With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not onl...With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not only a technological leap forward but also a major trend shaping the future of global connectivity.As a layered heterogeneous network,NTN integrates multiple aerial platforms—including satellites,high-altitude platform systems(HAPS),and unmanned aerial systems(UAS)—to provide flexible and composable solutions aimed at achieving seamless worldwide communication coverage.展开更多
In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,w...In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.展开更多
A layered network model for optical transport networks is proposed in this paper,which involves Internet Protocol(IP) ,Synchronous Digital Hierarchy(SDH) and Wavelength Division Mul-tiplexing(WDM) layers. The strategy...A layered network model for optical transport networks is proposed in this paper,which involves Internet Protocol(IP) ,Synchronous Digital Hierarchy(SDH) and Wavelength Division Mul-tiplexing(WDM) layers. The strategy of Dynamic Joint Routing and Resource Allocation(DJRRA) and its algorithm description are also presented for the proposed layered network model. DJRRA op-timizes the bandwidth usage of interface links between different layers and the logic links inside all layers. The simulation results show that DJRRA can reduce the blocking probability and increase network throughput effectively,which is in contrast to the classical separate sequential routing and resource allocation solutions.展开更多
Recent advancements in wireless communications have allowed the birth of novel wireless sensor networks(WSN).A sensor network comprises several micro-sensors deployed randomly in an area of interest.A micro-sensor is ...Recent advancements in wireless communications have allowed the birth of novel wireless sensor networks(WSN).A sensor network comprises several micro-sensors deployed randomly in an area of interest.A micro-sensor is provided with an energy resource to supply electricity to all of its components.However,the disposed energy resource is limited and battery replacement is generally infeasible.With this restriction,the sensors must conserve energy to prolong their lifetime.Various energy conservation strategies for WSNs have been presented in the literature,from the application to the physical layer.Most of these solutions focus only on optimizing a single layer in terms of energy consumption.In this research,a novel cross-layer technique for WSNs’effective energy usage is presented.Because most energy consumption factors exist in the Medium Access Control(MAC)layer and network layer,our EECLP protocol(Energy Efficient Cross-Layer Protocol for Wireless Sensor Networks)integrates these two layers to satisfy energy efficiency criteria.To gain access to the transmission channel,we implement a communication regime at the MAC layer based on CSMA/CA(Carrier Sense Multiple Access/Collision Avoidance)techniques.Next,depending on the activity and a standby period,we employ the RTS/CTS(Request to Send/Clear to Send)method to prevent collisions and resolve hidden node concerns by utilizing the network allocation vector(NAV)to calculate the transmission duration.Employing a greedy strategy,we establish chains amongst cluster members to mitigate the issue of high energy consumption in routing data.An objective function was utilized to determine the optimal cross-chain path based on the distances to the base station(BS)and residual energy(RE).The simulation,testing,and comparison of the proposed protocol to peer protocols have shown superior outcomes and a prolonged network lifespan.Using the suggested protocol,the network lifetime increases by 10%compared to FAMACO(Fuzzy and Ant Colony Optimization based MAC/Routing Cross-layer)protocol,and it increases by 90%and 95%compared to IFUC(Improved Fuzzy Unequal Clustering)and UHEED(Unequal Hybrid Energy Efficient and Distributed)protocols successively.展开更多
Total resection of glioblastoma(GB)tumors is nearly impossible,and systemic administration of temozolomide(TMZ)is often inadequate.This study presents a hybrid layered composite nanofiber network(LHN)designed for loca...Total resection of glioblastoma(GB)tumors is nearly impossible,and systemic administration of temozolomide(TMZ)is often inadequate.This study presents a hybrid layered composite nanofiber network(LHN)designed for localized treatment in GB tumor bed.The LHN,consisting of polyvinyl alcohol and core-shell polylactic acid layers,was loaded with TMZ and rutin.In vitro analysis revealed that LHN^(TMZ) and LHNrutin decelerated epithelial-mesenchymal transition and growth of stem-like cells,while the combination,LHN^(TMZ)+rutin,significantly reduced sphere size compared to untreated and LHNTMZ-treated cells(P<0.0001).In an orthotopic C6-induced GB rat model,LHNTMZ+rutin therapy demonstrated a more pronounced tumor-reducing effect than LHNTMZ alone.Tumor volume,assessed by magnetic resonance imaging,was significantly reduced in LHN^(TMZ)+rutin-treated rats compared to untreated controls.Structural changes in tumor mitochondria,reduced membrane potential,and decreased PARP expression indicated the activation of apoptotic pathways in tumor cells,which was further confirmed by a reduction in PHH3,indicating decreased mitotic activity of tumor cells.Additionally,the local application of LHNs in the GB model mitigated aggressive tumor features without causing local tissue inflammation or adverse systemic effects.This was evidenced by a decrease in the angiogenesismarker CD31,the absence of inflammation or necrosis in H&E staining of the cerebellum,increased production of IFN-γ,decreased levels of interleukin-4 in splenic T cells,and lower serum AST levels.Our findings collectively indicate that LHN^(TMZ)+rutin is a promising biocompatible model for the local treatment of GB.展开更多
Corrosion engineering is an effective way to improve the oxygen evolution reaction(OER)activity of al-loys.However,the impact of grain boundary corrosion on the structure and electrochemical performance of alloy is st...Corrosion engineering is an effective way to improve the oxygen evolution reaction(OER)activity of al-loys.However,the impact of grain boundary corrosion on the structure and electrochemical performance of alloy is still unknown.Herein,the vacuum arc-melted CrCoNiFe alloys with interlaced network struc-tures via grain boundary corrosion methods were fabricated.The grain boundaries that existed as de-fects were severely corroded and an interlaced network structure was formed,promoting the exposure of the active site and the release of gas bubbles.Besides,the(oxy)hydroxides layer(25 nm)on the sur-face could act as the true active center and improve the surface wettability.Benefiting from the unique structure and constructed surface,the CrCoNiFe-12 affords a high urea oxidation reaction(UOR)perfor-mance with the lowest overpotential of 250 mV at 10 mA/cm^(2)in 1 M KOH adding 0.33 M urea.The CrCoNiFe-12||Pt only required a cell voltage of 1.485 V to afford 10 mA/cm^(2)for UOR and long-term sta-bility of 100 h at 10 mA/cm^(2)(27.6 mV decrease).These findings offer a facile strategy for designing bulk multiple-principal-element alloy electrodes for energy conversion.展开更多
Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for ...Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.展开更多
The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applicatio...The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applications.Therefore the research and development on 6 G have been put on the agenda.Regarding demands and characteristics of future 6 G,artificial intelligence(A),big data(B)and cloud computing(C)will play indispensable roles in achieving the highest efficiency and the largest benefits.Interestingly,the initials of these three aspects remind us the significance of vitamin ABC to human body.In this article we specifically expound on the three elements of ABC and relationships in between.We analyze the basic characteristics of wireless big data(WBD)and the corresponding technical action in A and C,which are the high dimensional feature and spatial separation,the predictive ability,and the characteristics of knowledge.Based on the abilities of WBD,a new learning approach for wireless AI called knowledge+data-driven deep learning(KD-DL)method,and a layered computing architecture of mobile network integrating cloud/edge/terminal computing,is proposed,and their achievable efficiency is discussed.These progress will be conducive to the development of future 6 G.展开更多
In this paper, the invulnerability and cascade failures are discussed for the urban bus network. Firstly, three static models（bus stop network, bus transfer network, and bus line network） are used to analyse the str...In this paper, the invulnerability and cascade failures are discussed for the urban bus network. Firstly, three static models（bus stop network, bus transfer network, and bus line network） are used to analyse the structure and invulnerability of urban bus network in order to understand the features of bus network comprehensively. Secondly, a new way is proposed to study the invulnerability of urban bus network by modelling two layered networks, i.e., the bus stop-line network and the bus line-transfer network and then the interactions between different models are analysed. Finally, by modelling a new layered network which can reflect the dynamic passenger flows, the cascade failures are discussed. Then a new load redistribution method is proposed to study the robustness of dynamic traffic. In this paper, the bus network of Shenyang City which is one of the biggest cities in China, is taken as a simulation example. In addition, some suggestions are given to improve the urban bus network and provide emergency strategies when traffic congestion occurs according to the numerical simulation results.展开更多
High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading del...High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.展开更多
5G is envisioned to guarantee high transmission rate,ultra-low latency,high reliability and massive connections.To satisfy the above requirements,the 5G architecture is designed with the properties of using service-ba...5G is envisioned to guarantee high transmission rate,ultra-low latency,high reliability and massive connections.To satisfy the above requirements,the 5G architecture is designed with the properties of using service-based architecture,cloud-native oriented,adopting IT-based API interfaces and introduction of the Network Repository Function.However,with the wide commercialization of 5G network and the exploration towards 6G,the 5G architecture exposes the disadvantages of high architecture complexity,difficult inter-interface communication,low cognitive capability,bad instantaneity,and deficient intelligence.To overcome these limitations,this paper investigates 6G network architecture,and proposes a cognitive intelligence based distributed 6G network architecture.This architecture consists of a physical network layer and an intelligent decision layer.The two layers coordinate through flexible service interfaces,supporting function decoupling and joint evolution of intelligence services and network services.With the above design,the proposed 6G architecture can be updated autonomously to deal with the future unpredicted complex services.展开更多
Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolvi...Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.展开更多
The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challengi...The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker(KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.展开更多
Accurate prediction on geological hazards can prevent disaster events in advance and greatly reduce property losses and life casualties.Glacial debris flows are the most serious hazards in southeastern Tibet in China ...Accurate prediction on geological hazards can prevent disaster events in advance and greatly reduce property losses and life casualties.Glacial debris flows are the most serious hazards in southeastern Tibet in China due to their complexity in formation mechanism and the difficulty in prediction.Data collected from 102 glacier debris flow events from 31 gullies since 1970 and regional meteorological data from 1970 to 2019 in ParlungZangbo River Basin in southeastern Tibet were used for Artificial Neural Network(ANN)-based prediction of glacial debris flows.The formation mechanism of glacial debris flows in the ParlungZangbo Basin was systematically analyzed,and the calculations involving the meteorological data and disaster events were conducted by using the statistical methods and two layers fully connected neural networks.The occurrence probabilities and scales of glacial debris flows(small,medium,and large)were predicted,and promising results have been achieved.Through the proposed model calculations,a prediction accuracy of 78.33%was achieved for the scale of glacial debris flows in the study area.The prediction accuracy for both large-and medium-scale debris flows are higher than that for small-scale debris flows.The debris flow scale and the probability of occurrence increase with increasing rainfall and temperature.In addition,the K-fold cross-validation method was used to verify the reliability of the model.The average accuracy of the model calculated under this method is about 93.3%,which validates the proposed model.Practices have proved that the combination of ANN and disaster events can provide sound prediction on geological hazards under complex conditions.展开更多
A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluct...A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.展开更多
As a smart spectrum sharing technology, Cognitive Radio (CR) is becoming a hot topic in the field of wireless telecommunications. Besides providing traditional services, the cognitive radio network Media Access Contro...As a smart spectrum sharing technology, Cognitive Radio (CR) is becoming a hot topic in the field of wireless telecommunications. Besides providing traditional services, the cognitive radio network Media Access Control (MAC) layer is required to perform an entirely new set of functions for effective reusing spectrum opportunity, without causing any harmful interference to incumbents. Spectrum sensing management selects and optimizes sensing strategies and parameters by the selection of sensing mode, sensing period, sensing time, sensing channel, and sensing quiet period. Access control avoids collision with primary users mainly by cooperation access and transparent access. Dynamic spectrum allocation optimizes the allocation of uncertain spectrum for binary interference model and accumulative interference model. Security mechanism adds authentication and encryption mechanisms to MAC frame to defense MAC layer security attacks. Cross-layer design combines MAC layer information with physical layer or higher layers information, such as network layer, transmission layer, to achieve global optimization.展开更多
Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a sh...Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a shortened harvest cycle,lacks a consistent maturity identification standard,and relies heavily on manual labor.To address these issues,a new method for detecting the maturity of Hemerocallis citrina Baroni,called LTCB YOLOv7,has been introduced.To begin with,the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution,a lightweight technique that streamlines the model architecture.This results in a reduction of model parameters and computational workload.Second,a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks,which enhances the model precision and compensates for the performance decline caused by lightweight design.Ultimately,a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network.This modification enables the integration of information across different stages,resulting in a gradual improvement in the overall model performance.The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G,respectively,and the model volume is compressed by about 3.5M.This refinement leads to enhancements in precision and recall by approximately 0.58%and 0.18%respectively,while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61%and 0.82%respectively.Furthermore,the algorithm achieves a real-time detection performance of 96.15 FPS.The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni,effectively addressing the challenge of balancing model complexity and performance.It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes.展开更多
Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selecti...Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.展开更多
Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） ...Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.展开更多
文摘With the advent of the digital era,the field of communications is undergoing profound transformation.Among the most groundbreaking developments is the emergence of non-terrestrial networks(NTN),which represent not only a technological leap forward but also a major trend shaping the future of global connectivity.As a layered heterogeneous network,NTN integrates multiple aerial platforms—including satellites,high-altitude platform systems(HAPS),and unmanned aerial systems(UAS)—to provide flexible and composable solutions aimed at achieving seamless worldwide communication coverage.
基金the National Natural Science Foundation of China under Grant 61502411Natural Science Foundation of Jiangsu Province under Grant BK20150432 and BK20151299+7 种基金Natural Science Research Project for Universities of Jiangsu Province under Grant 15KJB520034China Postdoctoral Science Foundation under Grant 2015M581843Jiangsu Provincial Qinglan ProjectTeachers Overseas Study Program of Yancheng Institute of TechnologyJiangsu Provincial Government Scholarship for Overseas StudiesTalents Project of Yancheng Institute of Technology under Grant KJC2014038“2311”Talent Project of Yancheng Institute of TechnologyOpen Fund of Modern Agricultural Resources Intelligent Management and Application Laboratory of Huzhou Normal University.
文摘In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.
基金the Science & Technology Foundation of Huawei Ltd. (No.YJCB2005040SW)the Creative Foundation of Xidian University (No.05030).
文摘A layered network model for optical transport networks is proposed in this paper,which involves Internet Protocol(IP) ,Synchronous Digital Hierarchy(SDH) and Wavelength Division Mul-tiplexing(WDM) layers. The strategy of Dynamic Joint Routing and Resource Allocation(DJRRA) and its algorithm description are also presented for the proposed layered network model. DJRRA op-timizes the bandwidth usage of interface links between different layers and the logic links inside all layers. The simulation results show that DJRRA can reduce the blocking probability and increase network throughput effectively,which is in contrast to the classical separate sequential routing and resource allocation solutions.
基金supported by National Natural Science Foundation of China(61304263,61233007)the Cross-disciplinary Collaborative Teams Program for Science,Technology and Innovation of Chinese Academy of Sciences-Network and System Technologies for Security Monitoring and Information Interaction in Smart Arid
基金This research was partially funded by the Algerian National Agency of Research and Development(DGRSDT-PRFU Project Number C00L07UN010120200001)The research was also partially funded by Mohammed Bin Rashid Smart Learning Program,United Arab Emirates(MBRSLP/06/23).
文摘Recent advancements in wireless communications have allowed the birth of novel wireless sensor networks(WSN).A sensor network comprises several micro-sensors deployed randomly in an area of interest.A micro-sensor is provided with an energy resource to supply electricity to all of its components.However,the disposed energy resource is limited and battery replacement is generally infeasible.With this restriction,the sensors must conserve energy to prolong their lifetime.Various energy conservation strategies for WSNs have been presented in the literature,from the application to the physical layer.Most of these solutions focus only on optimizing a single layer in terms of energy consumption.In this research,a novel cross-layer technique for WSNs’effective energy usage is presented.Because most energy consumption factors exist in the Medium Access Control(MAC)layer and network layer,our EECLP protocol(Energy Efficient Cross-Layer Protocol for Wireless Sensor Networks)integrates these two layers to satisfy energy efficiency criteria.To gain access to the transmission channel,we implement a communication regime at the MAC layer based on CSMA/CA(Carrier Sense Multiple Access/Collision Avoidance)techniques.Next,depending on the activity and a standby period,we employ the RTS/CTS(Request to Send/Clear to Send)method to prevent collisions and resolve hidden node concerns by utilizing the network allocation vector(NAV)to calculate the transmission duration.Employing a greedy strategy,we establish chains amongst cluster members to mitigate the issue of high energy consumption in routing data.An objective function was utilized to determine the optimal cross-chain path based on the distances to the base station(BS)and residual energy(RE).The simulation,testing,and comparison of the proposed protocol to peer protocols have shown superior outcomes and a prolonged network lifespan.Using the suggested protocol,the network lifetime increases by 10%compared to FAMACO(Fuzzy and Ant Colony Optimization based MAC/Routing Cross-layer)protocol,and it increases by 90%and 95%compared to IFUC(Improved Fuzzy Unequal Clustering)and UHEED(Unequal Hybrid Energy Efficient and Distributed)protocols successively.
基金This study was supported by Scientific and Technological Research Council of Turkey(TUBITAK)under the Grant Number 121S624.
文摘Total resection of glioblastoma(GB)tumors is nearly impossible,and systemic administration of temozolomide(TMZ)is often inadequate.This study presents a hybrid layered composite nanofiber network(LHN)designed for localized treatment in GB tumor bed.The LHN,consisting of polyvinyl alcohol and core-shell polylactic acid layers,was loaded with TMZ and rutin.In vitro analysis revealed that LHN^(TMZ) and LHNrutin decelerated epithelial-mesenchymal transition and growth of stem-like cells,while the combination,LHN^(TMZ)+rutin,significantly reduced sphere size compared to untreated and LHNTMZ-treated cells(P<0.0001).In an orthotopic C6-induced GB rat model,LHNTMZ+rutin therapy demonstrated a more pronounced tumor-reducing effect than LHNTMZ alone.Tumor volume,assessed by magnetic resonance imaging,was significantly reduced in LHN^(TMZ)+rutin-treated rats compared to untreated controls.Structural changes in tumor mitochondria,reduced membrane potential,and decreased PARP expression indicated the activation of apoptotic pathways in tumor cells,which was further confirmed by a reduction in PHH3,indicating decreased mitotic activity of tumor cells.Additionally,the local application of LHNs in the GB model mitigated aggressive tumor features without causing local tissue inflammation or adverse systemic effects.This was evidenced by a decrease in the angiogenesismarker CD31,the absence of inflammation or necrosis in H&E staining of the cerebellum,increased production of IFN-γ,decreased levels of interleukin-4 in splenic T cells,and lower serum AST levels.Our findings collectively indicate that LHN^(TMZ)+rutin is a promising biocompatible model for the local treatment of GB.
基金supported by the National Natu-ral Science Foundation of China(No.52102210)the Natural Sci-ence Foundation of Sichuan Province(Nos.2022NSFSC2005 and 2022NSFSC1255)+1 种基金the Opening Project of Key Laboratory of Op-toelectronic Chemical Materials and Devices of Ministry of Educa-tion,Jianghan University(No.JDGD-202218)Supplementary materials Supplementary material associated with this article can be found,in the online version,at doi:10.1016/j.jmst.2024.01.096.106。
文摘Corrosion engineering is an effective way to improve the oxygen evolution reaction(OER)activity of al-loys.However,the impact of grain boundary corrosion on the structure and electrochemical performance of alloy is still unknown.Herein,the vacuum arc-melted CrCoNiFe alloys with interlaced network struc-tures via grain boundary corrosion methods were fabricated.The grain boundaries that existed as de-fects were severely corroded and an interlaced network structure was formed,promoting the exposure of the active site and the release of gas bubbles.Besides,the(oxy)hydroxides layer(25 nm)on the sur-face could act as the true active center and improve the surface wettability.Benefiting from the unique structure and constructed surface,the CrCoNiFe-12 affords a high urea oxidation reaction(UOR)perfor-mance with the lowest overpotential of 250 mV at 10 mA/cm^(2)in 1 M KOH adding 0.33 M urea.The CrCoNiFe-12||Pt only required a cell voltage of 1.485 V to afford 10 mA/cm^(2)for UOR and long-term sta-bility of 100 h at 10 mA/cm^(2)(27.6 mV decrease).These findings offer a facile strategy for designing bulk multiple-principal-element alloy electrodes for energy conversion.
基金suported by the Fundamental Research Grant Scheme(FRGS)of Universiti Sains Malaysia,Research Number:FRGS/1/2024/ICT02/USM/02/1.
文摘Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.
基金supported by Key Program of Natural Science Foundation of China(Grant No.61631018)Anhui Provincial Natural Science Foundation(Grant No.1908085MF177)Huawei Technology Innovative Research(YBN2018095087)。
文摘The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applications.Therefore the research and development on 6 G have been put on the agenda.Regarding demands and characteristics of future 6 G,artificial intelligence(A),big data(B)and cloud computing(C)will play indispensable roles in achieving the highest efficiency and the largest benefits.Interestingly,the initials of these three aspects remind us the significance of vitamin ABC to human body.In this article we specifically expound on the three elements of ABC and relationships in between.We analyze the basic characteristics of wireless big data(WBD)and the corresponding technical action in A and C,which are the high dimensional feature and spatial separation,the predictive ability,and the characteristics of knowledge.Based on the abilities of WBD,a new learning approach for wireless AI called knowledge+data-driven deep learning(KD-DL)method,and a layered computing architecture of mobile network integrating cloud/edge/terminal computing,is proposed,and their achievable efficiency is discussed.These progress will be conducive to the development of future 6 G.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61473073,61374178,61104074,and 61203329)the Fundamental Research Funds for the Central Universities(Grant Nos.N130417006,L1517004)the Program for Liaoning Excellent Talents in University(Grant No.LJQ2014028)
文摘In this paper, the invulnerability and cascade failures are discussed for the urban bus network. Firstly, three static models（bus stop network, bus transfer network, and bus line network） are used to analyse the structure and invulnerability of urban bus network in order to understand the features of bus network comprehensively. Secondly, a new way is proposed to study the invulnerability of urban bus network by modelling two layered networks, i.e., the bus stop-line network and the bus line-transfer network and then the interactions between different models are analysed. Finally, by modelling a new layered network which can reflect the dynamic passenger flows, the cascade failures are discussed. Then a new load redistribution method is proposed to study the robustness of dynamic traffic. In this paper, the bus network of Shenyang City which is one of the biggest cities in China, is taken as a simulation example. In addition, some suggestions are given to improve the urban bus network and provide emergency strategies when traffic congestion occurs according to the numerical simulation results.
基金supported by the National Natural Science Foundation of China (U1834211, 61925302, 62103033)the Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems (20210104)。
文摘High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.
基金funded by Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center,the National Key R&D Program of China(2018YFE0205503)the National Natural Science Foundation of China(61902036,62032003,61922017)Fundamental Research Funds for the Central Universities。
文摘5G is envisioned to guarantee high transmission rate,ultra-low latency,high reliability and massive connections.To satisfy the above requirements,the 5G architecture is designed with the properties of using service-based architecture,cloud-native oriented,adopting IT-based API interfaces and introduction of the Network Repository Function.However,with the wide commercialization of 5G network and the exploration towards 6G,the 5G architecture exposes the disadvantages of high architecture complexity,difficult inter-interface communication,low cognitive capability,bad instantaneity,and deficient intelligence.To overcome these limitations,this paper investigates 6G network architecture,and proposes a cognitive intelligence based distributed 6G network architecture.This architecture consists of a physical network layer and an intelligent decision layer.The two layers coordinate through flexible service interfaces,supporting function decoupling and joint evolution of intelligence services and network services.With the above design,the proposed 6G architecture can be updated autonomously to deal with the future unpredicted complex services.
基金supported by the National Natural Science Foundation of China(615730176140149961174162)
文摘Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.
基金supported by the National Natural Science Foundation of China under Grant No.61371075the 863 project SS2015AA011306
文摘The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker(KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.41671112)the Sichuan Province Science and Technology Plan Project Key research and development projects(Grant No.18ZDYF0329)the National Natural Science Foundation of China(Grant No.41861134008)。
文摘Accurate prediction on geological hazards can prevent disaster events in advance and greatly reduce property losses and life casualties.Glacial debris flows are the most serious hazards in southeastern Tibet in China due to their complexity in formation mechanism and the difficulty in prediction.Data collected from 102 glacier debris flow events from 31 gullies since 1970 and regional meteorological data from 1970 to 2019 in ParlungZangbo River Basin in southeastern Tibet were used for Artificial Neural Network(ANN)-based prediction of glacial debris flows.The formation mechanism of glacial debris flows in the ParlungZangbo Basin was systematically analyzed,and the calculations involving the meteorological data and disaster events were conducted by using the statistical methods and two layers fully connected neural networks.The occurrence probabilities and scales of glacial debris flows(small,medium,and large)were predicted,and promising results have been achieved.Through the proposed model calculations,a prediction accuracy of 78.33%was achieved for the scale of glacial debris flows in the study area.The prediction accuracy for both large-and medium-scale debris flows are higher than that for small-scale debris flows.The debris flow scale and the probability of occurrence increase with increasing rainfall and temperature.In addition,the K-fold cross-validation method was used to verify the reliability of the model.The average accuracy of the model calculated under this method is about 93.3%,which validates the proposed model.Practices have proved that the combination of ANN and disaster events can provide sound prediction on geological hazards under complex conditions.
文摘A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.
基金supported by the National Natural Science Foundation of China under Grant No.60772110.
文摘As a smart spectrum sharing technology, Cognitive Radio (CR) is becoming a hot topic in the field of wireless telecommunications. Besides providing traditional services, the cognitive radio network Media Access Control (MAC) layer is required to perform an entirely new set of functions for effective reusing spectrum opportunity, without causing any harmful interference to incumbents. Spectrum sensing management selects and optimizes sensing strategies and parameters by the selection of sensing mode, sensing period, sensing time, sensing channel, and sensing quiet period. Access control avoids collision with primary users mainly by cooperation access and transparent access. Dynamic spectrum allocation optimizes the allocation of uncertain spectrum for binary interference model and accumulative interference model. Security mechanism adds authentication and encryption mechanisms to MAC frame to defense MAC layer security attacks. Cross-layer design combines MAC layer information with physical layer or higher layers information, such as network layer, transmission layer, to achieve global optimization.
基金funded by the Shanxi Provincial Science and Technology Department Surface Project(Grant No.202303021211330)Innovation Platform Project of Science and Technology Innovation Program of Higher Education Institutions in Shanxi Province(Grant No.2022P009)+2 种基金Shanxi Province Basic Research Program Projects(Grant No.202303021212244)the Datong City Shanxi Province Key Research&Development(Agriculture)Program Projects(Grants No.2023006,2023015)the 2024 Basic Research Program of Shanxi Province(Free Exploration Category)Program Projects(Grant No.202403021221181).
文摘Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a shortened harvest cycle,lacks a consistent maturity identification standard,and relies heavily on manual labor.To address these issues,a new method for detecting the maturity of Hemerocallis citrina Baroni,called LTCB YOLOv7,has been introduced.To begin with,the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution,a lightweight technique that streamlines the model architecture.This results in a reduction of model parameters and computational workload.Second,a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks,which enhances the model precision and compensates for the performance decline caused by lightweight design.Ultimately,a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network.This modification enables the integration of information across different stages,resulting in a gradual improvement in the overall model performance.The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G,respectively,and the model volume is compressed by about 3.5M.This refinement leads to enhancements in precision and recall by approximately 0.58%and 0.18%respectively,while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61%and 0.82%respectively.Furthermore,the algorithm achieves a real-time detection performance of 96.15 FPS.The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni,effectively addressing the challenge of balancing model complexity and performance.It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes.
文摘Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.
文摘Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.
