In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)network.We first derive the secure transmission rate based ...In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)network.We first derive the secure transmission rate based on the mMIMO under imperfect channel state information.Based on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit power.Due to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach method.Next,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the solutions.Finally,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.展开更多
Purpose–This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.Design/method...Purpose–This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.Design/methodology/approach–This paper provides a comprehensive overview of the definition,connotations,characteristics and key technologies of digital twin technology.It also conducts a thorough analysis of the current state of digital twin applications,with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure.Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study,the paper details the construction process of the twin system from the perspectives of system architecture,theoretical definition,model construction and platform design.Findings–Digital twin technology can play an important role in the whole life cycle management,fault prediction and condition monitoring in the field of high-speed rail operation and maintenance.Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.Originality/value–This paper systematically summarizes the main components of digital twin railway.The general framework of the digital twin bridge is given,and its application in the field of intelligent operation and maintenance is prospected.展开更多
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
The advent of Grover’s algorithm presents a significant threat to classical block cipher security,spurring research into post-quantum secure cipher design.This study engineers quantum circuit implementations for thre...The advent of Grover’s algorithm presents a significant threat to classical block cipher security,spurring research into post-quantum secure cipher design.This study engineers quantum circuit implementations for three versions of the Ballet family block ciphers.The Ballet‑p/k includes a modular-addition operation uncommon in lightweight block ciphers.Quantum ripple-carry adder is implemented for both“32+32”and“64+64”scale to support this operation.Subsequently,qubits,quantum gates count,and quantum circuit depth of three versions of Ballet algorithm are systematically evaluated under quantum computing model,and key recovery attack circuits are constructed based on Grover’s algorithm against each version.The comprehensive analysis shows:Ballet-128/128 fails to NIST Level 1 security,while when the resource accounting is restricted to the Clifford gates and T gates set for the Ballet-128/256 and Ballet-256/256 quantum circuits,the design attains Level 3.展开更多
Background:Pneumonia remains a critical global health challenge,manifesting as a severe respiratory infection caused by viruses,bacteria,and fungi.Early detection is paramount for effective treatment,potentially reduc...Background:Pneumonia remains a critical global health challenge,manifesting as a severe respiratory infection caused by viruses,bacteria,and fungi.Early detection is paramount for effective treatment,potentially reducing mortality rates and optimizing healthcare resource allocation.Despite the importance of chest X-ray diagnosis,image analysis presents significant challenges,particularly in regions with limited medical expertise.This study addresses these challenges by proposing a computer-aided diagnosis system leveraging targeted image preprocessing and optimized deep learning techniques.Methods:We systematically evaluated contrast limited adaptive histogram equalization with varying clip limits for preprocessing chest X-ray images,demonstrating its effectiveness in enhancing feature visibility for diagnostic accuracy.Employing a comprehensive dataset of 5,863 X-ray images(1,583 pneumonia-negative,4,280 pneumonia-positive)collected from multiple healthcare facilities,we conducted a comparative analysis of transfer learning with pre-trained models including ResNet50v2,VGG-19,and MobileNetV2.Statistical validation was performed through 5-fold cross-validation.Results:Our results show that the contrast limited adaptive histogram equalization-enhanced approach with ResNet50v2 achieves 93.40%accuracy,outperforming VGG-19(84.90%)and MobileNetV2(89.70%).Statistical validation confirms the significance of these improvements(P<0.01).The development and optimization resulted in a lightweight mobile application(74 KB)providing rapid diagnostic support(1-2 s response time).Conclusion:The proposed approach demonstrates practical applicability in resource-constrained settings,balancing diagnostic accuracy with deployment efficiency,and offers a viable solution for computer-aided pneumonia diagnosis in areas with limited medical expertise.展开更多
In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this neces...In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.展开更多
The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)den...The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)denial conditions.Therefore,this paper proposes a UAV Clusters Information Geometry Fusion Positioning(UC-IGFP)method using pseudoranges from the LEO satellites.A novel graph model for linking and computing between the UAV clusters and LEO satellites was established.By utilizing probability to describe the positional states of UAVs and sensor errors,the distributed multivariate Probability Fusion Cooperative Positioning(PF-CP)algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster.Criteria to select the centroid of the cluster were set.A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory,which overcomes the discontinuity problem caused by the change of cluster centroids.Finally,the UC-IGFP method achieves the LEO continuous highprecision positioning of UAV clusters.The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster.It significantly improves the accuracy and reliability of LEO-UAV cluster positioning.The results of comprehensive simulation experiments show that the proposed method has a 30.5%improvement in performance over the mainstream positioning methods,with a positioning error of 14.267 m.展开更多
Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional h...Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional hidden dangers.Design/methodology/approach–The paper proposes a regional systematic risk identification method based on Bayesian and independent component analysis(ICA)theories.Firstly,the Gray Wolf Optimization(GWO)algorithm is used to partition each group of monitoring data in the hidden danger area,so that the data distribution characteristics within each sub-block are similar.Then,a distributed ICA early warning model is constructed to obtain prior knowledge such as control limits and statistics of the area under normal conditions.For the online evaluation process,the input data is partitioned following the above-mentioned procedure and the ICA statistics of each sub-block are calculated.The Bayesian method is applied to fuse online parameters with offline parameters,yielding statistics under a specific confidence interval.These statistics are then compared with the control limits–specifically,checking whether they exceed the pre-set confidence parameters–thus realizing the systematic risk identification of the hidden danger area.Findings–Through simulation experiments,the proposed method can integrate prior knowledge such as control limits and statistics to effectively determine the overall stability status of the area,thereby realizing the systematic risk identification of the hidden danger area.Originality/value–The proposed method leverages Bayesian theory to fuse online process parameters with offline parameters and further compares them with confidence parameters,thereby effectively enhancing the utilization efficiency of monitoring data and the robustness of the analytical model.展开更多
Purpose–This study aims to enhance the accuracy of key entity extraction from railway accident report texts and address challenges such as complex domain-specific semantics,data sparsity and strong inter-sentence sem...Purpose–This study aims to enhance the accuracy of key entity extraction from railway accident report texts and address challenges such as complex domain-specific semantics,data sparsity and strong inter-sentence semantic dependencies.A robust entity extraction method tailored for accident texts is proposed.Design/methodology/approach–This method is implemented through a dual-branch multi-task mutual learning model named R-MLP,which jointly performs entity recognition and accident phase classification.The model leverages a shared BERT encoder to extract contextual features and incorporates a sentence span indexing module to align feature granularity.A cross-task mutual learning mechanism is also introduced to strengthen semantic representation.Findings–R-MLP effectively mitigates the impact of semantic complexity and data sparsity in domain entities and enhances the model’s ability to capture inter-sentence semantic dependencies.Experimental results show that R-MLP achieves a maximum F1-score of 0.736 in extracting six types of key railway accident entities,significantly outperforming baseline models such as RoBERTa and MacBERT.Originality/value–This demonstrates the proposed method’s superior generalization and accuracy in domainspecific entity extraction tasks,confirming its effectiveness and practical value.展开更多
Software-Defined Perimeter(SDP)provides a logical perimeter to restrict access to services.However,due to the security vulnerability of a single controller and the programmability lack of a gateway,existing SDP is fac...Software-Defined Perimeter(SDP)provides a logical perimeter to restrict access to services.However,due to the security vulnerability of a single controller and the programmability lack of a gateway,existing SDP is facing challenges.To solve the above problems,we propose a flexible and secure SDP mechanism named Mimic SDP(MSDP).MSDP consists of endogenous secure controllers and a dynamic gateway.The controllers avoid single point failure by heterogeneity and redundancy.And the dynamic gateway realizes flexible forwarding in programmable data plane by changing the processing of packet construction and deconstruction,thereby confusing the potential adversary.Besides,we propose a Markov model to evaluate the security of our SDP framework.We implement a prototype of MSDP and evaluate it in terms of functionality,performance,and scalability in different groups of systems and languages.Evaluation results demonstrate that MSDP can provide a secure connection of 93.38%with a cost of 6.34%under reasonable configuration.展开更多
In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical inte...In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical interpretability.In this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to ML.We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.展开更多
In recent years,video coding has been widely applied in the field of video image processing to remove redundant information and improve data transmission efficiency.However,during the video coding process,irrelevant o...In recent years,video coding has been widely applied in the field of video image processing to remove redundant information and improve data transmission efficiency.However,during the video coding process,irrelevant objects such as background elements are often encoded due to environmental disturbances,resulting in the wastage of computational resources.Existing research on video coding efficiency optimization primarily focuses on optimizing encoding units during intra-frame or inter frame prediction after the generation of coding units,neglecting the optimization of video images before coding unit generation.To address this challenge,This work proposes an image semantic segmentation compression algorithm based on macroblock encoding,called image semantic segmentation compression algorithm based on macroblock encoding(ISSC-ME),which consists of three modules.(1)The semantic label generation module generates interesting object labels using a grid-based approach to reduce redundant coding of consecutive frames.(2)The image segmentation network module generates a semantic segmentation image using U-Net.(3)The macroblock coding module,is a block segmentation-based video encoding and decoding algorithm used to compress images and improve video transmission efficiency.Experimental results show that the proposed image semantic segmentation optimization algorithm can reduce the computational costs,and improve the overall accuracy by 1.00%and the mean intersection over union(IoU)by 1.20%.In addition,the proposed compression algorithm utilizes macroblock fusion,resulting in the image compression rate achieving 80.64%.It has been proven that the proposed algorithm greatly reduces data storage and transmission,and enables fast image compression processing at the millisecond level.展开更多
Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attack...Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.展开更多
Purpose-The rapid development of China’s railway construction has led to an increase in data generated by the high-speed rail(HSR)catenary system.Traditional management methods struggle with challenges such as poor i...Purpose-The rapid development of China’s railway construction has led to an increase in data generated by the high-speed rail(HSR)catenary system.Traditional management methods struggle with challenges such as poor information sharing,disconnected business applications and insufficient intelligence throughout the lifecycle.This study aims to address these issues by applying building information modeling(BIM)technology to improve lifecycle management efficiency for HSR catenary systems.Design/methodology/approach-Based on the lifecycle management needs of catenary engineering,incorporating the intelligent HSR“Model-Data Driven,Axis-Plane Coordination”philosophy,this paper constructs a BIM-based lifecycle management framework for HSR catenary engineering.Findings-This study investigates the full-process lifecycle management of the catenary system across various stages of design,manufacture,construction and operation,exploring integrated BIM models and data transmission methods,along with key technologies for BIM model transmission,transformation and lightweighting.Originality/value-This study establishes a lossless information circulation and transmission system for HSR catenary lifecycle management.Multi-stage applications are verified through the construction of the Chongqing-Kunming High-Speed Railway,comprehensive advancing the intelligent promotion and highquality development of catenary engineering.展开更多
This paper examines the application of the Verkle tree—an efficient data structure that leverages commitments and a novel proof technique in cryptographic solutions.Unlike traditional Merkle trees,the Verkle tree sig...This paper examines the application of the Verkle tree—an efficient data structure that leverages commitments and a novel proof technique in cryptographic solutions.Unlike traditional Merkle trees,the Verkle tree significantly reduces signature size by utilizing polynomial and vector commitments.Compact proofs also accelerate the verification process,reducing computational overhead,which makes Verkle trees particularly useful.The study proposes a new approach based on a non-positional polynomial notation(NPN)employing the Chinese Remainder Theorem(CRT).CRT enables efficient data representation and verification by decomposing data into smaller,indepen-dent components,simplifying computations,reducing overhead,and enhancing scalability.This technique facilitates parallel data processing,which is especially advantageous in cryptographic applications such as commitment and proof construction in Verkle trees,as well as in systems with constrained computational resources.Theoretical foundations of the approach,its advantages,and practical implementation aspects are explored,including resistance to potential attacks,application domains,and a comparative analysis with existing methods based on well-known parameters and characteristics.An analysis of potential attacks and vulnerabilities,including greatest common divisor(GCD)attacks,approximate multiple attacks(LLL lattice-based),brute-force search for irreducible polynomials,and the estimation of their total number,indicates that no vulnerabilities have been identified in the proposed method thus far.Furthermore,the study demonstrates that integrating CRT with Verkle trees ensures high scalability,making this approach promising for blockchain systems and other distributed systems requiring compact and efficient proofs.展开更多
Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and ...Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and compaction parameters.Design/methodology/approach-To address these issues,a novel multi-indicator IVCT method was proposed,including physical indicator dry density(ρd)and mechanical indicators dynamic stiffness(Krb)and bearing capacity coefficient(K20).Then,a series of IVCTs on HRGA under different compaction parameters were conducted with an improved vibration compactor,which could monitor the physical-mechanical indicators in real-time.Finally,the optimal vibration compaction parameters,including the moisture content(ω),the diameter-to-maximum particle size ratio(Rd),the thickness-to-maximum particle size ratio(Rh),the vibration frequency(f),the vibration mass(Mc)and the eccentric distance(re),were determined based on the evolution characteristics for the physical-mechanical indicators during compaction.Findings-All results indicated that theρd gradually increased and then stabilized,and the Krb initially increased and then decreased.Moreover,the inflection time of the Krb was present as the optimal compaction time(Tlp)during compaction.Additionally,optimal compaction was achieved whenωwas the water-holding content after mud pumping,Rd was 3.4,Rh was 3.5,f was the resonance frequency,and the ratio between the excitation force and the Mc was 1.8.Originality/value-The findings of this paper were significant for the quality control of HRGA compaction.展开更多
Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid...Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states.This limitation hinders timely information sharing and insightful path decisions for UAVs,resulting in inefficient or even failed collaborative search.Aiming at this issue,this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments.Specifically,an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment.Based on the fused information,we formalize the trajectory planning as a multiobjective optimization problem by jointly considering search performance and UAV energy harnessing.A multi-agent deep reinforcement learning based algorithm is proposed to solve it,where the reward-guided real-time path is determined to achieve an energyefficient search.Finally,extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption under uncertain search environments.展开更多
Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed ...Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.展开更多
Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situ...Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.展开更多
Accurate evaluation of elec-tron correlations is essential for the reliable quantitative de-scription of electronic struc-tures in strongly correlated sys-tems,including bond-dissociat-ing molecules,polyradicals,large...Accurate evaluation of elec-tron correlations is essential for the reliable quantitative de-scription of electronic struc-tures in strongly correlated sys-tems,including bond-dissociat-ing molecules,polyradicals,large conjugated molecules,and transition metal complex-es.To provide a user-friendly tool for studying such challeng-ing systems,our team developed Kylin 1.0[J.Comput.Chem.44,1316(2023)],an ab initio quantum chemistry program designed for efficient density matrix renormalization group(DMRG)and post-DMRG methods,enabling high-accuracy calculations with large active spaces.We have now further advanced the software with the release of Kylin 1.3,featuring optimized DMRG algorithms and an improved tensor contraction scheme in the diagonaliza-tion step.Benchmark calculations on the Mn_(4)CaO_(5)cluster demonstrate a remarkable speed-up of up to 16 fater than Kylin 1.0.Moreover,a more user-friendly and efficient algorithm[J.Chem.Theory Comput.17,3414(2021)]for sampling configurations from DMRG wavefunc-tion is implemented as well.Additionally,we have also implemented a spin-adapted version of the externally contracted multi-reference configuration interaction(EC-MRCI)method[J.Phys.Chem.A 128,958(2024)],further enhancing the program’s efficiency and accuracy for electron correlation calculations.展开更多
基金The Natural Science Foundation of Henan Province(No.232300421097)the Program for Science&Technology Innovation Talents in Universities of Henan Province(No.23HASTIT019,24HASTIT038)+2 种基金the China Postdoctoral Science Foundation(No.2023T160596,2023M733251)the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University(No.2023D11)the Song Shan Laboratory Foundation(No.YYJC022022003)。
文摘In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)network.We first derive the secure transmission rate based on the mMIMO under imperfect channel state information.Based on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit power.Due to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach method.Next,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the solutions.Finally,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.
基金funded by the China State Railway Group Co.,Ltd.Science and technology research and development program project(K2023G085).
文摘Purpose–This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.Design/methodology/approach–This paper provides a comprehensive overview of the definition,connotations,characteristics and key technologies of digital twin technology.It also conducts a thorough analysis of the current state of digital twin applications,with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure.Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study,the paper details the construction process of the twin system from the perspectives of system architecture,theoretical definition,model construction and platform design.Findings–Digital twin technology can play an important role in the whole life cycle management,fault prediction and condition monitoring in the field of high-speed rail operation and maintenance.Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.Originality/value–This paper systematically summarizes the main components of digital twin railway.The general framework of the digital twin bridge is given,and its application in the field of intelligent operation and maintenance is prospected.
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
基金State Key Lab of Processors,Institute of Computing Technology,Chinese Academy of Sciences(CLQ202516)the Fundamental Research Funds for the Central Universities of China(3282025047,3282024051,3282024009)。
文摘The advent of Grover’s algorithm presents a significant threat to classical block cipher security,spurring research into post-quantum secure cipher design.This study engineers quantum circuit implementations for three versions of the Ballet family block ciphers.The Ballet‑p/k includes a modular-addition operation uncommon in lightweight block ciphers.Quantum ripple-carry adder is implemented for both“32+32”and“64+64”scale to support this operation.Subsequently,qubits,quantum gates count,and quantum circuit depth of three versions of Ballet algorithm are systematically evaluated under quantum computing model,and key recovery attack circuits are constructed based on Grover’s algorithm against each version.The comprehensive analysis shows:Ballet-128/128 fails to NIST Level 1 security,while when the resource accounting is restricted to the Clifford gates and T gates set for the Ballet-128/256 and Ballet-256/256 quantum circuits,the design attains Level 3.
文摘Background:Pneumonia remains a critical global health challenge,manifesting as a severe respiratory infection caused by viruses,bacteria,and fungi.Early detection is paramount for effective treatment,potentially reducing mortality rates and optimizing healthcare resource allocation.Despite the importance of chest X-ray diagnosis,image analysis presents significant challenges,particularly in regions with limited medical expertise.This study addresses these challenges by proposing a computer-aided diagnosis system leveraging targeted image preprocessing and optimized deep learning techniques.Methods:We systematically evaluated contrast limited adaptive histogram equalization with varying clip limits for preprocessing chest X-ray images,demonstrating its effectiveness in enhancing feature visibility for diagnostic accuracy.Employing a comprehensive dataset of 5,863 X-ray images(1,583 pneumonia-negative,4,280 pneumonia-positive)collected from multiple healthcare facilities,we conducted a comparative analysis of transfer learning with pre-trained models including ResNet50v2,VGG-19,and MobileNetV2.Statistical validation was performed through 5-fold cross-validation.Results:Our results show that the contrast limited adaptive histogram equalization-enhanced approach with ResNet50v2 achieves 93.40%accuracy,outperforming VGG-19(84.90%)and MobileNetV2(89.70%).Statistical validation confirms the significance of these improvements(P<0.01).The development and optimization resulted in a lightweight mobile application(74 KB)providing rapid diagnostic support(1-2 s response time).Conclusion:The proposed approach demonstrates practical applicability in resource-constrained settings,balancing diagnostic accuracy with deployment efficiency,and offers a viable solution for computer-aided pneumonia diagnosis in areas with limited medical expertise.
基金supported by the National Key Research and Development Program of China(No.2021YFB2900504).
文摘In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.
基金supported in part by the National Natural Science Foundation of China(Nos.62171375,62271397,62001392,62101458,62173276,61803310 and 61801394)the Shenzhen Science and Technology Innovation ProgramChina(No.JCYJ20220530161615033)。
文摘The existing Low-Earth-Orbit(LEO)positioning performance cannot meet the requirements of Unmanned Aerial Vehicle(UAV)clusters for high-precision real-time positioning in the Global Navigation Satellite System(GNSS)denial conditions.Therefore,this paper proposes a UAV Clusters Information Geometry Fusion Positioning(UC-IGFP)method using pseudoranges from the LEO satellites.A novel graph model for linking and computing between the UAV clusters and LEO satellites was established.By utilizing probability to describe the positional states of UAVs and sensor errors,the distributed multivariate Probability Fusion Cooperative Positioning(PF-CP)algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster.Criteria to select the centroid of the cluster were set.A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory,which overcomes the discontinuity problem caused by the change of cluster centroids.Finally,the UC-IGFP method achieves the LEO continuous highprecision positioning of UAV clusters.The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster.It significantly improves the accuracy and reliability of LEO-UAV cluster positioning.The results of comprehensive simulation experiments show that the proposed method has a 30.5%improvement in performance over the mainstream positioning methods,with a positioning error of 14.267 m.
基金supported by Science and Technology Research and Development Program Project of China State Railway Group Co.,Ltd.(award number:K2024X010).
文摘Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional hidden dangers.Design/methodology/approach–The paper proposes a regional systematic risk identification method based on Bayesian and independent component analysis(ICA)theories.Firstly,the Gray Wolf Optimization(GWO)algorithm is used to partition each group of monitoring data in the hidden danger area,so that the data distribution characteristics within each sub-block are similar.Then,a distributed ICA early warning model is constructed to obtain prior knowledge such as control limits and statistics of the area under normal conditions.For the online evaluation process,the input data is partitioned following the above-mentioned procedure and the ICA statistics of each sub-block are calculated.The Bayesian method is applied to fuse online parameters with offline parameters,yielding statistics under a specific confidence interval.These statistics are then compared with the control limits–specifically,checking whether they exceed the pre-set confidence parameters–thus realizing the systematic risk identification of the hidden danger area.Findings–Through simulation experiments,the proposed method can integrate prior knowledge such as control limits and statistics to effectively determine the overall stability status of the area,thereby realizing the systematic risk identification of the hidden danger area.Originality/value–The proposed method leverages Bayesian theory to fuse online process parameters with offline parameters and further compares them with confidence parameters,thereby effectively enhancing the utilization efficiency of monitoring data and the robustness of the analytical model.
基金funded by the Technology Research and Development Plan Program of China State Railway Group Co.,Ltd.(No.Q2024T001)the Foundation of China Academy of Railway Sciences Co.,Ltd.(No:2024YJ259).
文摘Purpose–This study aims to enhance the accuracy of key entity extraction from railway accident report texts and address challenges such as complex domain-specific semantics,data sparsity and strong inter-sentence semantic dependencies.A robust entity extraction method tailored for accident texts is proposed.Design/methodology/approach–This method is implemented through a dual-branch multi-task mutual learning model named R-MLP,which jointly performs entity recognition and accident phase classification.The model leverages a shared BERT encoder to extract contextual features and incorporates a sentence span indexing module to align feature granularity.A cross-task mutual learning mechanism is also introduced to strengthen semantic representation.Findings–R-MLP effectively mitigates the impact of semantic complexity and data sparsity in domain entities and enhances the model’s ability to capture inter-sentence semantic dependencies.Experimental results show that R-MLP achieves a maximum F1-score of 0.736 in extracting six types of key railway accident entities,significantly outperforming baseline models such as RoBERTa and MacBERT.Originality/value–This demonstrates the proposed method’s superior generalization and accuracy in domainspecific entity extraction tasks,confirming its effectiveness and practical value.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB2901304)。
文摘Software-Defined Perimeter(SDP)provides a logical perimeter to restrict access to services.However,due to the security vulnerability of a single controller and the programmability lack of a gateway,existing SDP is facing challenges.To solve the above problems,we propose a flexible and secure SDP mechanism named Mimic SDP(MSDP).MSDP consists of endogenous secure controllers and a dynamic gateway.The controllers avoid single point failure by heterogeneity and redundancy.And the dynamic gateway realizes flexible forwarding in programmable data plane by changing the processing of packet construction and deconstruction,thereby confusing the potential adversary.Besides,we propose a Markov model to evaluate the security of our SDP framework.We implement a prototype of MSDP and evaluate it in terms of functionality,performance,and scalability in different groups of systems and languages.Evaluation results demonstrate that MSDP can provide a secure connection of 93.38%with a cost of 6.34%under reasonable configuration.
基金supported by the National Natural Science Foundation of China(No.92370117)the CAS Project for Young Scientists in Basic Research(No.YSBR-090)。
文摘In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical interpretability.In this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to ML.We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.
文摘In recent years,video coding has been widely applied in the field of video image processing to remove redundant information and improve data transmission efficiency.However,during the video coding process,irrelevant objects such as background elements are often encoded due to environmental disturbances,resulting in the wastage of computational resources.Existing research on video coding efficiency optimization primarily focuses on optimizing encoding units during intra-frame or inter frame prediction after the generation of coding units,neglecting the optimization of video images before coding unit generation.To address this challenge,This work proposes an image semantic segmentation compression algorithm based on macroblock encoding,called image semantic segmentation compression algorithm based on macroblock encoding(ISSC-ME),which consists of three modules.(1)The semantic label generation module generates interesting object labels using a grid-based approach to reduce redundant coding of consecutive frames.(2)The image segmentation network module generates a semantic segmentation image using U-Net.(3)The macroblock coding module,is a block segmentation-based video encoding and decoding algorithm used to compress images and improve video transmission efficiency.Experimental results show that the proposed image semantic segmentation optimization algorithm can reduce the computational costs,and improve the overall accuracy by 1.00%and the mean intersection over union(IoU)by 1.20%.In addition,the proposed compression algorithm utilizes macroblock fusion,resulting in the image compression rate achieving 80.64%.It has been proven that the proposed algorithm greatly reduces data storage and transmission,and enables fast image compression processing at the millisecond level.
基金partially supported by the National Natural Science Foundation (62272248)the Open Project Fund of State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences (CARCHA202108,CARCH201905)+1 种基金the Natural Science Foundation of Tianjin (20JCZDJC00610)Sponsored by Zhejiang Lab (2021KF0AB04)。
文摘Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.
基金supported by China Academy of Railway Sciences Foundation(Research on Multi Agent Collaborative Mechanism of Intelligent High Speed Rail System Based on Complex Adaptive System Theory,Grant 2023YJ392).
文摘Purpose-The rapid development of China’s railway construction has led to an increase in data generated by the high-speed rail(HSR)catenary system.Traditional management methods struggle with challenges such as poor information sharing,disconnected business applications and insufficient intelligence throughout the lifecycle.This study aims to address these issues by applying building information modeling(BIM)technology to improve lifecycle management efficiency for HSR catenary systems.Design/methodology/approach-Based on the lifecycle management needs of catenary engineering,incorporating the intelligent HSR“Model-Data Driven,Axis-Plane Coordination”philosophy,this paper constructs a BIM-based lifecycle management framework for HSR catenary engineering.Findings-This study investigates the full-process lifecycle management of the catenary system across various stages of design,manufacture,construction and operation,exploring integrated BIM models and data transmission methods,along with key technologies for BIM model transmission,transformation and lightweighting.Originality/value-This study establishes a lossless information circulation and transmission system for HSR catenary lifecycle management.Multi-stage applications are verified through the construction of the Chongqing-Kunming High-Speed Railway,comprehensive advancing the intelligent promotion and highquality development of catenary engineering.
基金funded by the Ministry of Science and Higher Education of Kazakhstan and carried out within the framework of the project AP23488112“Development and study of a quantum-resistant digital signature scheme based on a Verkle tree”at the Institute of Information and Computational Technologies.
文摘This paper examines the application of the Verkle tree—an efficient data structure that leverages commitments and a novel proof technique in cryptographic solutions.Unlike traditional Merkle trees,the Verkle tree significantly reduces signature size by utilizing polynomial and vector commitments.Compact proofs also accelerate the verification process,reducing computational overhead,which makes Verkle trees particularly useful.The study proposes a new approach based on a non-positional polynomial notation(NPN)employing the Chinese Remainder Theorem(CRT).CRT enables efficient data representation and verification by decomposing data into smaller,indepen-dent components,simplifying computations,reducing overhead,and enhancing scalability.This technique facilitates parallel data processing,which is especially advantageous in cryptographic applications such as commitment and proof construction in Verkle trees,as well as in systems with constrained computational resources.Theoretical foundations of the approach,its advantages,and practical implementation aspects are explored,including resistance to potential attacks,application domains,and a comparative analysis with existing methods based on well-known parameters and characteristics.An analysis of potential attacks and vulnerabilities,including greatest common divisor(GCD)attacks,approximate multiple attacks(LLL lattice-based),brute-force search for irreducible polynomials,and the estimation of their total number,indicates that no vulnerabilities have been identified in the proposed method thus far.Furthermore,the study demonstrates that integrating CRT with Verkle trees ensures high scalability,making this approach promising for blockchain systems and other distributed systems requiring compact and efficient proofs.
基金funded by the National Key R&D Program“Transportation Infrastructure”project(No.2022YFB2603400)the Technology Research and Development Plan Program of China State Railway Group Co.,Ltd.(No.Q2024T001)the National project pre research project of Suzhou City University(No.2023SGY019).
文摘Purpose-The indoor vibration compaction test(IVCT)was a key step in controlling the compaction quality for high-speed railway graded aggregate(HRGA),which currently had a research gap on the assessment indicators and compaction parameters.Design/methodology/approach-To address these issues,a novel multi-indicator IVCT method was proposed,including physical indicator dry density(ρd)and mechanical indicators dynamic stiffness(Krb)and bearing capacity coefficient(K20).Then,a series of IVCTs on HRGA under different compaction parameters were conducted with an improved vibration compactor,which could monitor the physical-mechanical indicators in real-time.Finally,the optimal vibration compaction parameters,including the moisture content(ω),the diameter-to-maximum particle size ratio(Rd),the thickness-to-maximum particle size ratio(Rh),the vibration frequency(f),the vibration mass(Mc)and the eccentric distance(re),were determined based on the evolution characteristics for the physical-mechanical indicators during compaction.Findings-All results indicated that theρd gradually increased and then stabilized,and the Krb initially increased and then decreased.Moreover,the inflection time of the Krb was present as the optimal compaction time(Tlp)during compaction.Additionally,optimal compaction was achieved whenωwas the water-holding content after mud pumping,Rd was 3.4,Rh was 3.5,f was the resonance frequency,and the ratio between the excitation force and the Mc was 1.8.Originality/value-The findings of this paper were significant for the quality control of HRGA compaction.
基金supported by National Natural Science Foundation of China(No.62202449 and No.62472410)National Key Research and Development Program of China(2021YFB2900102)。
文摘Multiple UAVs cooperative target search has been widely used in various environments,such as emergency rescue and traffic monitoring.However,uncertain communication network among UAVs exhibits unstable links and rapid topological fluctuations due to mission complexity and unpredictable environmental states.This limitation hinders timely information sharing and insightful path decisions for UAVs,resulting in inefficient or even failed collaborative search.Aiming at this issue,this paper proposes a multi-UAV cooperative search strategy by developing a real-time trajectory decision that incorporates autonomous connectivity to reinforce multi-UAV collaboration and achieve search acceleration in uncertain search environments.Specifically,an autonomous connectivity strategy based on node cognitive information and network states is introduced to enable effective message transmission and adapt to the dynamic network environment.Based on the fused information,we formalize the trajectory planning as a multiobjective optimization problem by jointly considering search performance and UAV energy harnessing.A multi-agent deep reinforcement learning based algorithm is proposed to solve it,where the reward-guided real-time path is determined to achieve an energyefficient search.Finally,extensive experimental results show that the proposed algorithm outperforms existing works in terms of average search rate and coverage rate with reduced energy consumption under uncertain search environments.
基金supported by the National Natural Science Foundation of China under Grant U21A20449in part by Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2。
文摘Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle platoons.Existing CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)topologies.However,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be guaranteed.This paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle spacing.To this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is proposed.Simulation results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.
基金supported by China Academy of Railway Sciences Corporation Limited(No.2021YJ127).
文摘Artificial intelligence,such as deep learning technology,has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations.To pursue a high facial expression recognition accuracy,the network model of deep learning is generally designed to be very deep while the model’s real-time performance is typically constrained and limited.With MobileNetV3,a lightweight model with a good accuracy,a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH(Single Stage Headless Face Detector)context module to expand the model’s perceptual field.In this article,the enhanced model named Res-MobileNetV3,could alleviate the subpar of real-time performance and compress the size of large network models,which can process information at a rate of up to 33 frames per second.Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets,it can bring a good balance on accuracy,real-time performance,model size and model complexity in practical applications.
基金supported by Shandong Provincial Nat-ural Science Foundation(ZR2024ZD30)the National Natural Science Foundation of China(Nos.22325302 and 22403100).
文摘Accurate evaluation of elec-tron correlations is essential for the reliable quantitative de-scription of electronic struc-tures in strongly correlated sys-tems,including bond-dissociat-ing molecules,polyradicals,large conjugated molecules,and transition metal complex-es.To provide a user-friendly tool for studying such challeng-ing systems,our team developed Kylin 1.0[J.Comput.Chem.44,1316(2023)],an ab initio quantum chemistry program designed for efficient density matrix renormalization group(DMRG)and post-DMRG methods,enabling high-accuracy calculations with large active spaces.We have now further advanced the software with the release of Kylin 1.3,featuring optimized DMRG algorithms and an improved tensor contraction scheme in the diagonaliza-tion step.Benchmark calculations on the Mn_(4)CaO_(5)cluster demonstrate a remarkable speed-up of up to 16 fater than Kylin 1.0.Moreover,a more user-friendly and efficient algorithm[J.Chem.Theory Comput.17,3414(2021)]for sampling configurations from DMRG wavefunc-tion is implemented as well.Additionally,we have also implemented a spin-adapted version of the externally contracted multi-reference configuration interaction(EC-MRCI)method[J.Phys.Chem.A 128,958(2024)],further enhancing the program’s efficiency and accuracy for electron correlation calculations.
