When discharge faults occur in dry air switchgear,the air decomposes to produce diverse gases,with NO_(2) reaching the highest levels.Detecting the NO_(2) level can reflect the operation status of the equipment.This p...When discharge faults occur in dry air switchgear,the air decomposes to produce diverse gases,with NO_(2) reaching the highest levels.Detecting the NO_(2) level can reflect the operation status of the equipment.This paper proposes to combine ZnO cluster with MoS_(2) to improve the gassensitive properties of the monolayer.Based on the Density Functional Theory(DFT),the effect of(ZnO)n size on the behavior of MoS_(2 )is considered.Key parameters such as adsorption energy and band gap of(ZnO)n-MoS_(2)/NO_(2) system were calculated.The ZnO-MoS_(2) heterojunction was successfully synthesized by a hydrothermal method.The gas sensor exhibits a remarkable response and a fast response-recovery time to 100 ppm NO_(2).In addition,it demonstrates excellent selectivity,long-term stability and a low detection limit.This work confirms the potential of the ZnO-MoS_(2) composite structure as a highly effective gas sensor for NO_(2) detection,which provides valuable theoretical and experimental insights for fault detection in dry air switchgear.展开更多
This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid s...This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.展开更多
Memristive neural network has attracted tremendous attention since the memristor array can perform parallel multiplyaccumulate calculation(MAC)operations and memory-computation operations as compared with digital CMOS...Memristive neural network has attracted tremendous attention since the memristor array can perform parallel multiplyaccumulate calculation(MAC)operations and memory-computation operations as compared with digital CMOS hardware systems.However,owing to the variability of the memristor,the implementation of high-precision neural network in memristive computation units is still difficult.Existing learning algorithms for memristive artificial neural network(ANN)is unable to achieve the performance comparable to high-precision by using CMOS-based system.Here,we propose an algorithm based on off-chip learning for memristive ANN in low precision.Training the ANN in the high-precision in digital CPUs and then quantifying the weight of the network to low precision,the quantified weights are mapped to the memristor arrays based on VTEAM model through using the pulse coding weight-mapping rule.In this work,we execute the inference of trained 5-layers convolution neural network on the memristor arrays and achieve an accuracy close to the inference in the case of high precision(64-bit).Compared with other algorithms-based off-chip learning,the algorithm proposed in the present study can easily implement the mapping process and less influence of the device variability.Our result provides an effective approach to implementing the ANN on the memristive hardware platform.展开更多
Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thic...Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.展开更多
The COVID-19 outbreak has significantly disrupted the lives of individuals worldwide.Following the lifting of COVID-19 interventions,there is a heightened risk of future outbreaks from other circulating respiratory in...The COVID-19 outbreak has significantly disrupted the lives of individuals worldwide.Following the lifting of COVID-19 interventions,there is a heightened risk of future outbreaks from other circulating respiratory infections,such as influenza-like illness(ILI).Accurate prediction models for ILI cases are crucial in enabling governments to implement necessary measures and persuade individuals to adopt personal precautions against the disease.This paper aims to provide a forecasting model for ILI cases with actual cases.We propose a specific model utilizing the partial differential equation(PDE)that will be developed and validated using real-world data obtained from the Chinese National Influenza Center.Our model combines the effects of transboundary spread among regions in China mainland and human activities’impact on ILI transmission dynamics.The simulated results demonstrate that our model achieves excellent predictive performance.Additionally,relevant factors influencing the dissemination are further examined in our analysis.Furthermore,we investigate the effectiveness of travel restrictions on ILI cases.Results can be used to utilize to mitigate the spread of disease.展开更多
In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environ...In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.展开更多
There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of ...There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics.Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.展开更多
To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and tr...To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and transfer learning-based method for printing defect detection was proposed in this study.This method enabled defect detection in printed surface without the need for extensive labeled defect.The ResNet101-SSTU model was used in this study.On the public dataset of printing defect images,the ResNet101-SSTU model not only achieves comparable performance and speed to mainstream supervised learning detection models but also successfully addresses some of the detection challenges encountered in supervised learning.The proposed ResNet101-SSTU model effectively eliminates the need for extensive defect samples and labeled data in training,providing an efficient solution for quality inspection in the printing industry.展开更多
With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of...With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of The Open University of China online education platform were taken as the research object,their user behavior data was collected,cleaned,and analyzed with text mining.The RFM model and the improved K-Means algorithm were used to construct the user portrait of the platform group and the needs and preferences of different types of the users were analyzded.Chinese word segmentation was used to show the key words of different types of users and the word cloud of their using frequency.The focus of different user groups was determined to facilitate for the follow-up course recommendation and precision marketing.Experimental results showed that the improved K-Means algorithm can well depict the behavior of group users.The index of silhouette score was improved to 0.811 by the improved K-Means algorithm,from random uncertainty to a fixed value,which can effectively solve the problem of inconsistent results caused by outlier sample points.展开更多
In the data-driven era of the internet and business environments,constructing accurate user profiles is paramount for personalized user understanding and classification.The traditional TF-IDF algorithm has some limita...In the data-driven era of the internet and business environments,constructing accurate user profiles is paramount for personalized user understanding and classification.The traditional TF-IDF algorithm has some limitations when evaluating the impact of words on classification results.Consequently,an improved TF-IDF-K algorithm was introduced in this study,which included an equalization factor,aimed at constructing user profiles by processing and analyzing user search records.Through the training and prediction capabilities of a Support Vector Machine(SVM),it enabled the prediction of user demographic attributes.The experimental results demonstrated that the TF-IDF-K algorithm has achieved a significant improvement in classification accuracy and reliability.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on...The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on the YOLOv8 model was proposed in this paper.Firstly,the Global Attention Module(GAM)was introduced to enhance data prediction capability and model expression ability.Secondly,the Space-to-Depth(SPD)module was incorporated into the backbone network for fine-grained feature information learning to mitigate feature information loss due to down-sampling.Finally,a 160 pixels×160 pixels feature layer was added to expand small target feature information and effectively reduce instances of missed targets.Experimental validation on the public VisDrone2019 UAV small target detaset demonstrated that the proposed model achieves significant performance improvement in small target detection tasks compared to existing models,exhibiting higher accuracy.展开更多
Brain-inspired intelligence is considered to be a computational model with the most promising potential to overcome the shortcomings of the von Neumann architecture,making it a current research hotspot.Due to advantag...Brain-inspired intelligence is considered to be a computational model with the most promising potential to overcome the shortcomings of the von Neumann architecture,making it a current research hotspot.Due to advantages such as nonvolatility,high density,low power consumption,and high response ratio,memristors are regarded as devices with promising applications in brain-inspired intelligence.This paper proposes a physical Ag/HfO_(x)/FeO_(x)/Pt memristor model.The Ag/HfO_(x)/FeO_(x)/Pt memristor is first fabricated using magnetron sputtering,and its internal principles and characteristics are then thoroughly analyzed.Furthermore,we construct a corresponding physical memristor model which achieves a simulation accuracy of up to 99.72%for the physical memristor.We design a fully functional Pavlovian associative memory circuit,realizing functions including generalization,primary differentiation,secondary differentiation,and forgetting.Finally,the circuit is validated through PSPICE simulation and analysis.展开更多
Spontaneous imbibition is an important phenomenon in tight reservoirs.The existence of a large number of fractures and micro-nano pores is the key factor affecting the spontaneous imbibition of tight reservoirs.In thi...Spontaneous imbibition is an important phenomenon in tight reservoirs.The existence of a large number of fractures and micro-nano pores is the key factor affecting the spontaneous imbibition of tight reservoirs.In this study,based on high-pressure mercury injection and nuclear magnetic resonance experiments,the pore distribution of tight sandstone is described.The influence of fractures,core porosity and permeability,and surfactants on the spontaneous imbibition of tight sandstone are studied by physical fracturing,interfacial tension test,wettability test and imbibition experiments.The results show that:the pore radius of tight sandstone is concentrated in 0.01-1 mm.Fractures can effectively reduce the oil drop adsorption on the core surface,enhancing the imbibition recovery of the tight sandstone with an increase of about 10%.As the number of fractures increases,the number of oil droplets adsorbed on the core surface decrease and the imbibition rate increases.The imbibition recovery increases with the increase in pore connectivity,while the imbibition rate increases with the increases in core porosity and permeability.The surfactant can improve the core water wettability and reduce the oilwater interfacial tension,reducing the adsorption of oil droplets on the core surface,and improving the core imbibition recovery with an increase of about 15%.In a word,the existence of fractures and surfactants can enhance the pore connectivity of the reservoir,reduce the adsorption of oil droplets on the core surface,and improve the imbibition rate and recovery rate of the tight oil reservoir.展开更多
In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible...In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible for one-and two-dimensional low-field and low signal to noise ratio NMR data.In this method,the low-rank and sparsity restraints are introduced into the objective function instead of the smoothing term.The low-rank features in relaxation spectra are extracted to ensure the local characteristics and morphology of spectra.The sparsity and residual term are contributed to the resolution and precision of spectra,with the elimination of the redundant relaxation components.Optimization process of the objective function is designed with alternating direction method of multiples,in which the objective function is decomposed into three subproblems to be independently solved.The optimum solution can be obtained by alternating iteration and updating process.At first,numerical simulations are conducted on synthetic echo data with different signal-to-noise ratios,to optimize the desirable regularization parameters and verify the feasibility and effectiveness of proposed method.Then,NMR experiments on solutions and artificial sandstone samples are conducted and analyzed,which validates the robustness and reliability of the proposed method.The results from simulations and experiments have demonstrated that the suggested method has unique advantages for improving the resolution of relaxation spectra and enhancing the ability of fluid quantitative identification.展开更多
Kinds of complex-structure wells can effectively improve production,which are widely used.However,in the process of drilling and completion,complex-structure wells with long drilling cycle and large exposed area of re...Kinds of complex-structure wells can effectively improve production,which are widely used.However,in the process of drilling and completion,complex-structure wells with long drilling cycle and large exposed area of reservoir can lead to the fact that reservoir near wellbore is more vulnerable to the working fluid invasion,resulting in more serious formation damage.In order to quantitatively describe the reservoir formation damage in the construction of complex-structure well,taking the inclined well section as the research object,the coordinate transformation method and conformal transformation method are given according to the flow characteristics of reservoir near wellbore in anisotropic reservoir.Then the local skin factor in orthogonal plane of wellbore is deduced.Considering the un-even distribution of local skin factor along the wellbore,the oscillation decreasing model and empirical equation model of damage zone radius distribution along the wellbore direction are established and then the total skin factor model of the whole well is superimposed to realize the reservoir damage evaluation of complex-structure wells.Combining the skin factor model with the production model,the production of complex-structure wells can be predicted more accurately.The two field application cases show that the accuracy of the model can be more than 90%,which can also fully reflect the invasion characteristics of drilling and completion fluid in any well section of complex-structure wells in anisotropic reservoir,so as to further provide guidance for the scientific establish-ment of reservoir production system.展开更多
Borehole nuclear magnetic resonance(NMR)is a powerful technology to characterize the petrophysical properties of underground reservoirs in the petroleum industry.The rising complexity of oil and gas exploration and de...Borehole nuclear magnetic resonance(NMR)is a powerful technology to characterize the petrophysical properties of underground reservoirs in the petroleum industry.The rising complexity of oil and gas exploration and development objectives,as well as the novel application contexts of underground reservoirs,have led to increasingly demanding requirements on borehole NMR technology including instrument design and related processing methods.This mini review summarizes the advances and applications of borehole NMR instruments along with some future possibilities.It may be helpful for researchers and engineers in the petroleum industry to understand the development status and future trends of borehole NMR technology.展开更多
Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to d...Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.展开更多
Since December 2019,the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade,national policies and the natural environment.To closely monitor the emergence of new COVID-...Since December 2019,the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade,national policies and the natural environment.To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy,we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations(PDEs),which captures epidemic diffusion along the edges of a network driven by population flow data.In this paper,we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19.Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models.Furthermore,we study the effectiveness of intervention measures,such as traffic lockdowns and social distancing,which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model.To our knowledge,this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.展开更多
The thermostatted system is a conservative system different from Hamiltonian systems,and has attracted much attention because of its rich and different nonlinear dynamics.We report and analyze the multiple equilibria ...The thermostatted system is a conservative system different from Hamiltonian systems,and has attracted much attention because of its rich and different nonlinear dynamics.We report and analyze the multiple equilibria and curve axes of the cluster-shaped conservative flows generated from a generalized thermostatted system.It is found that the cluster-shaped structure is reflected in the geometry of the Hamiltonian,such as isosurfaces and local centers,and the shapes of cluster-shaped chaotic flows and invariant tori rely on the isosurfaces determined by initial conditions,while the numbers of clusters are subject to the local centers solved by the Hessian matrix of the Hamiltonian.Moreover,the study shows that the cluster-shaped chaotic flows and invariant tori are chained together by curve axes,which are the segments of equilibrium curves of the generalized thermostatted system.Furthermore,the interesting results are vividly demonstrated by the numerical simulations.展开更多
基金the financial support of National Natural Science Foundation of China(Nos.52207175 and 52407178)。
文摘When discharge faults occur in dry air switchgear,the air decomposes to produce diverse gases,with NO_(2) reaching the highest levels.Detecting the NO_(2) level can reflect the operation status of the equipment.This paper proposes to combine ZnO cluster with MoS_(2) to improve the gassensitive properties of the monolayer.Based on the Density Functional Theory(DFT),the effect of(ZnO)n size on the behavior of MoS_(2 )is considered.Key parameters such as adsorption energy and band gap of(ZnO)n-MoS_(2)/NO_(2) system were calculated.The ZnO-MoS_(2) heterojunction was successfully synthesized by a hydrothermal method.The gas sensor exhibits a remarkable response and a fast response-recovery time to 100 ppm NO_(2).In addition,it demonstrates excellent selectivity,long-term stability and a low detection limit.This work confirms the potential of the ZnO-MoS_(2) composite structure as a highly effective gas sensor for NO_(2) detection,which provides valuable theoretical and experimental insights for fault detection in dry air switchgear.
基金Project supported by Jilin Provincial Science and Technology Development Plan(Grant No.20220101137JC).
文摘This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.
基金the National Natural Science Foundation of China(Grant Nos.62076208,62076207,and U20A20227)the National Key Research and Development Program of China(Grant No.2018YFB1306600)。
文摘Memristive neural network has attracted tremendous attention since the memristor array can perform parallel multiplyaccumulate calculation(MAC)operations and memory-computation operations as compared with digital CMOS hardware systems.However,owing to the variability of the memristor,the implementation of high-precision neural network in memristive computation units is still difficult.Existing learning algorithms for memristive artificial neural network(ANN)is unable to achieve the performance comparable to high-precision by using CMOS-based system.Here,we propose an algorithm based on off-chip learning for memristive ANN in low precision.Training the ANN in the high-precision in digital CPUs and then quantifying the weight of the network to low precision,the quantified weights are mapped to the memristor arrays based on VTEAM model through using the pulse coding weight-mapping rule.In this work,we execute the inference of trained 5-layers convolution neural network on the memristor arrays and achieve an accuracy close to the inference in the case of high precision(64-bit).Compared with other algorithms-based off-chip learning,the algorithm proposed in the present study can easily implement the mapping process and less influence of the device variability.Our result provides an effective approach to implementing the ANN on the memristive hardware platform.
基金Supported by the National Natural Science Foundation of China(42272110)CNPC-China University of Petroleum(Beijing)Strategic Cooperation Project(ZLZX2020-02).
文摘Aiming at the problem that the data-driven automatic correlation methods which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness,an intelligent automatic correlation method of oil-bearing strata based on pattern constraints is formed.We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata,constraining the correlation process by stratigraphic sedimentary patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate the correlation of marker layers and the interfaces of each stratum.The application in Shishen 100 block in the Shinan Oilfield of the Bohai Bay Basin shows that the coincidence rate of the marker layers identified by this method is over 95.00%,and the average coincidence rate of identified oil-bearing strata reaches 90.02% compared to artificial correlation results,which is about 17 percentage points higher than that of the existing automatic correlation methods.The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.
基金supported by the National Natural Science Foundation of China(Grant No.62373197)Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX18_0892).
文摘The COVID-19 outbreak has significantly disrupted the lives of individuals worldwide.Following the lifting of COVID-19 interventions,there is a heightened risk of future outbreaks from other circulating respiratory infections,such as influenza-like illness(ILI).Accurate prediction models for ILI cases are crucial in enabling governments to implement necessary measures and persuade individuals to adopt personal precautions against the disease.This paper aims to provide a forecasting model for ILI cases with actual cases.We propose a specific model utilizing the partial differential equation(PDE)that will be developed and validated using real-world data obtained from the Chinese National Influenza Center.Our model combines the effects of transboundary spread among regions in China mainland and human activities’impact on ILI transmission dynamics.The simulated results demonstrate that our model achieves excellent predictive performance.Additionally,relevant factors influencing the dissemination are further examined in our analysis.Furthermore,we investigate the effectiveness of travel restrictions on ILI cases.Results can be used to utilize to mitigate the spread of disease.
基金supported by National Natural Science Foundation of China(NSFC)(No.62101274 and 62101275)Natural Science Foundation of Jiangsu Province(BK20210640)Open Research Fund of National Mobile Communications Research Laboratory Southeast University under Grant 2021D03。
文摘In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 62373197 and 62203229)the Postgraduate Research & Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX24_1211)。
文摘There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics.Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.
文摘To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and transfer learning-based method for printing defect detection was proposed in this study.This method enabled defect detection in printed surface without the need for extensive labeled defect.The ResNet101-SSTU model was used in this study.On the public dataset of printing defect images,the ResNet101-SSTU model not only achieves comparable performance and speed to mainstream supervised learning detection models but also successfully addresses some of the detection challenges encountered in supervised learning.The proposed ResNet101-SSTU model effectively eliminates the need for extensive defect samples and labeled data in training,providing an efficient solution for quality inspection in the printing industry.
文摘With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of The Open University of China online education platform were taken as the research object,their user behavior data was collected,cleaned,and analyzed with text mining.The RFM model and the improved K-Means algorithm were used to construct the user portrait of the platform group and the needs and preferences of different types of the users were analyzded.Chinese word segmentation was used to show the key words of different types of users and the word cloud of their using frequency.The focus of different user groups was determined to facilitate for the follow-up course recommendation and precision marketing.Experimental results showed that the improved K-Means algorithm can well depict the behavior of group users.The index of silhouette score was improved to 0.811 by the improved K-Means algorithm,from random uncertainty to a fixed value,which can effectively solve the problem of inconsistent results caused by outlier sample points.
文摘In the data-driven era of the internet and business environments,constructing accurate user profiles is paramount for personalized user understanding and classification.The traditional TF-IDF algorithm has some limitations when evaluating the impact of words on classification results.Consequently,an improved TF-IDF-K algorithm was introduced in this study,which included an equalization factor,aimed at constructing user profiles by processing and analyzing user search records.Through the training and prediction capabilities of a Support Vector Machine(SVM),it enabled the prediction of user demographic attributes.The experimental results demonstrated that the TF-IDF-K algorithm has achieved a significant improvement in classification accuracy and reliability.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on the YOLOv8 model was proposed in this paper.Firstly,the Global Attention Module(GAM)was introduced to enhance data prediction capability and model expression ability.Secondly,the Space-to-Depth(SPD)module was incorporated into the backbone network for fine-grained feature information learning to mitigate feature information loss due to down-sampling.Finally,a 160 pixels×160 pixels feature layer was added to expand small target feature information and effectively reduce instances of missed targets.Experimental validation on the public VisDrone2019 UAV small target detaset demonstrated that the proposed model achieves significant performance improvement in small target detection tasks compared to existing models,exhibiting higher accuracy.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62476230 and 61976246)the Natural Science Foundation of Chongqing(Grant No.CSTB2023NSCQ-MSX0018)Fundamental Research Funds for the Central Universities(Grant No.SWUKR22046)。
文摘Brain-inspired intelligence is considered to be a computational model with the most promising potential to overcome the shortcomings of the von Neumann architecture,making it a current research hotspot.Due to advantages such as nonvolatility,high density,low power consumption,and high response ratio,memristors are regarded as devices with promising applications in brain-inspired intelligence.This paper proposes a physical Ag/HfO_(x)/FeO_(x)/Pt memristor model.The Ag/HfO_(x)/FeO_(x)/Pt memristor is first fabricated using magnetron sputtering,and its internal principles and characteristics are then thoroughly analyzed.Furthermore,we construct a corresponding physical memristor model which achieves a simulation accuracy of up to 99.72%for the physical memristor.We design a fully functional Pavlovian associative memory circuit,realizing functions including generalization,primary differentiation,secondary differentiation,and forgetting.Finally,the circuit is validated through PSPICE simulation and analysis.
基金This work was supported by the National Natural Science Foundation of China(No.51874320).
文摘Spontaneous imbibition is an important phenomenon in tight reservoirs.The existence of a large number of fractures and micro-nano pores is the key factor affecting the spontaneous imbibition of tight reservoirs.In this study,based on high-pressure mercury injection and nuclear magnetic resonance experiments,the pore distribution of tight sandstone is described.The influence of fractures,core porosity and permeability,and surfactants on the spontaneous imbibition of tight sandstone are studied by physical fracturing,interfacial tension test,wettability test and imbibition experiments.The results show that:the pore radius of tight sandstone is concentrated in 0.01-1 mm.Fractures can effectively reduce the oil drop adsorption on the core surface,enhancing the imbibition recovery of the tight sandstone with an increase of about 10%.As the number of fractures increases,the number of oil droplets adsorbed on the core surface decrease and the imbibition rate increases.The imbibition recovery increases with the increase in pore connectivity,while the imbibition rate increases with the increases in core porosity and permeability.The surfactant can improve the core water wettability and reduce the oilwater interfacial tension,reducing the adsorption of oil droplets on the core surface,and improving the core imbibition recovery with an increase of about 15%.In a word,the existence of fractures and surfactants can enhance the pore connectivity of the reservoir,reduce the adsorption of oil droplets on the core surface,and improve the imbibition rate and recovery rate of the tight oil reservoir.
基金supported by “National Natural Science Foundation of China (Grant No. 42204106)”“China Postdoctoral Science Foundation (Grant No. 2021M700172)”+1 种基金“The Strategic Cooperation Technology Projects of CNPC and CUP (Grant No. ZLZX2020-03)”“Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 20KJD430002)”
文摘In this paper,we proposed a novel method for low-field nuclear magnetic resonance(NMR)inversion based on low-rank and sparsity restraint(LRSR)of relaxation spectra,with which high quality construction is made possible for one-and two-dimensional low-field and low signal to noise ratio NMR data.In this method,the low-rank and sparsity restraints are introduced into the objective function instead of the smoothing term.The low-rank features in relaxation spectra are extracted to ensure the local characteristics and morphology of spectra.The sparsity and residual term are contributed to the resolution and precision of spectra,with the elimination of the redundant relaxation components.Optimization process of the objective function is designed with alternating direction method of multiples,in which the objective function is decomposed into three subproblems to be independently solved.The optimum solution can be obtained by alternating iteration and updating process.At first,numerical simulations are conducted on synthetic echo data with different signal-to-noise ratios,to optimize the desirable regularization parameters and verify the feasibility and effectiveness of proposed method.Then,NMR experiments on solutions and artificial sandstone samples are conducted and analyzed,which validates the robustness and reliability of the proposed method.The results from simulations and experiments have demonstrated that the suggested method has unique advantages for improving the resolution of relaxation spectra and enhancing the ability of fluid quantitative identification.
基金supported by National Natural Science Foundation of China(Grant No.52004297 and Grant No.51991361)China Postdoctoral Science Foundation(Grant No.BX20200384)。
文摘Kinds of complex-structure wells can effectively improve production,which are widely used.However,in the process of drilling and completion,complex-structure wells with long drilling cycle and large exposed area of reservoir can lead to the fact that reservoir near wellbore is more vulnerable to the working fluid invasion,resulting in more serious formation damage.In order to quantitatively describe the reservoir formation damage in the construction of complex-structure well,taking the inclined well section as the research object,the coordinate transformation method and conformal transformation method are given according to the flow characteristics of reservoir near wellbore in anisotropic reservoir.Then the local skin factor in orthogonal plane of wellbore is deduced.Considering the un-even distribution of local skin factor along the wellbore,the oscillation decreasing model and empirical equation model of damage zone radius distribution along the wellbore direction are established and then the total skin factor model of the whole well is superimposed to realize the reservoir damage evaluation of complex-structure wells.Combining the skin factor model with the production model,the production of complex-structure wells can be predicted more accurately.The two field application cases show that the accuracy of the model can be more than 90%,which can also fully reflect the invasion characteristics of drilling and completion fluid in any well section of complex-structure wells in anisotropic reservoir,so as to further provide guidance for the scientific establish-ment of reservoir production system.
基金“The Strategic Cooperation Technology Projects of CNPC and CUP(Grant Number ZLZX2020-03)”“China Postdoctoral Science Foundation(Grant Number 2021M700172)”.
文摘Borehole nuclear magnetic resonance(NMR)is a powerful technology to characterize the petrophysical properties of underground reservoirs in the petroleum industry.The rising complexity of oil and gas exploration and development objectives,as well as the novel application contexts of underground reservoirs,have led to increasingly demanding requirements on borehole NMR technology including instrument design and related processing methods.This mini review summarizes the advances and applications of borehole NMR instruments along with some future possibilities.It may be helpful for researchers and engineers in the petroleum industry to understand the development status and future trends of borehole NMR technology.
文摘Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61672298,61873326,and 61802155)the Philosophy Social Science Research Key Project Fund of Jiangsu University(Grant No.2018SJZDI142)。
文摘Since December 2019,the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade,national policies and the natural environment.To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy,we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations(PDEs),which captures epidemic diffusion along the edges of a network driven by population flow data.In this paper,we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19.Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models.Furthermore,we study the effectiveness of intervention measures,such as traffic lockdowns and social distancing,which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model.To our knowledge,this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.
基金the National Natural Science Foundation of China(Grant Nos.61973175 and 61873186)the South African National Research Foundation(Grant No.132797)+1 种基金the South African National Research Foundation Incentive(Grant No.114911)the South African Eskom Tertiary Education Support Programme.
文摘The thermostatted system is a conservative system different from Hamiltonian systems,and has attracted much attention because of its rich and different nonlinear dynamics.We report and analyze the multiple equilibria and curve axes of the cluster-shaped conservative flows generated from a generalized thermostatted system.It is found that the cluster-shaped structure is reflected in the geometry of the Hamiltonian,such as isosurfaces and local centers,and the shapes of cluster-shaped chaotic flows and invariant tori rely on the isosurfaces determined by initial conditions,while the numbers of clusters are subject to the local centers solved by the Hessian matrix of the Hamiltonian.Moreover,the study shows that the cluster-shaped chaotic flows and invariant tori are chained together by curve axes,which are the segments of equilibrium curves of the generalized thermostatted system.Furthermore,the interesting results are vividly demonstrated by the numerical simulations.
