Anti-phase domain defects easily form in the in-plane GaAs nanowires(NWs)grown on CMOS-compatiblegroup IV substrates,which makes it difficult to obtain GaAs NWs with a designed length and also leads to asignificant li...Anti-phase domain defects easily form in the in-plane GaAs nanowires(NWs)grown on CMOS-compatiblegroup IV substrates,which makes it difficult to obtain GaAs NWs with a designed length and also leads to asignificant limitation in the growth of high-quality in-plane GaAs NW networks on such substrates.Here,wereport on the selective area growth of anti-phase domain-free in-plane GaAs NWs and NW networks on Ge(111)substrates.Detailed structural studies confirm that the GaAs NW grown using a large pattern period and GaAsNW networks grown by adding the Sb are both high-quality pure zinc-blende single crystals free of stackingfaults,twin defects,and anti-phase domain defects.Room-temperature photoluminescence measurements show asubstantial improvement in crystal quality and good consistency and uniformity of the GaAs NW networks.Ourwork provides useful insights into the controlled growth of high-quality anti-phase domain-defects-free in-planeIII-V NWs and NW networks.展开更多
Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and pea...Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.展开更多
With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with ...With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.展开更多
The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy...The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti_(34)Cu17Ni_(31.36)Hf_(17.64)and Ti_(36)Cu_(18)Ni_(29.44)Hf_(16.56)alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions.展开更多
Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree a...Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.展开更多
In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser...In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.展开更多
In this paper, we investigate a cooperation mechanism for satellite-terrestrial integrated networks. The terrestrial relays act as the supplement of traditional small cells and cooperatively provide seamless coverage ...In this paper, we investigate a cooperation mechanism for satellite-terrestrial integrated networks. The terrestrial relays act as the supplement of traditional small cells and cooperatively provide seamless coverage for users in the densely populated areas.To deal with the dynamic satellite backhaul links and backhaul capacity caused by the satellite mobility, severe co-channel interference in both satellite backhaul links and user links introduced by spectrum sharing,and the difference demands of users as well as heterogeneous characteristics of terrestrial backhaul and satellite backhaul, we propose a joint user association and satellite selection scheme to maximize the total sum rate. The optimization problem is formulated via jointly considering the influence of dynamic backhaul links, individual requirements and targeted interference management strategies, which is decomposed into two subproblems: user association and satellite selection. The user association is formulated as a nonconvex optimization problem, and solved through a low-complexity heuristic scheme to find the most suitable access point serving each user. Then, the satellite selection is resolved based on the cooperation among terrestrial relays to maximize the total backhaul capacity with the minimum date rate constraints. Finally,simulation results show the effectiveness of the proposed scheme in terms of total sum rate and power efficiency of TRs' backhaul.展开更多
Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resu...Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.展开更多
We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linkin...We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linking of polystyrene（PS） shell layer in the core-shell bottlebrush copolymers led to the formation of micropores and large-sized nanopores（meso/macrospores） in MONNs, respectively, while selective removal of polylactide（PLA） core layer generated mesoporous tubular structure. The size of PLA-templated mesoporous cores and porous structure both at micro-and meso-scale could be controlled by simple tuning of the ratio of core/shell or the PLA core fraction in the bottlebrush precursors. Moreover, the resultant MONNs showed a highly selective adsorption capacity for the positively charged dyes on the basis of multi-porosity and carboxylate group-rich structure. In addition, MONNs also exhibited effective performance in size-selective adsorption of biomacromolecules. This work represents a new avenue for the preparation of MONNs and also provides a new application for molecular bottlebrushes in nanotechnology.展开更多
In order to solve the problem of uncertainty and fuzzy information in the process of weapon equipment system selec-tion,a multi-attribute decision-making(MADM)method based on probabilistic hesitant fuzzy set(PHFS)is p...In order to solve the problem of uncertainty and fuzzy information in the process of weapon equipment system selec-tion,a multi-attribute decision-making(MADM)method based on probabilistic hesitant fuzzy set(PHFS)is proposed.Firstly,we introduce the concept of probability and fuzzy entropy to mea-sure the ambiguity,hesitation and uncertainty of probabilistic hesitant fuzzy elements(PHFEs).Sequentially,the expert trust network is constructed,and the importance of each expert in the network can be obtained by calculating the cumulative trust value under multiple trust propagation paths,so as to obtain the expert weight vector.Finally,we put forward an MADM method combining the probabilistic hesitant fuzzy entropy and grey rela-tion analysis(GRA)model,and an illustrative case is employed to prove the feasibility and effectiveness of the method when solving the weapon system selection decision-making problem.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The freq...A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The frequency tuning is achieved by inserting varactor diodes between the square mesh and each unattached square patch. The square mesh is divided into two parts for biasing the varactor diodes. Full-wave numerical simulations show that a wide tuning range can be achieved by changing the capacitances of these loaded varactors. Two homo-type samples using fixed lumped capacitors are fabricated and measured using a standard waveguide measurement setup. Excellent agreement between the measured and simulated results is demonstrated.展开更多
Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the se...Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the selective aging neuron model is discussed. We also give some properties of the new network and find that the neuron model displays a power-law behavior. If the brain network is small world-like network, the mean avalanche size is almost the same unless the aging parameter is big enough.展开更多
We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the propo...We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the proposed network to improve the average end-to-end throughput performance.More specifically,by assuming that the DF relay performs instantaneous throughput comparisons before performing corresponding protocols,we derive the expressions of system instantaneous bit-error-rate(BER),instantaneous end-to-end throughput,average end-to-end throughput,single node detection(SND)occurrence probability and average end-to-end BER over non-identical fading channels.Finally,theoretical analyses and Monte Carlo simulation results are presented.Evaluations show that:1) SPNC protocol outperforms the conventional physical-layer network coding(PNC) protocol in infrequent light shadowing(ILS),average shadowing(AS) and frequent heavy shadowing(FHS) Shadowed-Rician fading channels.2) As the satellite-relay channel fading gets more sewere,SPNC protocol can achieve more performance improvement than PNC protocol and the occurrence probability of SND protocol increases progressively.3) The occurrence probability increase of SND has a beneficial effect on the average end-to-end throughput in low signal-to-noise ratio(SNR) regime,while the occurrence probability decrease of SND has a beneficial effect on the average end-to-end BER in highSNR regime.展开更多
Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy o...Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and ...Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.展开更多
Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuro...Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuronal damage,it is crucial to find a biomarker to distinguish individuals with these diseases from healthy people.In this study,we construct a brain function network based on electroencephalography data to study changes in AD and MCI patients.Using a graph-theoretical approach,we examine connectivity features and explore their contributions to dementia recognition at edge,node,and network levels.We find that connectivity is reduced in AD and MCI patients compared with healthy controls.We also find that the edge-level features give the best performance when machine learning models are used to recognize dementia.The results of feature selection identify the top 50 ranked edge-level features constituting an optimal subset,which is mainly connected with the frontal nodes.A threshold analysis reveals that the performance of edge-level features is more sensitive to the threshold for the connection strength than that of node-and network-level features.In addition,edge-level features with a threshold of 0 provide the most effective dementia recognition.The K-nearest neighbors(KNN)machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0.Visualization of edge-level features suggests that there are more long connections linking the frontal region with the occipital and parietal regions in AD and MCI patients compared with healthy controls.Our codes are publicly available at https://github.com/Debbie-85/eeg-connectivity.展开更多
Underwater wireless sensor networks(UWSNs)have emerged as a new paradigm of real-time organized systems,which are utilized in a diverse array of scenarios to manage the underwater environment surrounding them.One of t...Underwater wireless sensor networks(UWSNs)have emerged as a new paradigm of real-time organized systems,which are utilized in a diverse array of scenarios to manage the underwater environment surrounding them.One of the major challenges that these systems confront is topology control via clustering,which reduces the overload of wireless communications within a network and ensures low energy consumption and good scalability.This study aimed to present a clustering technique in which the clustering process and cluster head(CH)selection are performed based on the Markov decision process and deep reinforcement learning(DRL).DRL algorithm selects the CH by maximizing the defined reward function.Subsequently,the sensed data are collected by the CHs and then sent to the autonomous underwater vehicles.In the final phase,the consumed energy by each sensor is calculated,and its residual energy is updated.Then,the autonomous underwater vehicle performs all clustering and CH selection operations.This procedure persists until the point of cessation when the sensor’s power has been reduced to such an extent that no node can become a CH.Through analysis of the findings from this investigation and their comparison with alternative frameworks,the implementation of this method can be used to control the cluster size and the number of CHs,which ultimately augments the energy usage of nodes and prolongs the lifespan of the network.Our simulation results illustrate that the suggested methodology surpasses the conventional low-energy adaptive clustering hierarchy,the distance-and energy-constrained K-means clustering scheme,and the vector-based forward protocol and is viable for deployment in an actual operational environment.展开更多
The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicl...The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicleto-infrastructure coordination frameworks partially address congestion mitigation,they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles.To bridge this gap,this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol,explicitly balancing system-wide efficiency(measured by network throughput)with priority vehicle rights protection(quantified via time-sensitive utility functions).The approach innovatively combines(1)a multi-vehicle dynamic routing model with quantifiable preference weights,and(2)a distributed Nash equilibrium solver updated using replicator sub-dynamic models.The framework was evaluated on an urban road network containing 25 intersections with mixed priority ratios(10%–30%of vehicles with priority access demand),and the framework showed consistent benefits on four benchmarks(Social routing algorithm,Shortest path algorithm,The comprehensive path optimisation model,The emergency vehicle timing collaborative evolution path optimization method)showed consistent benefits.Results showthat across different traffic demand configurations,the proposed method reduces the average vehicle traveling time by at least 365 s,increases the road network throughput by 48.61%,and effectively balances the road loads.This approach successfully meets the diverse traffic demands of various vehicle types while optimizing road resource allocations.The proposed coordination paradigm advances theoretical foundations for fairness-aware traffic optimization while offering implementable strategies for next-generation cooperative vehicle-road systems,particularly in smart city deployments requiring mixed-priority mobility guarantees.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12374459,61974138,and 92065106)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302400)+1 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0460000)the support from the Youth Innovation Promotion Association,Chinese Academy of Sciences(Grant Nos.2017156 and Y2021043)。
文摘Anti-phase domain defects easily form in the in-plane GaAs nanowires(NWs)grown on CMOS-compatiblegroup IV substrates,which makes it difficult to obtain GaAs NWs with a designed length and also leads to asignificant limitation in the growth of high-quality in-plane GaAs NW networks on such substrates.Here,wereport on the selective area growth of anti-phase domain-free in-plane GaAs NWs and NW networks on Ge(111)substrates.Detailed structural studies confirm that the GaAs NW grown using a large pattern period and GaAsNW networks grown by adding the Sb are both high-quality pure zinc-blende single crystals free of stackingfaults,twin defects,and anti-phase domain defects.Room-temperature photoluminescence measurements show asubstantial improvement in crystal quality and good consistency and uniformity of the GaAs NW networks.Ourwork provides useful insights into the controlled growth of high-quality anti-phase domain-defects-free in-planeIII-V NWs and NW networks.
基金supported by the US Appalachian Regional Commission(ARC)under Grant MU-21579-23。
文摘Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.
基金supported by the National Natural Science Foundation of China(No.51601019)the Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515010233)+1 种基金the Key Project of Shaanxi Province of Qinchuangyuan“Scientist and Engineer”Team Construction,China(No.2023KXJ-123)the Natural Science Foundation of Shaanxi Province,China(No.2024JC-YBMS-014).
文摘The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti_(34)Cu17Ni_(31.36)Hf_(17.64)and Ti_(36)Cu_(18)Ni_(29.44)Hf_(16.56)alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions.
基金supported by the Project of Guangdong Province High Level University Construction for Guangdong University of Technology(Grant No.262519003)the College Student Innovation Training Program of Guangdong University of Technology(Grant Nos.S202211845154 and xj2023118450384).
文摘Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.
基金National Natural Science Foundation of China(No.51475315)Innovative Project on the Integration of Industry,Education and Research of Jiangsu Province,China(No.BY2014059-10)
文摘In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.
基金supported by National Natural Science Foundation of China (No. 62201593, 62471480, and 62171466)。
文摘In this paper, we investigate a cooperation mechanism for satellite-terrestrial integrated networks. The terrestrial relays act as the supplement of traditional small cells and cooperatively provide seamless coverage for users in the densely populated areas.To deal with the dynamic satellite backhaul links and backhaul capacity caused by the satellite mobility, severe co-channel interference in both satellite backhaul links and user links introduced by spectrum sharing,and the difference demands of users as well as heterogeneous characteristics of terrestrial backhaul and satellite backhaul, we propose a joint user association and satellite selection scheme to maximize the total sum rate. The optimization problem is formulated via jointly considering the influence of dynamic backhaul links, individual requirements and targeted interference management strategies, which is decomposed into two subproblems: user association and satellite selection. The user association is formulated as a nonconvex optimization problem, and solved through a low-complexity heuristic scheme to find the most suitable access point serving each user. Then, the satellite selection is resolved based on the cooperation among terrestrial relays to maximize the total backhaul capacity with the minimum date rate constraints. Finally,simulation results show the effectiveness of the proposed scheme in terms of total sum rate and power efficiency of TRs' backhaul.
基金Supported by the Zimin Institute for Engineering Solutions Advancing Better Lives。
文摘Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.
基金financially supported by the National Natural Science Foundation of China (Nos. 51273066 and 21574042)Shanghai Pujiang Program (No. 13PJ1402300)
文摘We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linking of polystyrene（PS） shell layer in the core-shell bottlebrush copolymers led to the formation of micropores and large-sized nanopores（meso/macrospores） in MONNs, respectively, while selective removal of polylactide（PLA） core layer generated mesoporous tubular structure. The size of PLA-templated mesoporous cores and porous structure both at micro-and meso-scale could be controlled by simple tuning of the ratio of core/shell or the PLA core fraction in the bottlebrush precursors. Moreover, the resultant MONNs showed a highly selective adsorption capacity for the positively charged dyes on the basis of multi-porosity and carboxylate group-rich structure. In addition, MONNs also exhibited effective performance in size-selective adsorption of biomacromolecules. This work represents a new avenue for the preparation of MONNs and also provides a new application for molecular bottlebrushes in nanotechnology.
基金supported by the National Natural Science Foundation of China(71901214).
文摘In order to solve the problem of uncertainty and fuzzy information in the process of weapon equipment system selec-tion,a multi-attribute decision-making(MADM)method based on probabilistic hesitant fuzzy set(PHFS)is proposed.Firstly,we introduce the concept of probability and fuzzy entropy to mea-sure the ambiguity,hesitation and uncertainty of probabilistic hesitant fuzzy elements(PHFEs).Sequentially,the expert trust network is constructed,and the importance of each expert in the network can be obtained by calculating the cumulative trust value under multiple trust propagation paths,so as to obtain the expert weight vector.Finally,we put forward an MADM method combining the probabilistic hesitant fuzzy entropy and grey rela-tion analysis(GRA)model,and an illustrative case is employed to prove the feasibility and effectiveness of the method when solving the weapon system selection decision-making problem.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60901029, 61172148, and 60925005)the Natural Science Foundation of Shaanxi Province, China (Grant No. 2011JQ8040)
文摘A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The frequency tuning is achieved by inserting varactor diodes between the square mesh and each unattached square patch. The square mesh is divided into two parts for biasing the varactor diodes. Full-wave numerical simulations show that a wide tuning range can be achieved by changing the capacitances of these loaded varactors. Two homo-type samples using fixed lumped capacitors are fabricated and measured using a standard waveguide measurement setup. Excellent agreement between the measured and simulated results is demonstrated.
基金National Natural Science Foundation of China under Grant No.10675060
文摘Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the selective aging neuron model is discussed. We also give some properties of the new network and find that the neuron model displays a power-law behavior. If the brain network is small world-like network, the mean avalanche size is almost the same unless the aging parameter is big enough.
基金National Natural Science Foundation of China(No.62071146).
文摘We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the proposed network to improve the average end-to-end throughput performance.More specifically,by assuming that the DF relay performs instantaneous throughput comparisons before performing corresponding protocols,we derive the expressions of system instantaneous bit-error-rate(BER),instantaneous end-to-end throughput,average end-to-end throughput,single node detection(SND)occurrence probability and average end-to-end BER over non-identical fading channels.Finally,theoretical analyses and Monte Carlo simulation results are presented.Evaluations show that:1) SPNC protocol outperforms the conventional physical-layer network coding(PNC) protocol in infrequent light shadowing(ILS),average shadowing(AS) and frequent heavy shadowing(FHS) Shadowed-Rician fading channels.2) As the satellite-relay channel fading gets more sewere,SPNC protocol can achieve more performance improvement than PNC protocol and the occurrence probability of SND protocol increases progressively.3) The occurrence probability increase of SND has a beneficial effect on the average end-to-end throughput in low signal-to-noise ratio(SNR) regime,while the occurrence probability decrease of SND has a beneficial effect on the average end-to-end BER in highSNR regime.
基金Supported by the National Natural Science Foundation of China (61074153, 61104131)the Fundamental Research Fundsfor Central Universities of China (ZY1111, JD1104)
文摘Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金supported by the National Natural Science Foundation of China(724701189072431011).
文摘Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.62071451,62331025,and U21A20447)the National Key Research and Development Project(Grant No.2021YFC3002204)the CAMS Innovation Fund for Medical Sciences(Grant No.2019-I2M-5-019).
文摘Dementias such as Alzheimer disease(AD)and mild cognitive impairment(MCI)lead to problems with memory,language,and daily activities resulting from damage to neurons in the brain.Given the irreversibility of this neuronal damage,it is crucial to find a biomarker to distinguish individuals with these diseases from healthy people.In this study,we construct a brain function network based on electroencephalography data to study changes in AD and MCI patients.Using a graph-theoretical approach,we examine connectivity features and explore their contributions to dementia recognition at edge,node,and network levels.We find that connectivity is reduced in AD and MCI patients compared with healthy controls.We also find that the edge-level features give the best performance when machine learning models are used to recognize dementia.The results of feature selection identify the top 50 ranked edge-level features constituting an optimal subset,which is mainly connected with the frontal nodes.A threshold analysis reveals that the performance of edge-level features is more sensitive to the threshold for the connection strength than that of node-and network-level features.In addition,edge-level features with a threshold of 0 provide the most effective dementia recognition.The K-nearest neighbors(KNN)machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0.Visualization of edge-level features suggests that there are more long connections linking the frontal region with the occipital and parietal regions in AD and MCI patients compared with healthy controls.Our codes are publicly available at https://github.com/Debbie-85/eeg-connectivity.
文摘Underwater wireless sensor networks(UWSNs)have emerged as a new paradigm of real-time organized systems,which are utilized in a diverse array of scenarios to manage the underwater environment surrounding them.One of the major challenges that these systems confront is topology control via clustering,which reduces the overload of wireless communications within a network and ensures low energy consumption and good scalability.This study aimed to present a clustering technique in which the clustering process and cluster head(CH)selection are performed based on the Markov decision process and deep reinforcement learning(DRL).DRL algorithm selects the CH by maximizing the defined reward function.Subsequently,the sensed data are collected by the CHs and then sent to the autonomous underwater vehicles.In the final phase,the consumed energy by each sensor is calculated,and its residual energy is updated.Then,the autonomous underwater vehicle performs all clustering and CH selection operations.This procedure persists until the point of cessation when the sensor’s power has been reduced to such an extent that no node can become a CH.Through analysis of the findings from this investigation and their comparison with alternative frameworks,the implementation of this method can be used to control the cluster size and the number of CHs,which ultimately augments the energy usage of nodes and prolongs the lifespan of the network.Our simulation results illustrate that the suggested methodology surpasses the conventional low-energy adaptive clustering hierarchy,the distance-and energy-constrained K-means clustering scheme,and the vector-based forward protocol and is viable for deployment in an actual operational environment.
基金funded by the National Key Research and Development Program Project 2022YFB4300404.
文摘The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicleto-infrastructure coordination frameworks partially address congestion mitigation,they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles.To bridge this gap,this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol,explicitly balancing system-wide efficiency(measured by network throughput)with priority vehicle rights protection(quantified via time-sensitive utility functions).The approach innovatively combines(1)a multi-vehicle dynamic routing model with quantifiable preference weights,and(2)a distributed Nash equilibrium solver updated using replicator sub-dynamic models.The framework was evaluated on an urban road network containing 25 intersections with mixed priority ratios(10%–30%of vehicles with priority access demand),and the framework showed consistent benefits on four benchmarks(Social routing algorithm,Shortest path algorithm,The comprehensive path optimisation model,The emergency vehicle timing collaborative evolution path optimization method)showed consistent benefits.Results showthat across different traffic demand configurations,the proposed method reduces the average vehicle traveling time by at least 365 s,increases the road network throughput by 48.61%,and effectively balances the road loads.This approach successfully meets the diverse traffic demands of various vehicle types while optimizing road resource allocations.The proposed coordination paradigm advances theoretical foundations for fairness-aware traffic optimization while offering implementable strategies for next-generation cooperative vehicle-road systems,particularly in smart city deployments requiring mixed-priority mobility guarantees.
