With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integ...With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation systems.In this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user.Firstly,we design their own feature extraction models for multiple heterogeneous data sources.Secondly,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning.Thirdly,we design each task’s own unique presentation layer for discriminant output of specific-task.Finally,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task.Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.展开更多
To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances...To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.展开更多
The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-...The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.展开更多
In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co...Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.展开更多
This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimiz...This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.展开更多
A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by ana...A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.展开更多
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter...With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.展开更多
Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities.This study used Shanghai’s catering industry as a case study,leveraging electron...Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities.This study used Shanghai’s catering industry as a case study,leveraging electronic reviews and consumer data sourced from third-party restaurant platforms collected in 2021.By performing weighted processing on two-dimensional point-of-interest(POI)data,clustering hotspots of high-dimensional restaurant data were identified.A hierarchical network of restaurant hotspots was constructed following the Central Place Theory(CPT)framework,while the Geo-Informatic Tupu method was employed to resolve the challenges posed by network deformation in multi-scale processes.These findings suggest the necessity of enhancing the spatial balance of Shanghai’s urban centers by moderately increasing the number and service capacity of suburban centers at the urban periphery.Such measures would contribute to a more optimized urban structure and facilitate the outward dispersion of comfort-oriented facilities such as the restaurant industry.At a finer spatial scale,the distribution of restaurant hotspots demonstrates a polycentric and symmetric spatial pattern,with a developmental trend radiating outward along the city’s ring roads.This trend can be attributed to the efforts of restaurants to establish connections with other urban functional spaces,leading to the reconfiguration of urban spaces,expansion of restaurant-dedicated land use,and the reorganization of associated commercial activities.The results validate the existence of a polycentric urban structure in Shanghai but also highlight the instability of the restaurant hotspot network during cross-scale transitions.展开更多
Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from...Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development.展开更多
As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique ...As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals.展开更多
Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often l...Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often lead to yield inaccurate features of influential users due to neighborhood aggregation,and require a large substantial amount of labeled data for training,making them difficult and challenging to apply in practice.To address this issue,we propose a semi-supervised contrastive learning method for identifying influential users.First,the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics related to influence;then,contrastive learning is employed to guide the encoder to generate various influence-related features for users;finally,with only a small amount of labeled data,an attention-based user classifier is trained to accurately identify influential users.Experiments conducted on three public social network datasets demonstrate that the proposed method,using only 20%of the labeled data as the training set,achieves F1 values that are 5.9%,5.8%,and 8.7%higher than those unsupervised EVC method,and it matches the performance of GNN-based methods such as DeepInf,InfGCN and OlapGN,which require 80%of labeled data as the training set.展开更多
With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce opera...With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions.In this regard,a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity‒carbon markets.Specifically,generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established,and a Stackelberg game-based interactive operation strategy is proposed for load aggregators(LAs)and energy-intensive industrial users in joint electricity‒carbon markets,where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit,whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA.Then,the existence and uniqueness of the Stackelberg equilibrium(SE)are analyzed,and a decentralized solution algorithm is suggested to reach the SE.Finally,the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users,which achieves a win-win result.展开更多
Objective： To understand the prevalence and behavioral risk factors of HIV infection among injection drug users in the Pearl River Delta Region （PRDR） of Guangdong province, and to provide evidence for establishing...Objective： To understand the prevalence and behavioral risk factors of HIV infection among injection drug users in the Pearl River Delta Region （PRDR） of Guangdong province, and to provide evidence for establishing effective intervention strategies. Methods： Face to face interviews were conducted and serum samples from injection drug users from detoxification centers and the community were collected for HIV screening. Results： 655 drug users were recruited and interviewed. The HIV seropositive rate was 29.0%. 99.5 % of subjects were injection drug users （IDUs）, of whom,75.4% reported sharing injection equipment. Conclusion： HIV prevalence among injection drug users is high in the PRDR of Guangdong. Injection drug use is the principal behavioral risk factor for HIV transmission. Pragmatic harm reduction programs should be implemented to prevent the spread of HIV infection.展开更多
Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of ble...Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.展开更多
New idea studying urban parks was opened up by "Reflection on Urban Park:Public Space and Multi-culture". By introducing anthropology into planning and design methodology of urban public space, it provided a...New idea studying urban parks was opened up by "Reflection on Urban Park:Public Space and Multi-culture". By introducing anthropology into planning and design methodology of urban public space, it provided a new research method for the planning of urban park in our country. Through introduction and assessment of the book, existing problems and suggestions of the construction of our urban parks were proposed. In the perspective of urban parks' users, by the method of ethnology and anthropology, cultural diversity of urban public space would be investigated, paying attention to the usage behavior and different cultural characteristics, thinking about how to respect ecology and environmental construction of parks and coordinate the relation between culture and ecology while cultural diversity was respected. What should be considered in the construction of urban parks was not only culture but also ecological environment protection when culture was respected and citizens' interests during environment construction. These were also the fundamental problems needed to be considered and solved in parks' planning, construction and operation.展开更多
基金This work is supported by State Grid Science and Technology Project under Grant No.520613180002,62061318C002the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)+4 种基金Weihai Science and Technology Development Program(2016DXGJMS15)Key Research and Development Program in Shandong Provincial(2017GGX90103)Sanming Science and Technology Project,Grant No.2015-G-6,Shandong province vocational education educational reform research project.Grant No.2017209Study and Development of Smart Agriculture Control System Based on Spark Big Data Decision(2017N0029)Jiangsu Province industrial Communication Technology Application Technology Innovation Team Project.
文摘With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation systems.In this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user.Firstly,we design their own feature extraction models for multiple heterogeneous data sources.Secondly,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning.Thirdly,we design each task’s own unique presentation layer for discriminant output of specific-task.Finally,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task.Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.
基金supported by the National Key R&D Program of China(No.2023YFB2603602)the National Natural Science Foundation of China(Nos.52222810 and 52178383).
文摘To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.
基金supported by the National Natural Science Foundation of China(21663032 and 22061041)the Open Sharing Platform for Scientific and Technological Resources of Shaanxi Province(2021PT-004)the National Innovation and Entrepreneurship Training Program for College Students of China(S202110719044)。
文摘The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
基金supported by the Sichuan Science and Technology Program(Nos.2024JDRC0100 and 2023YFQ0091)the National Natural Science Foundation of China(Nos.U21A20167 and 52475138)the Scientific Research Foundation of the State Key Laboratory of Rail Transit Vehicle System(No.2024RVL-T08).
文摘Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.
文摘This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.
文摘A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors.Collaborative filtering,a popular technique within recommender systems,predicts user interests by analyzing patterns in interactions and similarities between users,leveraging past behavior data to make personalized recommendations.Despite its popularity,collaborative filtering faces notable challenges,and one of them is the issue of grey-sheep users who have unusual tastes in the system.Surprisingly,existing research has not extensively explored outlier detection techniques to address the grey-sheep problem.To fill this research gap,this study conducts a comprehensive comparison of 12 outlier detectionmethods(such as LOF,ABOD,HBOS,etc.)and introduces innovative user representations aimed at improving the identification of outliers within recommender systems.More specifically,we proposed and examined three types of user representations:1)the distribution statistics of user-user similarities,where similarities were calculated based on users’rating vectors;2)the distribution statistics of user-user similarities,but with similarities derived from users represented by latent factors;and 3)latent-factor vector representations.Our experiments on the Movie Lens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.
文摘With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.
基金Under the auspices of the Key Program of National Natural Science Foundation of China(No.42030409)。
文摘Multi-source data fusion provides high-precision spatial situational awareness essential for analyzing granular urban social activities.This study used Shanghai’s catering industry as a case study,leveraging electronic reviews and consumer data sourced from third-party restaurant platforms collected in 2021.By performing weighted processing on two-dimensional point-of-interest(POI)data,clustering hotspots of high-dimensional restaurant data were identified.A hierarchical network of restaurant hotspots was constructed following the Central Place Theory(CPT)framework,while the Geo-Informatic Tupu method was employed to resolve the challenges posed by network deformation in multi-scale processes.These findings suggest the necessity of enhancing the spatial balance of Shanghai’s urban centers by moderately increasing the number and service capacity of suburban centers at the urban periphery.Such measures would contribute to a more optimized urban structure and facilitate the outward dispersion of comfort-oriented facilities such as the restaurant industry.At a finer spatial scale,the distribution of restaurant hotspots demonstrates a polycentric and symmetric spatial pattern,with a developmental trend radiating outward along the city’s ring roads.This trend can be attributed to the efforts of restaurants to establish connections with other urban functional spaces,leading to the reconfiguration of urban spaces,expansion of restaurant-dedicated land use,and the reorganization of associated commercial activities.The results validate the existence of a polycentric urban structure in Shanghai but also highlight the instability of the restaurant hotspot network during cross-scale transitions.
基金Sponsored by Beijing Youth Innovation Talent Support Program for Urban Greening and Landscaping——The 2024 Special Project for Promoting High-Quality Development of Beijing’s Landscaping through Scientific and Technological Innovation(KJCXQT202410).
文摘Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22A20598 and 52104107)the"Qinglan Project"of Jiangsu Colleges and Universities,Young Elite Scientists Sponsorship Program of Jiangsu Province(Grant No.TJ-2023-086).
文摘As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals.
基金supported by the National Key Project of the National Natural Science Foundation of China under Grant No.U23A20305.
文摘Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion.Existing identification methods based on Graph Neural Networks(GNNs)often lead to yield inaccurate features of influential users due to neighborhood aggregation,and require a large substantial amount of labeled data for training,making them difficult and challenging to apply in practice.To address this issue,we propose a semi-supervised contrastive learning method for identifying influential users.First,the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics related to influence;then,contrastive learning is employed to guide the encoder to generate various influence-related features for users;finally,with only a small amount of labeled data,an attention-based user classifier is trained to accurately identify influential users.Experiments conducted on three public social network datasets demonstrate that the proposed method,using only 20%of the labeled data as the training set,achieves F1 values that are 5.9%,5.8%,and 8.7%higher than those unsupervised EVC method,and it matches the performance of GNN-based methods such as DeepInf,InfGCN and OlapGN,which require 80%of labeled data as the training set.
基金grateful for the financial support from the National Key R&D Program of China(2023YFB2407300).
文摘With increasing awareness of environmental protection and rising carbon emission costs,participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions.In this regard,a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity‒carbon markets.Specifically,generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established,and a Stackelberg game-based interactive operation strategy is proposed for load aggregators(LAs)and energy-intensive industrial users in joint electricity‒carbon markets,where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit,whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA.Then,the existence and uniqueness of the Stackelberg equilibrium(SE)are analyzed,and a decentralized solution algorithm is suggested to reach the SE.Finally,the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users,which achieves a win-win result.
文摘Objective： To understand the prevalence and behavioral risk factors of HIV infection among injection drug users in the Pearl River Delta Region （PRDR） of Guangdong province, and to provide evidence for establishing effective intervention strategies. Methods： Face to face interviews were conducted and serum samples from injection drug users from detoxification centers and the community were collected for HIV screening. Results： 655 drug users were recruited and interviewed. The HIV seropositive rate was 29.0%. 99.5 % of subjects were injection drug users （IDUs）, of whom,75.4% reported sharing injection equipment. Conclusion： HIV prevalence among injection drug users is high in the PRDR of Guangdong. Injection drug use is the principal behavioral risk factor for HIV transmission. Pragmatic harm reduction programs should be implemented to prevent the spread of HIV infection.
文摘Multi-source seismic technology is an efficient seismic acquisition method that requires a group of blended seismic data to be separated into single-source seismic data for subsequent processing. The separation of blended seismic data is a linear inverse problem. According to the relationship between the shooting number and the simultaneous source number of the acquisition system, this separation of blended seismic data is divided into an easily determined or overdetermined linear inverse problem and an underdetermined linear inverse problem that is difficult to solve. For the latter, this paper presents an optimization method that imposes the sparsity constraint on wavefields to construct the object function of inversion, and the problem is solved by using the iterative thresholding method. For the most extremely underdetermined separation problem with single-shooting and multiple sources, this paper presents a method of pseudo-deblending with random noise filtering. In this method, approximate common shot gathers are received through the pseudo-deblending process, and the random noises that appear when the approximate common shot gathers are sorted into common receiver gathers are eliminated through filtering methods. The separation methods proposed in this paper are applied to three types of numerical simulation data, including pure data without noise, data with random noise, and data with linear regular noise to obtain satisfactory results. The noise suppression effects of these methods are sufficient, particularly with single-shooting blended seismic data, which verifies the effectiveness of the proposed methods.
文摘New idea studying urban parks was opened up by "Reflection on Urban Park:Public Space and Multi-culture". By introducing anthropology into planning and design methodology of urban public space, it provided a new research method for the planning of urban park in our country. Through introduction and assessment of the book, existing problems and suggestions of the construction of our urban parks were proposed. In the perspective of urban parks' users, by the method of ethnology and anthropology, cultural diversity of urban public space would be investigated, paying attention to the usage behavior and different cultural characteristics, thinking about how to respect ecology and environmental construction of parks and coordinate the relation between culture and ecology while cultural diversity was respected. What should be considered in the construction of urban parks was not only culture but also ecological environment protection when culture was respected and citizens' interests during environment construction. These were also the fundamental problems needed to be considered and solved in parks' planning, construction and operation.
