mode can promote students to grow into talents.
基金supported by the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.under Grant B311JY21000A。
文摘In this paper,a model free volt/var control(VVC)algorithm is developed by using deep reinforcement learning(DRL).We transform the VVC problem of distribution networks into the network framework of PPO algorithm,in order to avoid directly solving a large-scale nonlinear optimization problem.We select photovoltaic inverters as agents to adjust system voltage in a distribution network,taking the reactive power output of inverters as action variables.An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment.OPENDSS is used to output system node voltage and network loss.This method realizes the goal of optimal VVC in distribution network.The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm.Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.
基金supported by the National Natural Science Foundation of China (62173303)the Fundamental Research for the Zhejiang P rovincial Universities (RF-C2020003)。
文摘The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.
基金the National Natural Science Foundation of China(No.32273185)the National Key R&D Program of China(No.2020YFD0901205)the Marine Fishery Resources Investigation and Exploration Program of the Ministry of Agriculture and Rural Affairs of China in 2021(No.D-8006-21-0215)。
文摘Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The results suggested that 1)albacore tuna fishing ground can be predicted by LSTM;2)the VMS records the data in detail and can be used scientifically to calculate the CPUE;3)correlation analysis,and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model;4)the spatial resolution should be 1°×1°in the forecast of albacore tuna fishing ground in waters near the Cook Islands.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51902300,11972333,and 11902316)the Natural Science Foundation of Zhejiang Province,China(Grant Nos.LY21F010011,LZ19A020001,and LZ23A020002)the Fundamental Research Funds for the Provincial Universities of Zhejiang(Grant Nos.2021YW02 and 2022YW88)。
文摘Magnetic skyrmions, with topologically protected particle-like magnetization configurations, are promising information carriers for future spintronics devices with ultralow energy consumption. Generally, during motion, skyrmions suffer from the skyrmion Hall effect(Sk HE) wherein the skyrmions deflect away from the intended path of the driving force.Numerous methods have been proposed to avoid this detrimental effect. In this study, we propose controllable alternating current(AC)-driven skyrmion propagation in a ferromagnetic nanowire based on combination of gate-voltage-controlled magnetic anisotropy(VCMA) and Sk HE. Micromagnetic simulations show that a skyrmion oscillatory closed-loop-like in situ motion driven by AC can be transformed into directional ratchet-like propagation along the nanotrack by creating a VCMA-gate barrier. Additionally, we show that the skyrmion propagation conditions depend on the gate barrier potential and driving AC parameters, and they can be used for the optimal design of nanotrack devices. Moreover, this mechanism could be used to control skyrmion macroscopic propagation directions by dynamically alternating the voltage of another series of gates. We further show the dynamic control of the long-distance propagation of skyrmions along with the pinning state. The study results provide a promising route for designing future skyrmion-based spintronics logical and memory devices.
基金supported by the NationalNatural Science Foundation of China(Grant No.52107129).
文摘With the rapid and wide deployment of renewable energy,the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance.The output power of renewable energy is uncertain,and thus flexible regulation for the power balance is highly demanded.Considering the multi-timescale output characteristics of renewable energy,a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper.Through the multi-timescale decomposition algorithm on the basis of mathematical morphology,the multi-timescale components are separated to determine the flexibility requirements on different timescales.Based on the obtained flexibility requirements,a multi-timescale energy resources deployment model based on bi-level optimization is established considering the economic performance and the flexibility of system operation.This optimization model can allocate corresponding flexibility resources according to the economy,flexibility and reliability requirements of the power system,and achieve the trade-off between them.Finally,case studies demonstrate the effectiveness of our model and method.
基金supported in part by the Advanced and High-level Discipline Construction Fund of Universities in Beijing(No.3201023)in part by the Beijing Electronic Science and Technology Institute of Basic Research Funds Outstanding Master Training Project(No.328202233)in part by the National First-class Undergraduate Discipline Construction of”Communication Engineering”and“Electronic Information Engineering,”and in part by the National Cryptography Science Foundation of China.
文摘As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain.Given the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope.Based on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’perspectives.The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other issues.After the upload test,the average upload speed reaches 80.6 M/s.The average download speed of the system is 88.7 M/s after the download test.The multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 ms.The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.
基金the Fundamental Research Funds for the Central Universities,China(Grant No.GK201903020)the National Natural Science Foundation of China(Grant No.12005006)Scientific research project of Education Department of Gansu Province,China(Grant No.2016A-049).
文摘Experimental and theoretical studies have reported that the precise firing of neurons is crucial for sensory representation.Autapse serves as a special synapse connecting neuron and itself,which has also been found to improve the accuracy of neuronal response.In current work,the effect of autaptic delay signal on the spike-timing precision is investigated on a single autaptic Hodgkin–Huxley neuron in the present of noise.The simulation results show that both excitatory and inhibitory autaptic signals can effectively adjust the precise spike time of neurons with noise by choosing the appropriate coupling strength g and time delay of autaptic signalτ.The g–τparameter space is divided into two regions:one is the region where the spike-timing precision is effectively regulated;the other is the region where the neuronal firing is almost not regulated.For the excitatory and inhibitory autapse,the range of parameters causing the accuracy of neuronal firing is different.Moreover,it is also found that the mechanisms of the spike-timing precision regulation are different for the two kinds of autaptic signals.
基金supported by the National Natural Science Foundation of China under Grant 51777193.
文摘An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.
基金This work was supported by Shandong social science planning and research project in 2021(No.21CPYJ40).
文摘In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a customer churn prediction framework based on situational awareness and integrating customer attributes,the impact of project hotspots on customer interests,and customer satisfaction with the project has been built.This framework introduces the background factors in the financial customer environment,and further discusses the relationship between customers,the background of customers and the characteristics of pre-lost customers.The improved Singular Value Decomposition(SVD)algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers,and the key index combination is screened to obtain the data of potential lost customers.The framework will change with time according to the customer’s interest,adding the time factor to the customer churn prediction,and improving the dimensionality reduction and prediction generalization ability in feature selection.Logistic regression,naive Bayes and decision tree are used to establish a prediction model in the experiment,and it is compared with the financial customer churn prediction framework under situational awareness.The prediction results of the framework are evaluated from four aspects:accuracy,accuracy,recall rate and F-measure.The experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends,so as to obtain potential customer data with high churn probability,and then these data can be transmitted to the company’s customer service department in time,so as to improve customer churn rate and customer loyalty through accurate service.
基金Supported by Bingtuan Science and Technology Program(2021DB0012021BB023)Science and Technology Innovation Team of Tarim University(TDZKCX202102).
文摘Apple disease samples were collected from the southern Xinjiang and annotated to design a convolutional neural network model based on deep learning.The accuracy and robustness of the model was improved through training and optimization algorithms,and a complete apple disease identification system was developed with the model as the core,and evaluated for its performance in terms of accuracy,recall rate and speed.This study provides a reliable AI-based apple disease diagnosis solution for the apple planting industry in the southern Xinjiang,hoping to help farmers better manage and protect crop health.
基金funded by the National Natural Science Foundation of China(No.61503069)the Fundamental Research Funds for the Central Universities(N150404020).
文摘Mach number is a key metric in the evaluation of wind tunnel flow field performance.This complex process of wind tunnel test mainly has the problems of nonlinearity and time lag.In order to overcome the problems and control the Mach number stability,this paper proposes a new method of Mach number prediction based on a nonlinear autoregressive exogenous-genetic algorithm-Elman(NARX-GA-Elman)model,which adopts NARX as the basic framework,determines the order of the input variables by using the false nearest neighbor(FNN),and uses the dynamic nonlinear network Elman to fit the model,and finally uses the global optimization algorithm GA to optimize the weight thresholds in the model to establish the Mach number prediction model with optimal performance under single working condition.By comparing with the traditional algorithm,the prediction accuracy of the model is improved by 61.5%,and the control accuracy is improved by 55.7%,which demonstrates that the model has very high prediction accuracy and good stability performance.
基金supported in part by the National Natural Science Foundation of China under Grant 62062061/in part by the Major Project Cultivation Fund of Xizang Minzu University under Grant 324112300447.
文摘Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.
基金supported partly by the Natural Science Foundation of Zhejiang Province,China(LGF21F020017).
文摘With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection(Skip-DSCGAN)for fall detection.The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data.A semisupervised learning approach is adopted to train the model using only activities of daily living(ADL)data,which can avoid data imbalance problems.Furthermore,a quantile-based approach is employed to determine the fall threshold,which makes the fall detection frameworkmore robust.This proposed fall detection framework is evaluated against four other generative adversarial network(GAN)models with superior anomaly detection performance using two fall public datasets(SisFall&MobiAct).The test results show that the proposed method achieves better results,reaching 96.93% and 92.75% accuracy on the above two test datasets,respectively.At the same time,the proposed method also achieves satisfactory results in terms ofmodel size and inference delay time,making it suitable for deployment on wearable devices with limited resources.In addition,this paper also compares GAN-based semisupervised learning methods with supervised learning methods commonly used in fall detection.It clarifies the advantages of GAN-based semisupervised learning methods in fall detection.
