Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predic...Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predictive behavior of dominant frequency responses in slope vibrations remain insufficiently understood and quantified.This study combines time-frequency analysis with machine learning to explore how the dominant frequency(f_(d))evolves in slopes under blasting.Continuous Wavelet Transform(CWT)was employed to characterize the temporal-frequency evolution of vibration signals,revealing that the dominant frequency exhibits strong spatial dependence and nonlinear variability influenced by blasting parameters and rock mass structures.Three machine learning models,namely Back Propagation Neural Network(BP),Support Vector Machine(SVM),and Random Forest(RF),were developed to predict f_(d) based on 1,000 monitoring samples obtained from numerical and field simulations.Among them,the RF model achieved the highest prediction accuracy,with mean absolute percentage errors(MAPE)below 15%,demonstrating strong robustness and generalization capability.Our analysis shows that external excitation factors,especially the loading frequency(f_(d)),mainly control the frequency response,while internal controlling factors,such as spatial position,lithological variation,and mechanical heterogeneity,modulate localized frequency amplification and energy redistribution.The results reveal that f_(d) tends to decrease with elevation and distance from the blasting source,whereas structural planes and weathered zones induce high-frequency amplification due to scattering and modal coupling effects.This study offers a new framework combining time-frequency analysis and machine learning to measure the nonlinear interaction between blasting and rock mass response,offering new insights for dynamic stability evaluation and hazard mitigation in complex rock slope systems.展开更多
The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,sate...The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.展开更多
With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge ...With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge Computing(MEC).To address this challenge,this paper constructs a cloud-edge-end collaborative MEC system that enables assembly robots to offload complex workflow tasks via multiple paths(horizontal,vertical,and hybrid collaboration).Tomitigate uncertainties arising frommobility,the location predictionmodule is employed.This enables proactive channel-quality estimation,providing forward-looking insights for offloading decisions.Furthermore,we propose a fairness-aware joint optimization framework.Utilizing an improved Multi-Agent Deep Reinforcement Learning(MADRL)algorithm whose reward function incorporates total system cost,positional reliability,and timeout penalties,the framework aims to balance resource distribution among assembly robots while maximizing system utility.Simulation results demonstrate that the proposed framework outperforms traditional offloading strategies.By integrating predictive mobility management with fairness-aware optimization,the framework offers a robust solution for dynamic industrial MEC environments.展开更多
The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,...The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,despite the fact that the scenario exists in practice.A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage.The heat release rate,the velocity of longitudinal air flow,and the location of the fire source were all varied.It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire.As the fire source’s position shifts downstream from the bifurcation point,the length of smoke back-layering progressively increases.A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow,significantly affecting the smoke back-layering length in the main tunnel.The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage,taking into account the positions of longitudinal fire sources.The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.展开更多
With the popularization of smart devices,Location-Based Services(LBS)greatly facilitates users’life,but at the same time brings the risk of users’location privacy leakage.Existing location privacy protection methods...With the popularization of smart devices,Location-Based Services(LBS)greatly facilitates users’life,but at the same time brings the risk of users’location privacy leakage.Existing location privacy protection methods are deficient,failing to reasonably allocate the privacy budget for non-outlier location points and ignoring the critical location information that may be contained in the outlier points,leading to decreased data availability and privacy exposure problems.To address these problems,this paper proposes a Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering(MLDP).The method first utilizes the DBSCAN clustering algorithm to classify location points into non-outliers and outliers.For non-outliers,the scoring function is designed by combining geographic information and semantic information,and the privacy budget is allocated according to the heat intensity of the hotspot area;for outliers,the scoring function is constructed to allocate the privacy budget based on their correlation with the hotspot area.By comprehensively considering the geographic information,semantic information,and correlation with hotspot areas of the location points,a reasonable privacy budget is assigned to each location point,andfinallynoise is added throughthe Laplacemechanismto realizeprivacyprotection.Experimental results on tworeal trajectory datasets,Geolife and T-Drive,show that the MLDP approach significantly improves data availability while effectively protecting location privacy.Compared with the comparison methods,the maximum available data ratio of MLDP is 1.Moreover,compared with the RandomNoise method,its execution time is 0.056–0.061 s longer,and the logRE is 0.12951–0.62194 lower;compared with KemeansDP,QTK-DP,DPK-F,IDP-SC,and DPK-Means-up methods,it saves 0.114–0.296 s in execution time,and the logRE is 0.01112–0.38283 lower.展开更多
Traveling wave(TW)fault location technology has been widely used in transmission systems due to its high accuracy and simplicity.Recently,there has been growing interest in applying this technology to medium voltage(M...Traveling wave(TW)fault location technology has been widely used in transmission systems due to its high accuracy and simplicity.Recently,there has been growing interest in applying this technology to medium voltage(MV)distribution lines.However,current practices in its deployment,signal measurement,and threshold setting are usually from the application experiences in transmission lines,despite significant differences in fault-induced wave characteristics between transmission and distribution systems.To address these issues,this paper investigates the feasibility and applicability of TW fault technology in MV overhead distribution lines through characteristic analysis of fault-induced TWs.The propagation characteristics of aerial mode and zero mode TWs on overhead distribution lines are studied.Furthermore,it evaluates the influence of critical distri-bution network components including distribution transformers,multi-branch configurations,and busbar structures on wave propagation characteristics.Deployment strategies for traveling wave fault location(TWFL)devices is proposed to address the unique challenges of distribution networks,while the fault location method is also improved.Field test results demonstrate the effectiveness of the proposed methodology,showing improved fault detection accuracy and system reliability in distri-bution network applications.This research provides practical implementation suggestions for TWFL technology in distribution networks.展开更多
A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa...A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.展开更多
Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requiremen...Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO).展开更多
This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 3:Mouse.This standard was released by the China Associ...This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 3:Mouse.This standard was released by the China Association of Acupuncture and Moxibustion on May 15,2020,implemented on October 31,2020,and published by Standards Press of China.The standard was drafted by the Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,and the Nanjing University of Chinese Medicine.Principal draftsmen:Xiang-hong JING and Xing-bang HUA.Participating draftsmen:Wan-zhu BAI,Bin XU,Dong-sheng XU,Yi GUO,Tie-ming MA,Xin-jun WANG,and Sheng-feng LU.展开更多
This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 2:Rat.This standard was released by the China Associat...This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 2:Rat.This standard was released by the China Association of Acupuncture and Moxibustion on May 15,2020,implemented on October 31,2020,and published by Standards Press of China.The standard was drafted by the Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,and the Nanjing University of Chinese Medicine.Principal draftsmen:Xiang-hong JING and Xing-bang HUA.Participating draftsmen:Wan-Zhu BAI,Bin XU,Dong-sheng XU,Yi GUO,Tie-ming MA,Xin-jun WANG,and Sheng-feng LU.展开更多
Despite advances in surgery,chemotherapy,and radiotherapy,the treatment of colorectal cancer(CRC)requires more personalized approaches based on tumor biology and molecular profiling.While some relevant mutations have ...Despite advances in surgery,chemotherapy,and radiotherapy,the treatment of colorectal cancer(CRC)requires more personalized approaches based on tumor biology and molecular profiling.While some relevant mutations have been associated with differential response to immunotherapy,such as RAS and BRAF mutations limiting response to anti-epithelial growth factor receptor drugs or microsatellite instability predisposing susceptibility to immune checkpoint inhibitors,the role of inflammation in dictating tumor progression and treatment response is still under investigation.Several inflammatory biomarkers have been identified to guide patient prognosis.These include the neutrophil-lymphocyte ratio,Glasgow prognostic score(GPS)and its modified version,lymphocyte-Creactive protein ratio,and platelet-lymphocyte ratio.However,these markers are not yet included in the standard clinical management of patients with CRC,and further research is needed to evaluate their efficacy in different patient populations.A recent study by Wang et al,published in the World Journal of Gastroenterology,sheds light on the prognostic significance of pan-immune-inflammation value(PIV)in CRC,particularly concerning primary tumor location.Specifically,the authors found that a high PIV was strongly correlated with worse disease-free survival in patients with left-sided colon cancer,whereas no such association was observed in patients with right-sided colon cancer.Integrating tumor location into the prognostic assessment of CRC may improve our ability to more accurately identify high-risk patients and develop personalized treatment plans that are more likely to improve patient outcomes.展开更多
Unmanned aerial vehicle(UAV)swarm network consisting of a collection of micro UAVs can be used for many applications.It is well established that packet routing is a fundamental problem to achieve UAV collaboration.How...Unmanned aerial vehicle(UAV)swarm network consisting of a collection of micro UAVs can be used for many applications.It is well established that packet routing is a fundamental problem to achieve UAV collaboration.However,the highly dynamic nature of UAVs,frequently changing network topologies and security issues,poses significant challenges to packet forwarding in UAV networks.The existing topology-based routing protocols are not well suited in UAV network due to their high controlling overhead or excessive end-to-end delay.Geographic routing is regarded as a promising solution,as it only requires local information.In order to enhance the accuracy and security of geographic routing in highly dynamic UAV network,in this paper,we propose a new predictive geographic(PGeo)routing strategy with location verification.First,a detection mechanism is adopted to recognize malicious UAVs falsifying their location.Then,an accurate average service time of a packet in the medium access control(MAC)layer is derived to assist location prediction.The proposed delay model can provide a theoretical basis for future work,and our simulation results reveal that PGeo outstrips the existing geographic routing protocols in terms of packet delivery ratio in the presence of location spoofing behavior.展开更多
A novel method is developed by utilizing the fractional frequency based multirange rulers to precisely position the passive inter-modulation(PIM)sources within radio frequency(RF)cables.The proposed method employs a s...A novel method is developed by utilizing the fractional frequency based multirange rulers to precisely position the passive inter-modulation(PIM)sources within radio frequency(RF)cables.The proposed method employs a set of fractional frequencies to create multiple measuring rulers with different metric ranges to determine the values of the tens,ones,tenths,and hundredths digits of the distance.Among these rulers,the one with the lowest frequency determines the maximum metric range,while the one with the highest frequency decides the highest achievable accuracy of the position system.For all rulers,the metric accuracy is uniquely determined by the phase accuracy of the detected PIM signals.With the all-phase Fourier transform method,the phases of the PIM signals at all fractional frequencies maintain almost the same accuracy,approximately 1°(about 1/360 wavelength in the positioning accuracy)at the signal-to-noise ratio(SNR)of 10 d B.Numerical simulations verify the effectiveness of the proposed method,improving the positioning accuracy of the cable PIM up to a millimeter level with the highest fractional frequency operating at 200 MHz.展开更多
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat...With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.展开更多
Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monit...Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monitoring.In this study,we propose an approach to locate microseismic events using a deep learning algorithm with surface data.A fully convolutional network is designed to predict source locations.The input data is the waveform of a microseismic event,and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x,y,and z dimensions.The theoretical dataset is generated to train the model,and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data.After applying the trained model to field data,the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method,making it promising for real-time microseismic monitoring.展开更多
The Thyristor-Controlled Series Compensator(TCSC)presents an effective solution for mitigating transmission congestion in power systems by regulating the distribution of line power flow.However,inherent faults within ...The Thyristor-Controlled Series Compensator(TCSC)presents an effective solution for mitigating transmission congestion in power systems by regulating the distribution of line power flow.However,inherent faults within the TCSC may lead to an unintended intensification of transmission congestion in other sections of the system post-installation,resulting in non-coherent phenomena of line blocking.In response to this challenge,this paper introduces a novel two-stage site selectionmethod for TCSC,emphasizing the enhancement of coherence in addressing line-blocking issues.Through rigorous non-coherent verification,this method mitigates the risk of line congestion deterioration due to TCSC faults.In the initial stage of the proposed method,TCSC faults are not considered during the extraction of system states.System state analysis is performed based on the TCSC site selection model,aiming to minimize system load reduction.The preliminary recommended installation position for TCSC is determined by sorting the frequency of TCSC installation occurrences on lines extracted from the analyzed system states.In the subsequent stage,accounting for the influence of TCSC faults on line faults,system operating states are extracted.Line and system congestion indices are calculated through the statistical analysis of the system state analysis results.The installation of TCSC at the preliminary position is scrutinized to identify non-coherent phenomena of line congestion on other lines.If such phenomena are observed,the installation position is excluded,and the TCSC site selection process is reinitiated based on the methodology from the first stage.To validate the effectiveness of the proposed method,a case study is conducted on a modified RBTS test system.The case study results indicate that,compared with TCSC siting schemes that do not consider transmission congestion non-coherency,the proposed non-coherency-based siting scheme reduces the system congestion expectation(SCE)and system congestion probability(SCP)by 17.7%and 11.4%,respectively,while lowering the LOLP and EENS by 2.56% and 4.55%,respectively.These results demonstrate that the proposed method can effectively alleviate transmission congestion and enhance the overall reliability of the system.展开更多
Data centers operate as physical digital infrastructure for generating,storing,computing,transmitting,and utilizing massive data and information,constituting the backbone of the flourishing digital economy across the ...Data centers operate as physical digital infrastructure for generating,storing,computing,transmitting,and utilizing massive data and information,constituting the backbone of the flourishing digital economy across the world.Given the lack of a consistent analysis for studying the locational factors of data centers and empirical deficiencies in longitudinal investigations on spatial dynamics of heterogeneous data centers,this paper develops a comprehensive analytical framework to examine the dynamic geographies and locational factors of techno-environmentally heterogeneous data centers across Chinese cities in the period of 2006–2021.First,we develop a“supply-demand-environment trinity”analytical framework as well as an accompanying evaluation indicator system with Chinese characteristics.Second,the dynamic geographies of data centers in Chinese cities over the last decades are characterized as spatial polarization in economically leading urban agglomerations alongside persistent interregional gaps across eastern,central,and western regions.Data centers present dual spatial expansion trajectories featuring outward radiation from eastern core urban agglomerations to adjacent peripheries and leapfrog diffusion to strategic central and western digital infrastructural hubs.Third,it is empirically verified that data center construction in Chinese cities over the last decades has been jointly influenced by supply-,demand-,and environment-side locational factors,echoing the efficacy of the trinity analytical framework.Overall,our findings demonstrate the temporal variance,contextual contingency,and attribute-based differentiation of locational factors underlying techno-environmentally heterogeneous data centers in Chinese cities.展开更多
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
文摘Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
基金supported by the National Natural Science Foundation of China(Grant Nos.52379098,52274075)the Project of Xingliao Talents Program(XLYC2203008)the Science and Technology Program Project of Liaoning Province(2025JH2/101900011).
文摘Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predictive behavior of dominant frequency responses in slope vibrations remain insufficiently understood and quantified.This study combines time-frequency analysis with machine learning to explore how the dominant frequency(f_(d))evolves in slopes under blasting.Continuous Wavelet Transform(CWT)was employed to characterize the temporal-frequency evolution of vibration signals,revealing that the dominant frequency exhibits strong spatial dependence and nonlinear variability influenced by blasting parameters and rock mass structures.Three machine learning models,namely Back Propagation Neural Network(BP),Support Vector Machine(SVM),and Random Forest(RF),were developed to predict f_(d) based on 1,000 monitoring samples obtained from numerical and field simulations.Among them,the RF model achieved the highest prediction accuracy,with mean absolute percentage errors(MAPE)below 15%,demonstrating strong robustness and generalization capability.Our analysis shows that external excitation factors,especially the loading frequency(f_(d)),mainly control the frequency response,while internal controlling factors,such as spatial position,lithological variation,and mechanical heterogeneity,modulate localized frequency amplification and energy redistribution.The results reveal that f_(d) tends to decrease with elevation and distance from the blasting source,whereas structural planes and weathered zones induce high-frequency amplification due to scattering and modal coupling effects.This study offers a new framework combining time-frequency analysis and machine learning to measure the nonlinear interaction between blasting and rock mass response,offering new insights for dynamic stability evaluation and hazard mitigation in complex rock slope systems.
基金National Natural Science Foundation of China(Grant Nos.42388102,42030105,42192535)the Open Fund of State Key Laboratory of Precision Geodesy,Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences(Grant No.SKLPG2025-1-5)。
文摘The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.
基金supported by the National Key R&D Program of China under Grant Nos.2024YFD2400200 and 2024YFD2400204supported in part by the Science and Technology Development Program for the Two Zones under Grant No.2023LQ02004.
文摘With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge Computing(MEC).To address this challenge,this paper constructs a cloud-edge-end collaborative MEC system that enables assembly robots to offload complex workflow tasks via multiple paths(horizontal,vertical,and hybrid collaboration).Tomitigate uncertainties arising frommobility,the location predictionmodule is employed.This enables proactive channel-quality estimation,providing forward-looking insights for offloading decisions.Furthermore,we propose a fairness-aware joint optimization framework.Utilizing an improved Multi-Agent Deep Reinforcement Learning(MADRL)algorithm whose reward function incorporates total system cost,positional reliability,and timeout penalties,the framework aims to balance resource distribution among assembly robots while maximizing system utility.Simulation results demonstrate that the proposed framework outperforms traditional offloading strategies.By integrating predictive mobility management with fairness-aware optimization,the framework offers a robust solution for dynamic industrial MEC environments.
基金funded by the National Natural Science Foundation of China(NSFC)under Grant No.52278415the National Key Research and Development Program of China under Grant No.2022YFC3801104+2 种基金Hebei Provincial Department of Education Project under Grant No.QN2025304the Innovation Fund Project of Hebei University of Engineering under Grant No.SJ2401002066the Sichuan Science and Technology Program under Grant No.2023YFS0407。
文摘The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,despite the fact that the scenario exists in practice.A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage.The heat release rate,the velocity of longitudinal air flow,and the location of the fire source were all varied.It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire.As the fire source’s position shifts downstream from the bifurcation point,the length of smoke back-layering progressively increases.A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow,significantly affecting the smoke back-layering length in the main tunnel.The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage,taking into account the positions of longitudinal fire sources.The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.
基金supported in part by the National Natural Science Foundation of China(Grant No.61971291)the Basic Scientific Research Project of the Liaoning Provincial Department of Education(LJ212410144013)+2 种基金the Leading Talent of the‘Xing Liao Ying Cai Plan’(XLYC2202013)the Shenyang Natural Science Foundation(22-315-6-10)the Guangxuan Scholar of Shenyang Ligong University(SYLUGXXZ202205).
文摘With the popularization of smart devices,Location-Based Services(LBS)greatly facilitates users’life,but at the same time brings the risk of users’location privacy leakage.Existing location privacy protection methods are deficient,failing to reasonably allocate the privacy budget for non-outlier location points and ignoring the critical location information that may be contained in the outlier points,leading to decreased data availability and privacy exposure problems.To address these problems,this paper proposes a Mix Location Privacy Preservation Method Based on Differential Privacy with Clustering(MLDP).The method first utilizes the DBSCAN clustering algorithm to classify location points into non-outliers and outliers.For non-outliers,the scoring function is designed by combining geographic information and semantic information,and the privacy budget is allocated according to the heat intensity of the hotspot area;for outliers,the scoring function is constructed to allocate the privacy budget based on their correlation with the hotspot area.By comprehensively considering the geographic information,semantic information,and correlation with hotspot areas of the location points,a reasonable privacy budget is assigned to each location point,andfinallynoise is added throughthe Laplacemechanismto realizeprivacyprotection.Experimental results on tworeal trajectory datasets,Geolife and T-Drive,show that the MLDP approach significantly improves data availability while effectively protecting location privacy.Compared with the comparison methods,the maximum available data ratio of MLDP is 1.Moreover,compared with the RandomNoise method,its execution time is 0.056–0.061 s longer,and the logRE is 0.12951–0.62194 lower;compared with KemeansDP,QTK-DP,DPK-F,IDP-SC,and DPK-Means-up methods,it saves 0.114–0.296 s in execution time,and the logRE is 0.01112–0.38283 lower.
基金supported by the National Natural Sci-ence Foundation of China(No.52107109).
文摘Traveling wave(TW)fault location technology has been widely used in transmission systems due to its high accuracy and simplicity.Recently,there has been growing interest in applying this technology to medium voltage(MV)distribution lines.However,current practices in its deployment,signal measurement,and threshold setting are usually from the application experiences in transmission lines,despite significant differences in fault-induced wave characteristics between transmission and distribution systems.To address these issues,this paper investigates the feasibility and applicability of TW fault technology in MV overhead distribution lines through characteristic analysis of fault-induced TWs.The propagation characteristics of aerial mode and zero mode TWs on overhead distribution lines are studied.Furthermore,it evaluates the influence of critical distri-bution network components including distribution transformers,multi-branch configurations,and busbar structures on wave propagation characteristics.Deployment strategies for traveling wave fault location(TWFL)devices is proposed to address the unique challenges of distribution networks,while the fault location method is also improved.Field test results demonstrate the effectiveness of the proposed methodology,showing improved fault detection accuracy and system reliability in distri-bution network applications.This research provides practical implementation suggestions for TWFL technology in distribution networks.
基金supported in part by the National Natural Science Foundation of China(62372385,62272078,62002337)Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX1486,CSTB2023NSCQ-LZX0069)。
文摘A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.
基金supported by the National Key Research and Development Program of China under No. 2019YFB1803200。
文摘Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO).
文摘This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 3:Mouse.This standard was released by the China Association of Acupuncture and Moxibustion on May 15,2020,implemented on October 31,2020,and published by Standards Press of China.The standard was drafted by the Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,and the Nanjing University of Chinese Medicine.Principal draftsmen:Xiang-hong JING and Xing-bang HUA.Participating draftsmen:Wan-zhu BAI,Bin XU,Dong-sheng XU,Yi GUO,Tie-ming MA,Xin-jun WANG,and Sheng-feng LU.
文摘This paper introduces part of the content in the association standard,T/CAAM0002–2020 Nomenclature and Location of Acupuncture Points for Laboratory Animals Part 2:Rat.This standard was released by the China Association of Acupuncture and Moxibustion on May 15,2020,implemented on October 31,2020,and published by Standards Press of China.The standard was drafted by the Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences,and the Nanjing University of Chinese Medicine.Principal draftsmen:Xiang-hong JING and Xing-bang HUA.Participating draftsmen:Wan-Zhu BAI,Bin XU,Dong-sheng XU,Yi GUO,Tie-ming MA,Xin-jun WANG,and Sheng-feng LU.
文摘Despite advances in surgery,chemotherapy,and radiotherapy,the treatment of colorectal cancer(CRC)requires more personalized approaches based on tumor biology and molecular profiling.While some relevant mutations have been associated with differential response to immunotherapy,such as RAS and BRAF mutations limiting response to anti-epithelial growth factor receptor drugs or microsatellite instability predisposing susceptibility to immune checkpoint inhibitors,the role of inflammation in dictating tumor progression and treatment response is still under investigation.Several inflammatory biomarkers have been identified to guide patient prognosis.These include the neutrophil-lymphocyte ratio,Glasgow prognostic score(GPS)and its modified version,lymphocyte-Creactive protein ratio,and platelet-lymphocyte ratio.However,these markers are not yet included in the standard clinical management of patients with CRC,and further research is needed to evaluate their efficacy in different patient populations.A recent study by Wang et al,published in the World Journal of Gastroenterology,sheds light on the prognostic significance of pan-immune-inflammation value(PIV)in CRC,particularly concerning primary tumor location.Specifically,the authors found that a high PIV was strongly correlated with worse disease-free survival in patients with left-sided colon cancer,whereas no such association was observed in patients with right-sided colon cancer.Integrating tumor location into the prognostic assessment of CRC may improve our ability to more accurately identify high-risk patients and develop personalized treatment plans that are more likely to improve patient outcomes.
基金co-supported by the National Key Research and Development Program of China(No.2024YFE0107900)the National Natural Science Foundation of China(No.62222105)+1 种基金the Natural Science Foundation of Guangdong Province,China(No.2024A1515010235)the 2024 China Unicom Guangdong low-altitude communication and sensing key technology research and digital twin platform research and development project(No.20241890).
文摘Unmanned aerial vehicle(UAV)swarm network consisting of a collection of micro UAVs can be used for many applications.It is well established that packet routing is a fundamental problem to achieve UAV collaboration.However,the highly dynamic nature of UAVs,frequently changing network topologies and security issues,poses significant challenges to packet forwarding in UAV networks.The existing topology-based routing protocols are not well suited in UAV network due to their high controlling overhead or excessive end-to-end delay.Geographic routing is regarded as a promising solution,as it only requires local information.In order to enhance the accuracy and security of geographic routing in highly dynamic UAV network,in this paper,we propose a new predictive geographic(PGeo)routing strategy with location verification.First,a detection mechanism is adopted to recognize malicious UAVs falsifying their location.Then,an accurate average service time of a packet in the medium access control(MAC)layer is derived to assist location prediction.The proposed delay model can provide a theoretical basis for future work,and our simulation results reveal that PGeo outstrips the existing geographic routing protocols in terms of packet delivery ratio in the presence of location spoofing behavior.
文摘A novel method is developed by utilizing the fractional frequency based multirange rulers to precisely position the passive inter-modulation(PIM)sources within radio frequency(RF)cables.The proposed method employs a set of fractional frequencies to create multiple measuring rulers with different metric ranges to determine the values of the tens,ones,tenths,and hundredths digits of the distance.Among these rulers,the one with the lowest frequency determines the maximum metric range,while the one with the highest frequency decides the highest achievable accuracy of the position system.For all rulers,the metric accuracy is uniquely determined by the phase accuracy of the detected PIM signals.With the all-phase Fourier transform method,the phases of the PIM signals at all fractional frequencies maintain almost the same accuracy,approximately 1°(about 1/360 wavelength in the positioning accuracy)at the signal-to-noise ratio(SNR)of 10 d B.Numerical simulations verify the effectiveness of the proposed method,improving the positioning accuracy of the cable PIM up to a millimeter level with the highest fractional frequency operating at 200 MHz.
基金supported by Natural Science Foundation of China(No.62371231)Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001Jiangsu Provincial Key Research and Development Program(No.BE2023027).
文摘With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.
基金supported by National Natural Science Foundation of China Grant(No.42004040,42474092,U2239204,and 42304145)Natural Science Foundation of Jiangxi Province Grant(20242BAB25190 and 20232BAB213077).
文摘Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring.Traditional methods,such as diffraction stacking,are time-consuming and challenging for real-time monitoring.In this study,we propose an approach to locate microseismic events using a deep learning algorithm with surface data.A fully convolutional network is designed to predict source locations.The input data is the waveform of a microseismic event,and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x,y,and z dimensions.The theoretical dataset is generated to train the model,and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data.After applying the trained model to field data,the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method,making it promising for real-time microseismic monitoring.
基金supported by Graduate Research and Innovation Program Project of Nanjing Institute of Technology(No.TB202517078).
文摘The Thyristor-Controlled Series Compensator(TCSC)presents an effective solution for mitigating transmission congestion in power systems by regulating the distribution of line power flow.However,inherent faults within the TCSC may lead to an unintended intensification of transmission congestion in other sections of the system post-installation,resulting in non-coherent phenomena of line blocking.In response to this challenge,this paper introduces a novel two-stage site selectionmethod for TCSC,emphasizing the enhancement of coherence in addressing line-blocking issues.Through rigorous non-coherent verification,this method mitigates the risk of line congestion deterioration due to TCSC faults.In the initial stage of the proposed method,TCSC faults are not considered during the extraction of system states.System state analysis is performed based on the TCSC site selection model,aiming to minimize system load reduction.The preliminary recommended installation position for TCSC is determined by sorting the frequency of TCSC installation occurrences on lines extracted from the analyzed system states.In the subsequent stage,accounting for the influence of TCSC faults on line faults,system operating states are extracted.Line and system congestion indices are calculated through the statistical analysis of the system state analysis results.The installation of TCSC at the preliminary position is scrutinized to identify non-coherent phenomena of line congestion on other lines.If such phenomena are observed,the installation position is excluded,and the TCSC site selection process is reinitiated based on the methodology from the first stage.To validate the effectiveness of the proposed method,a case study is conducted on a modified RBTS test system.The case study results indicate that,compared with TCSC siting schemes that do not consider transmission congestion non-coherency,the proposed non-coherency-based siting scheme reduces the system congestion expectation(SCE)and system congestion probability(SCP)by 17.7%and 11.4%,respectively,while lowering the LOLP and EENS by 2.56% and 4.55%,respectively.These results demonstrate that the proposed method can effectively alleviate transmission congestion and enhance the overall reliability of the system.
基金Major Program of National Social Science Foundation of China,No.21&ZD107。
文摘Data centers operate as physical digital infrastructure for generating,storing,computing,transmitting,and utilizing massive data and information,constituting the backbone of the flourishing digital economy across the world.Given the lack of a consistent analysis for studying the locational factors of data centers and empirical deficiencies in longitudinal investigations on spatial dynamics of heterogeneous data centers,this paper develops a comprehensive analytical framework to examine the dynamic geographies and locational factors of techno-environmentally heterogeneous data centers across Chinese cities in the period of 2006–2021.First,we develop a“supply-demand-environment trinity”analytical framework as well as an accompanying evaluation indicator system with Chinese characteristics.Second,the dynamic geographies of data centers in Chinese cities over the last decades are characterized as spatial polarization in economically leading urban agglomerations alongside persistent interregional gaps across eastern,central,and western regions.Data centers present dual spatial expansion trajectories featuring outward radiation from eastern core urban agglomerations to adjacent peripheries and leapfrog diffusion to strategic central and western digital infrastructural hubs.Third,it is empirically verified that data center construction in Chinese cities over the last decades has been jointly influenced by supply-,demand-,and environment-side locational factors,echoing the efficacy of the trinity analytical framework.Overall,our findings demonstrate the temporal variance,contextual contingency,and attribute-based differentiation of locational factors underlying techno-environmentally heterogeneous data centers in Chinese cities.
