Compact torus(CT)injection is a highly promising technique for the central fueling of future reactor-grade fusion devices since it features extremely high injection velocity and relatively high plasma mass.Recently,a ...Compact torus(CT)injection is a highly promising technique for the central fueling of future reactor-grade fusion devices since it features extremely high injection velocity and relatively high plasma mass.Recently,a CT injector for the EAST tokamak,EAST-CTI,was developed and platform-tested.In the first round of experiments conducted with low parameter settings,the maximum velocity and mass of the CT plasma were 150 km·s^(-1)and 90μg,respectively.However,the parameters obtained by EAST-CTI were still very low and were far from the requirements of a device such as EAST that has a strong magnetic field.In future,we plan to solve the spark problem that EAST-CTI currently encounters(that mainly hinders the further development of experiments)through engineering methods,and use greater power to obtain a more stable and suitable CT plasma for EAST.展开更多
A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are con...A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.展开更多
Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success.However,few methods exist for generating full-body animation...Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success.However,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient attention.Methods Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body animations.This parameterization method can be used to drive full-body animations of virtual characters and improve their portability.In the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial data.The Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio features.Finally,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function.Results By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape weights.Therefore,different 3D virtual character animations could be created through parameterization.Conclusions The experimental results showed that the proposed method could generate significant gestures and facial animations.展开更多
This study details an astrometric observation campaign of the Near-Earth Asteroid 1998 HH49,conducted with the aim of refining our understanding of its physical characteristics.Utilizing the 50 cm telescope located at...This study details an astrometric observation campaign of the Near-Earth Asteroid 1998 HH49,conducted with the aim of refining our understanding of its physical characteristics.Utilizing the 50 cm telescope located at the Wumingshan Mountain in Daocheng,Sichuan,images were obtained over four nights,from 2023 October 19 to October 22.These observations were processed using Astrometrica software,facilitating the precise determination of the asteroid's position.The observational results were compared with the ephemerides from three distinct sources to verify accuracy:the Jet Propulsion Laboratory(JPL)Horizons System,the Institut de Mécanique Céleste et de Calcul deséphémérides(IMCCE)Miriade,and the Near-Earth Objects Dynamic Site(NEODyS-2).When compared with the JPL ephemeris,a mean observed-minus-calculated(O-C)result of 0.″07 in the R.A.direction and-0.″35 in the decl.direction was yielded.Furthermore,the comparison with the IMCCE ephemeris yielded mean O-C results of 0.″08 in the R.A.direction and-0.″06 in the decl.direction.The comparison with the NEODyS-2 ephemeris yielded the mean O-C results of 0.″06 in R.A.and-0.″49 in decl.direction.The study's findings demonstrate a general consistency between the observed data and the ephemeris predictions,with minor discrepancies observed across the data sets.Notably,both the JPL and NEODyS-2 ephemerides show that the residuals in the decl.direction exceed those in the R.A.direction.The disparities may result from atmospheric differential color refraction,ephemeris discrepancies,observational errors,and other factors.Additionally,it is worth noting that further investigation is required due to the potential influence of additional factors.Overall,the Daocheng 50 cm Telescope exhibits the ability to conduct high-precision positional measurements.展开更多
Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial ...Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.展开更多
Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concer...Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.展开更多
Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the mil...Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.展开更多
The thermodynamic properties of a （2 ＋ 1）-dimensional black hole with non-linear electrodynamics from the viewpoint of geometry is studied and some kinds of temperatures of the black hole have been obtained. Weinho...The thermodynamic properties of a （2 ＋ 1）-dimensional black hole with non-linear electrodynamics from the viewpoint of geometry is studied and some kinds of temperatures of the black hole have been obtained. Weinhold curvature and Ruppeiner curvature are explored as information geometry. Moreover, based on Quevedo＇s theory, the Legendre invariant geometry is investigated for the black hole. We also study the relationship between the scalar curvatures of the above several metrics and the phase transitions produced from the heat capacity.展开更多
Parametric decay instability(PDI)is an important process in ionospheric heating.This paper focuses on the frequency and wavevector matching condition in the initial PDI process,the subsequent cascade stage,and the gen...Parametric decay instability(PDI)is an important process in ionospheric heating.This paper focuses on the frequency and wavevector matching condition in the initial PDI process,the subsequent cascade stage,and the generation of strong Langmuir turbulence.A more general numerical model is established based on Maxwell equations and plasma dynamic equations by coupling highfrequency electromagnetic waves to low-frequency waves via ponderomotive force.The primary PDI,cascade process,and strong Langmuir turbulence are excited in the simulation.The matching condition in the initial PDI stage and cascade process is verified.The result indicates that the cascade ion acoustic wave may induce or accelerate the formation of cavitons and lead to the wavenumber spectrum being more enhanced at 2k_(L)(where k_(L) is the primary Langmuir wavenumber).The wavenumber spectra develop from discrete to continuous spectra,which is attributed to the caviton collapse and strong Langmuir turbulence.展开更多
Guqin music is a precious cultural heritage of China. The notation of Guqin is very special, which records its playing methods and techniques. For the purpose of preserving the guqin art, the digitalization of guqin n...Guqin music is a precious cultural heritage of China. The notation of Guqin is very special, which records its playing methods and techniques. For the purpose of preserving the guqin art, the digitalization of guqin notation and an interpretation method of guqin notation were conducted. By using this interpretation method, raw images of handwritten notations are transformed into structural data that can be processed and analyzed by computers easily. The method decomposes each single complex character of guqin notations into simple radicals and finds the structure of the character. According to the radicals and the structure, the character is interpreted into meaningful codes. The experimental results show our method is effective.展开更多
A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure t...A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.展开更多
To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentat...To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object detection.Firstly,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient flow.Secondly,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training adjustments.Experimental results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,respectively.The detection performance surpasses that of other single-task or multi-task algorithm models.展开更多
Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social ...Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.展开更多
Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.O...Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.展开更多
Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC a...Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.展开更多
This article proposes a VGG network with histogram of oriented gradient(HOG) feature fusion(HOG-VGG) for polarization synthetic aperture radar(PolSAR) image terrain classification.VGG-Net has a strong ability of deep ...This article proposes a VGG network with histogram of oriented gradient(HOG) feature fusion(HOG-VGG) for polarization synthetic aperture radar(PolSAR) image terrain classification.VGG-Net has a strong ability of deep feature extraction,which can fully extract the global deep features of different terrains in PolSAR images,so it is widely used in PolSAR terrain classification.However,VGG-Net ignores the local edge & shape features,resulting in incomplete feature representation of the PolSAR terrains,as a consequence,the terrain classification accuracy is not promising.In fact,edge and shape features play an important role in PolSAR terrain classification.To solve this problem,a new VGG network with HOG feature fusion was specifically proposed for high-precision PolSAR terrain classification.HOG-VGG extracts both the global deep semantic features and the local edge & shape features of the PolSAR terrains,so the terrain feature representation completeness is greatly elevated.Moreover,HOG-VGG optimally fuses the global deep features and the local edge & shape features to achieve the best classification results.The superiority of HOG-VGG is verified on the Flevoland,San Francisco and Oberpfaffenhofen datasets.Experiments show that the proposed HOG-VGG achieves much better PolSAR terrain classification performance,with overall accuracies of 97.54%,94.63%,and 96.07%,respectively.展开更多
Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single...Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.展开更多
In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in ...In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments.Federated learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy.However,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still under-explored.In this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular IoT.The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with environments.The clients also work as forwarder nodes in information sharing among network entities.The client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking architecture.We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.展开更多
It is known that packet collisions in wireless networks will deteriorate system performance, hence substantial efforts have been made to avoid collision in multi-user access designs. Also, there have been many studies...It is known that packet collisions in wireless networks will deteriorate system performance, hence substantial efforts have been made to avoid collision in multi-user access designs. Also, there have been many studies on throughput analysis of CSMA wireless networks. However, for a typical CSMA network in which not all nodes can sense each other, it is still not well investigated how link throughputs are affected by collisions. We note that in practical 802.11-like networks, the time is divided into mini-timeslots and packet collisions are in fact unavoidable. Thus, it is desirable to move forward to explore how collisions in such a network will affect system performance. Based on the collision-free ideal CSMA network(ICN) model, this paper attempts to analyze link throughputs when taking the backoff collisions into account and examine the effect of collisions on link throughputs. Specifically, we propose an Extended Ideal CSMA Network(EICN) model to characterize the collision effects as well as the interactions and dependency among links in the network. Based on EICN, we could directly compute link throughputs and collision probabilities. Simulations show that the EICN model is of high accuracy. Under various network topologies and protocol parameter settings, the computation error of link throughputs using EICN is kept to 4% or below. Interestingly, we find that unlike expected, the effect of collisions on link throughputs in a modest CSMA wireless network is not significant, which enriches our understanding on practical CSMA wireless networks such as Wi-Fi.展开更多
Ti3C2Tx nanosheet,the first synthesized MXene with high capacity performance and charge/discharge rate,has attracted increasingly attention in renewable energy storage applications.By performing systematic density fun...Ti3C2Tx nanosheet,the first synthesized MXene with high capacity performance and charge/discharge rate,has attracted increasingly attention in renewable energy storage applications.By performing systematic density functional theory calculations,the theoretical capacity of the intrinsic structure of single-and multi-layered Ti3C2T2(T=F or O)corresponding to M(M=Li and Na)atoms are investigated.Theoretical volumetric capacity and gravimetric capacity are obtained,which are related to the stacking degree.The optimal ratios of capacity to structure are determined under different stacking degrees for understanding the influence of surface functional groups on energy storage performance.Its performance can be tuned by performing surface modification and increasing the interlayer distance.In addition,the reason for theoretical capacity differences of M atoms is analyzed,which is attributed to difference in interaction between the M-ions and substrate and the difference in electrostatic exclusion between adsorbed M-ions.These results provide an insight into the understanding of the method of efficiently increasing the energy storage performance,which will be useful for designing and using high performance electrode materials.展开更多
基金support of the National Key Research and Development Program of China(Nos.2017YFE0300501,2017YFE0300500)Institute of Energy,Hefei Comprehensive National Science Center(Nos.21KZS202,19KZS205)+3 种基金University Synergy Innovation Program of Anhui Province(Nos.GXXT-2021-014,GXXT-2021-029)National Natural Science Foundation of China(No.11905143)the Fundamental Research Funds for the Central Universities of China(No.JZ2022HGTB0302)supported in part by the Users with Excellence Program of Hefei Science Center CAS(No.2020HSC-UE008)。
文摘Compact torus(CT)injection is a highly promising technique for the central fueling of future reactor-grade fusion devices since it features extremely high injection velocity and relatively high plasma mass.Recently,a CT injector for the EAST tokamak,EAST-CTI,was developed and platform-tested.In the first round of experiments conducted with low parameter settings,the maximum velocity and mass of the CT plasma were 150 km·s^(-1)and 90μg,respectively.However,the parameters obtained by EAST-CTI were still very low and were far from the requirements of a device such as EAST that has a strong magnetic field.In future,we plan to solve the spark problem that EAST-CTI currently encounters(that mainly hinders the further development of experiments)through engineering methods,and use greater power to obtain a more stable and suitable CT plasma for EAST.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62105004 and 52174141)the College Student Innovation and Entrepreneurship Fund Project(Grant No.202210361053)+1 种基金Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center,Anhui University of Science&Technology(Grant No.KSJD202304)the Anhui Province Digital Agricultural Engineering Technology Research Center Open Project(Grant No.AHSZNYGC-ZXKF021)。
文摘A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.
基金Supported by the National Natural Science Foundation of China (62277014)the National Key Research and Development Program of China (2020YFC1523100)the Fundamental Research Funds for the Central Universities of China (PA2023GDSK0047)。
文摘Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success.However,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient attention.Methods Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body animations.This parameterization method can be used to drive full-body animations of virtual characters and improve their portability.In the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial data.The Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio features.Finally,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function.Results By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape weights.Therefore,different 3D virtual character animations could be created through parameterization.Conclusions The experimental results showed that the proposed method could generate significant gestures and facial animations.
基金funded by the National Key R&D Program of China(grant No.2022YFE0116800)the National Natural Science Foundation of China(NSFC,grant No.12173085)the West Light Foundation of The Chinese Academy of Sciences Key scientific research projects of colleges and universities in Henan Province(grant No.23B16001)。
文摘This study details an astrometric observation campaign of the Near-Earth Asteroid 1998 HH49,conducted with the aim of refining our understanding of its physical characteristics.Utilizing the 50 cm telescope located at the Wumingshan Mountain in Daocheng,Sichuan,images were obtained over four nights,from 2023 October 19 to October 22.These observations were processed using Astrometrica software,facilitating the precise determination of the asteroid's position.The observational results were compared with the ephemerides from three distinct sources to verify accuracy:the Jet Propulsion Laboratory(JPL)Horizons System,the Institut de Mécanique Céleste et de Calcul deséphémérides(IMCCE)Miriade,and the Near-Earth Objects Dynamic Site(NEODyS-2).When compared with the JPL ephemeris,a mean observed-minus-calculated(O-C)result of 0.″07 in the R.A.direction and-0.″35 in the decl.direction was yielded.Furthermore,the comparison with the IMCCE ephemeris yielded mean O-C results of 0.″08 in the R.A.direction and-0.″06 in the decl.direction.The comparison with the NEODyS-2 ephemeris yielded the mean O-C results of 0.″06 in R.A.and-0.″49 in decl.direction.The study's findings demonstrate a general consistency between the observed data and the ephemeris predictions,with minor discrepancies observed across the data sets.Notably,both the JPL and NEODyS-2 ephemerides show that the residuals in the decl.direction exceed those in the R.A.direction.The disparities may result from atmospheric differential color refraction,ephemeris discrepancies,observational errors,and other factors.Additionally,it is worth noting that further investigation is required due to the potential influence of additional factors.Overall,the Daocheng 50 cm Telescope exhibits the ability to conduct high-precision positional measurements.
基金supported by the MOE(Ministry of Education of China)Project of Humanities and Social Sciences(23YJAZH169)the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(T2020017)Henan Foreign Experts Project No.HNGD2023027.
文摘Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.
文摘Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.
基金This work was supported in part by the Natural Science Foundation of China under Grant 62203461 and Grant 62203365in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736+3 种基金in part by the Teaching reform project of higher education in Heilongjiang Province under Grant Nos.SJGY20210456 and SJGY20210457in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038in part by the graduate academic innovation project of Harbin Normal University under Grant Nos.HSDSSCX2022-17,HSDSSCX2022-18 andHSDSSCX2022-19in part by the Foreign Expert Project of Heilongjiang Province under Grant No.GZ20220131.
文摘Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.
基金supported by the National Natural Science Foundation of China(Grant No.11072276)the Scientific and Technological Foundation of Chongqing Municipal Education Commission,China(Grant No.KJ100706)
文摘The thermodynamic properties of a （2 ＋ 1）-dimensional black hole with non-linear electrodynamics from the viewpoint of geometry is studied and some kinds of temperatures of the black hole have been obtained. Weinhold curvature and Ruppeiner curvature are explored as information geometry. Moreover, based on Quevedo＇s theory, the Legendre invariant geometry is investigated for the black hole. We also study the relationship between the scalar curvatures of the above several metrics and the phase transitions produced from the heat capacity.
基金supported by the National Natural Science Founda-tion of China(NSFC Grant Nos.42104150,42074187,41774162,and 41704155)the Foundation of the National Key Laboratory of Electromagnetic Environment(Grant No.6142403200303)+2 种基金the Chinese Academy of Sciences,Key Laboratory of Geospace Envi-ronment,the University of Science&Technology of China(Grant No.GE2020-01)the Fundamental Research Funds for the Central Universities(Grant No.2042021kf0020)the Excellent Youth Foundation of Hubei Provincial Natural Science Foundation(Grant No.2019CFA054).
文摘Parametric decay instability(PDI)is an important process in ionospheric heating.This paper focuses on the frequency and wavevector matching condition in the initial PDI process,the subsequent cascade stage,and the generation of strong Langmuir turbulence.A more general numerical model is established based on Maxwell equations and plasma dynamic equations by coupling highfrequency electromagnetic waves to low-frequency waves via ponderomotive force.The primary PDI,cascade process,and strong Langmuir turbulence are excited in the simulation.The matching condition in the initial PDI stage and cascade process is verified.The result indicates that the cascade ion acoustic wave may induce or accelerate the formation of cavitons and lead to the wavenumber spectrum being more enhanced at 2k_(L)(where k_(L) is the primary Langmuir wavenumber).The wavenumber spectra develop from discrete to continuous spectra,which is attributed to the caviton collapse and strong Langmuir turbulence.
基金National Natural Science Foundation of China(No.6097507)
文摘Guqin music is a precious cultural heritage of China. The notation of Guqin is very special, which records its playing methods and techniques. For the purpose of preserving the guqin art, the digitalization of guqin notation and an interpretation method of guqin notation were conducted. By using this interpretation method, raw images of handwritten notations are transformed into structural data that can be processed and analyzed by computers easily. The method decomposes each single complex character of guqin notations into simple radicals and finds the structure of the character. According to the radicals and the structure, the character is interpreted into meaningful codes. The experimental results show our method is effective.
基金This work was supported in part by the Natural Science Foundation of China under Grant 62203461 and Grant 62203365in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736,in part by the Teaching Reform Project of Higher Education in Heilongjiang Province under Grant Nos.SJGY20210456 and SJGY20210457in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038,and in part by the Graduate Academic Innovation Project of Harbin Normal University under Grant Nos.HSDSSCX2022-17,HSDSSCX2022-18 and HSDSSCX2022-19。
文摘A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.
文摘To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object detection.Firstly,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient flow.Secondly,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training adjustments.Experimental results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,respectively.The detection performance surpasses that of other single-task or multi-task algorithm models.
基金supported by the NationalNatural Science Foundation of China(61972136)the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(T201410,T2020017)+1 种基金the Natural Science Foundation of Xiaogan City(XGKJ2022010095,XGKJ2022010094)the Science and Technology Research Project of Education Department of Hubei Province(No.Q20222704).
文摘Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.
基金supported in part by the Chongqing Natural Science Foundation Innovation and Development Joint Foundation(No.CSTB2024NSCQ-LZX0035)Science and Technology Research Project of Chongqing Education Commission(No.KJZD-M202300605)+4 种基金Nanning“Yongjiang Plan”Youth Talent Project(RC20230107)Special General Project for Chongqing’s TechNological Innovation and Application Development(CSTB2022TIAD-GPX0028)Chongqing Natural Science Foundation Project(CSTB2022NSCQ-MSX0230)supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R 343)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and the authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia,for funding this research work through the Project Number“NBU-FFR2024-1092-07”.
文摘Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.
基金supported by the Key Research and Development Project in Anhui Province of China(Grant No.202304a05020059)the Fundamental Research Funds for the Central Universities of China(Grant No.PA2023GDSK0055)the Project of Anhui Province Economic and Information Bureau(Grant No.JB20099).
文摘Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.
基金Sponsored by the Fundamental Research Funds for the Central Universities of China(Grant No.PA2023IISL0098)the Hefei Municipal Natural Science Foundation(Grant No.202201)+1 种基金the National Natural Science Foundation of China(Grant No.62071164)the Open Fund of Information Materials and Intelligent Sensing Laboratory of Anhui Province(Anhui University)(Grant No.IMIS202214 and IMIS202102)。
文摘This article proposes a VGG network with histogram of oriented gradient(HOG) feature fusion(HOG-VGG) for polarization synthetic aperture radar(PolSAR) image terrain classification.VGG-Net has a strong ability of deep feature extraction,which can fully extract the global deep features of different terrains in PolSAR images,so it is widely used in PolSAR terrain classification.However,VGG-Net ignores the local edge & shape features,resulting in incomplete feature representation of the PolSAR terrains,as a consequence,the terrain classification accuracy is not promising.In fact,edge and shape features play an important role in PolSAR terrain classification.To solve this problem,a new VGG network with HOG feature fusion was specifically proposed for high-precision PolSAR terrain classification.HOG-VGG extracts both the global deep semantic features and the local edge & shape features of the PolSAR terrains,so the terrain feature representation completeness is greatly elevated.Moreover,HOG-VGG optimally fuses the global deep features and the local edge & shape features to achieve the best classification results.The superiority of HOG-VGG is verified on the Flevoland,San Francisco and Oberpfaffenhofen datasets.Experiments show that the proposed HOG-VGG achieves much better PolSAR terrain classification performance,with overall accuracies of 97.54%,94.63%,and 96.07%,respectively.
基金supported by the National Science Foundation of China(61472289)National Key Research and Development Project(2016YFC0106305)The Key Technology R&D Program of Hubei Provence(2014BAA153)
文摘Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.
基金This research was supported in part by the National Natural Science Foundation of China under Grant No.62062031 and 61877053in part by Inner Mongolia natural science foundation grant number 2019MS06035,and Inner Mongolia Science and Technology Major Project,China+1 种基金in part by ROIS NII Open Collaborative Research 21S0601in part by JSPS KAKENHI grant numbers 18KK0279,19H04093,20H00592,and 21H03424.
文摘In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments.Federated learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy.However,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still under-explored.In this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular IoT.The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with environments.The clients also work as forwarder nodes in information sharing among network entities.The client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking architecture.We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.
基金partially supported by the National Natural Science Foundation of China under Grant 61571178,Grant 61771315 and Grant 61501160
文摘It is known that packet collisions in wireless networks will deteriorate system performance, hence substantial efforts have been made to avoid collision in multi-user access designs. Also, there have been many studies on throughput analysis of CSMA wireless networks. However, for a typical CSMA network in which not all nodes can sense each other, it is still not well investigated how link throughputs are affected by collisions. We note that in practical 802.11-like networks, the time is divided into mini-timeslots and packet collisions are in fact unavoidable. Thus, it is desirable to move forward to explore how collisions in such a network will affect system performance. Based on the collision-free ideal CSMA network(ICN) model, this paper attempts to analyze link throughputs when taking the backoff collisions into account and examine the effect of collisions on link throughputs. Specifically, we propose an Extended Ideal CSMA Network(EICN) model to characterize the collision effects as well as the interactions and dependency among links in the network. Based on EICN, we could directly compute link throughputs and collision probabilities. Simulations show that the EICN model is of high accuracy. Under various network topologies and protocol parameter settings, the computation error of link throughputs using EICN is kept to 4% or below. Interestingly, we find that unlike expected, the effect of collisions on link throughputs in a modest CSMA wireless network is not significant, which enriches our understanding on practical CSMA wireless networks such as Wi-Fi.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51772069 and 51772070)
文摘Ti3C2Tx nanosheet,the first synthesized MXene with high capacity performance and charge/discharge rate,has attracted increasingly attention in renewable energy storage applications.By performing systematic density functional theory calculations,the theoretical capacity of the intrinsic structure of single-and multi-layered Ti3C2T2(T=F or O)corresponding to M(M=Li and Na)atoms are investigated.Theoretical volumetric capacity and gravimetric capacity are obtained,which are related to the stacking degree.The optimal ratios of capacity to structure are determined under different stacking degrees for understanding the influence of surface functional groups on energy storage performance.Its performance can be tuned by performing surface modification and increasing the interlayer distance.In addition,the reason for theoretical capacity differences of M atoms is analyzed,which is attributed to difference in interaction between the M-ions and substrate and the difference in electrostatic exclusion between adsorbed M-ions.These results provide an insight into the understanding of the method of efficiently increasing the energy storage performance,which will be useful for designing and using high performance electrode materials.
