The ongoing data explosion introduced unprecedented challenges to the information security of communication networks.As images are one of the most commonly used information transmission carriers;therefore,their data r...The ongoing data explosion introduced unprecedented challenges to the information security of communication networks.As images are one of the most commonly used information transmission carriers;therefore,their data redundancy analysis and screening are of great significance.However,most of the current research focus on the algorithm improvement of commonly used image datasets.Thus,we should consider an important question:Is there data redundancy in the open datasets?Considering the factors of model structures and data distribution to ensure the generalization,we conducted extensive experiments to compare the average accuracy based on few random data to the baseline accuracy based on all data.The results show serious data redundancy in the open datasets from different domains.For instance,with the aid of deep model,only 20%data can achieve more than 90%of the baseline accuracy.Further,we proposed a novel entropy-based information screening method,which outperforms the random sampling under many experimental conditions.In particular,considering 20%of data,for the shallow model,the improvement is approximately 10%,and for the deep model,the ratio to the baseline accuracy increases to greater than 95%.Moreover,this work can also serve as a new way of learning from a few valuable samples,compressing the size of existing datasets and guiding the construction of high-quality datasets in the future.展开更多
Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,...Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.展开更多
Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inc...Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.展开更多
The recently developed magic-intensity trapping technique of neutral atoms efficiently mitigates the detrimental effect of light shifts on atomic qubits and substantially enhances the coherence time. This technique re...The recently developed magic-intensity trapping technique of neutral atoms efficiently mitigates the detrimental effect of light shifts on atomic qubits and substantially enhances the coherence time. This technique relies on applying a bias magnetic field precisely parallel to the wave vector of a circularly polarized trapping laser field. However, due to the presence of the vector light shift experienced by the trapped atoms, it is challenging to precisely define a parallel magnetic field, especially at a low bias magnetic field strength, for the magic-intensity trapping of85Rb qubits. In this work, we present a method to calibrate the angle between the bias magnetic field and the trapping laser field with the compensating magnetic fields in the other two directions orthogonal to the bias magnetic field direction. Experimentally, with a constantdepth trap and a fixed bias magnetic field, we measure the respective resonant frequencies of the atomic qubits in a linearly polarized trap and a circularly polarized one via the conventional microwave Rabi spectra with different compensating magnetic fields and obtain the corresponding total magnetic fields via the respective resonant frequencies using the Breit–Rabi formula. With known total magnetic fields, the angle is a function of the other two compensating magnetic fields.Finally, the projection value of the angle on either of the directions orthogonal to the bias magnetic field direction can be reduced to 0(4)° by applying specific compensating magnetic fields. The measurement error is mainly attributed to the fluctuation of atomic temperature. Moreover, it also demonstrates that, even for a small angle, the effect is strong enough to cause large decoherence of Rabi oscillation in a magic-intensity trap. Although the compensation method demonstrated here is explored for the magic-intensity trapping technique, it can be applied to a variety of similar precision measurements with trapped neutral atoms.展开更多
Aqueous Zn metal batteries(AZMBs)with intrinsic safety,high energy density and low cost have been regarded as promising electrochemical energy storage devices.However,the parasitic reaction on metallic Zn anode and th...Aqueous Zn metal batteries(AZMBs)with intrinsic safety,high energy density and low cost have been regarded as promising electrochemical energy storage devices.However,the parasitic reaction on metallic Zn anode and the incompatibility between electrode and electrolytes lead to the deterioration of electrochemical performance of AZMBs during the cycling.The critical point to achieve the stable cycling of AZMBs is to properly regulate the zinc ion solvated structure and transfer behavior between metallic Zn anode and electrolyte.In recent years,numerous achievements have been made to resolve the formation of Zn dendrite and interface incompatible issues faced by AZMBs via optimizing the sheath structure and transport capability of zinc ions at electrode-electrolyte interface.In this review,the challenges for metallic Zn anode and electrode-electrolyte interface in AZMBs including dendrite formation and interface characteristics are presented.Following the influences of different strategies involving designing advanced electrode structu re,artificial solid electrolyte interphase(SEI)on Zn anode and electrolyte engineering to regulate zinc ion solvated sheath structure and transport behavior are summarized and discussed.Finally,the perspectives for the future development of design strategies for dendrite-free Zn metal anode and long lifespan AZMBs are also given.展开更多
In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be amel...In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ameliorated.Specially,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be addressed.To deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI control.The upper limb-robotic exoskeleton system is re-modelled as an artificial system.Depending on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical system.Furthermore,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and compared.Theoretical results substantiate the global convergence and robustness of the proposed controller in the presence of different external disturbances.In addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.展开更多
During the operation of a DC microgrid,the nonlinearity and low damping characteristics of the DC bus make it prone to oscillatory instability.In this paper,we first establish a discrete nonlinear system dynamic model...During the operation of a DC microgrid,the nonlinearity and low damping characteristics of the DC bus make it prone to oscillatory instability.In this paper,we first establish a discrete nonlinear system dynamic model of a DC microgrid,study the effects of the converter sag coefficient,input voltage,and load resistance on the microgrid stability,and reveal the oscillation mechanism of a DC microgrid caused by a single source.Then,a DC microgrid stability analysis method based on the combination of bifurcation and strobe is used to analyze how the aforementioned parameters influence the oscillation characteristics of the system.Finally,the stability region of the system is obtained by the Jacobi matrix eigenvalue method.Grid simulation verifies the feasibility and effectiveness of the proposed method.展开更多
Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To ...Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns.The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment,agricultural drainage irrigation,port shore power,and electric vehicles.Finally,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and compared.Experiments showed that the optimal number of clusters is 4.This study demonstrates the potential of using a fuzzy C-means clustering algorithmin identifying emerging types of electricity consumption behavior,which can help power system operators and policymakers to make informed decisions and improve energy efficiency.展开更多
THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to pos...THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real environments.In this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].展开更多
AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the f...AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.展开更多
Information-Centric Networking(ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution ...Information-Centric Networking(ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution as a service of the network with lower delay and higher security in comparison with the current IP network. Applying ICN in current IP infrastructure leads to major complexities. One approach to deploy ICN with less complexity is to integrate ICN with Software Defined Networking(SDN). The SDN controller manages the content distribution, caching, and routing based on the users' requests. In this paper, we extend these context by addressing the ICN topology management problem over the SDN network to achieve an improved user experience as well as network performance. In particular, a centralized controller is designed to construct and manage the ICN overlay. Experimental results indicate that this adopted topology management strategy achieves high performance, in terms of low failure in interest satisfaction and reduced download time compared to a plain ICN.展开更多
Visible Light Communication( VLC) based on LED is a new wireless communication technology with high response rate and good modulation characteristics in the wavelengths of 380- 780 nm. Compared with conventional metho...Visible Light Communication( VLC) based on LED is a new wireless communication technology with high response rate and good modulation characteristics in the wavelengths of 380- 780 nm. Compared with conventional methods,the waveband of VLC is harmless to human and safe to communication because of no magnetism radiation. An audio information transmission system using LED traffic lights is presented based on VLC technology. The system is consisted of transmitting terminal,receiving terminal and communication channel. Some experiments were made under real communication environment. The experimental results showed that the traffic information transmission system works steadily with good communication quality and achieves the purpose of transmitting audio information through LED traffic lights,with a data transfer rate up to 250 kbps over a distance of 5 meters.展开更多
Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.Th...Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.展开更多
Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of...Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of AP typically involve the use of imaging technologies,such as computed tomography,magnetic resonance imaging,and ultrasound,and scoring systems,including Ranson,Acute Physiology and Chronic Health Evaluation II,and Bedside Index for Severity in AP scores.Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity,while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications.Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild,moderate,or severe categories,guiding treatment decisions,such as intensive care unit admission,early enteral feeding,and antibiotic use.Despite the central role of imaging technologies and scoring systems in AP management,these methods have limitations in terms of accuracy,reproducibility,practicality and economics.Recent advancements of artificial intelligence(AI)provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data.AI algorithms can analyze large amounts of clinical and imaging data,identify scoring system patterns,and predict the clinical course of disease.AI-based models have shown promising results in predicting the severity and mortality of AP,but further validation and standardization are required before widespread clinical application.In addition,understanding the correlation between these three technologies will aid in developing new methods that can accurately,sensitively,and specifically be used in the diagnosis,severity prediction,and prognosis assessment of AP through complementary advantages.展开更多
In this paper,a new compact ultrawideband(UWB)circularly polarized(CP)antenna array for vehicular communications is proposed.The antenna array consists of a 2×2 sequentially rotated T-shaped cross dipole,four par...In this paper,a new compact ultrawideband(UWB)circularly polarized(CP)antenna array for vehicular communications is proposed.The antenna array consists of a 2×2 sequentially rotated T-shaped cross dipole,four parasitic elements,and a feeding network.By loading the T-shaped cross dipoles with parasitic rectangular elements with cut corners,the bandwidth can be expanded.On this basis,the radiation pattern can be improved by the topology with sequential rotation of four T-shaped cross-dipole antennas,and the axial ratio(AR)bandwidth of the antenna also can be further enhanced.In addition,due to the special topology that the vertical arms of all Tshaped cross dipoles are all oriented toward the center of the antenna array,the gain of proposed antenna is improved while the size of the antenna is almost the same as the traditional cross dipole.Simulated and measured results show that the proposed antenna has good CP characteristics,an impedance bandwidth for S11<-10 d B of about 106.1%(3.26:1,1.57-5.12 GHz)and the 3-d B AR bandwidth of about 104.1%(3.17:1,1.57-4.98 GHz),a wide 3-d B gain bandwidth of 73.3%as well as the peak gain of 8.6 d Bic at 3.5 GHz.The overall size of antenna is 0.56λ×0.56λ×0.12λ(λrefers to the wavelength of the lowest operating frequency in free space).The good performance of this compact UWB CP antenna array is promising for applications in vehicular communications.展开更多
Horizontal gas-liquid two-phase flows widely exist in chemical engineering,oil/gas production and other important industrial processes.Slug flow pattern is the main form of horizontal gas-liquid flows and characterize...Horizontal gas-liquid two-phase flows widely exist in chemical engineering,oil/gas production and other important industrial processes.Slug flow pattern is the main form of horizontal gas-liquid flows and characterized by intermittent motion of film region and slug region.This work aims to develop the ultrasonic Doppler method to realize the simultaneous measurement of the velocity profile and liquid film thickness of slug flow.A single-frequency single-channel transducer is adopted in the design of the field-programmable gate array based ultrasonic Doppler system.A multiple echo repetition technology is used to improve the temporal-spatial resolution for the velocity profile.An experiment of horizontal gas-liquid two-phase flow is implemented in an acrylic pipe with an inner diameter of 20 mm.Considering the aerated characteristics of the liquid slug,slug flow is divided into low-aerated slug flow,high-aerated slug flow and pseudo slug flow.The temporal-spatial velocity distributions of the three kinds of slug flows are reconstructed by using the ultrasonic velocity profile measurement.The evolution characteristics of the average velocity profile in slug flows are investigated.A novel method is proposed to derive the liquid film thickness based on the instantaneous velocity profile.The liquid film thickness can be effectively measured by detecting the position and the size of the bubbles nearly below the elongated gas bubble.Compared with the time of flight method,the film thickness measured by the Doppler system shows a higher accuracy as a bubble layer occurs in the film region.The effect of the gas distribution on the film thickness is uncovered in three kinds of slug flows.展开更多
The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disas...The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disaster.The research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological progress.It is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine production.Therefore,timely and accurate identification and control of mine water source is very important to ensure mine production safety.Laser-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical methods.In this experiment,sandstone water and old air water were collected from the Huainan mining area as original samples.Five types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for testing.Four preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and interference.CT was determined as the optimal preprocessing method based on class discrimination,data distribution,and data range.To maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality reduction.Through comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction method.Finally,two neural network models,CT+PCA+CNN and CT+PCA+ResNet,were constructed by combining Convolutional Neural Networks(CNN)and Residual Neural Networks(ResNet),respectively.When selecting the neural network models,the training time,number of iterative parameters,accuracy,and cross-entropy loss function in the classification problem were compared to determine the model best suited for water source data.The results indicated that CT+PCA+ResNet was the optimal approach for water source identification in this study.展开更多
Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-obj...Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.展开更多
Wire electrical explosion may result in the existence of micro-sized large particles in powders while current injection ways may influence the size and content of micro-sized large particles. Therefore, two kinds of e...Wire electrical explosion may result in the existence of micro-sized large particles in powders while current injection ways may influence the size and content of micro-sized large particles. Therefore, two kinds of electrical explosion devices with different electrodes by gas discharge were designed in this paper. The pole-board electrodes and the cone electrodes were used respectively for studying copper wire electrical explosion process. The current and voltage data were measured with the Rogowski coil and high voltage probe. The results show that the pulverizing process of electrical explosion is more efficient when the wire electrode current density injected into the cone electrodes is approximately twice as much as the pole-board electrodes. The content of micro-sized large particles is the least among the products of the electrical explosion, when the total deposition energy of the wire prior to vaporization stage is 2. 5 times larger than that of the theoretical value of the completed vaporization.展开更多
Exploring the human brain is perhaps the most challenging and fascinating scientific issue in the 21st century.It will facilitate the development of various aspects of the society,including economics,education,health ...Exploring the human brain is perhaps the most challenging and fascinating scientific issue in the 21st century.It will facilitate the development of various aspects of the society,including economics,education,health care,national defense and daily life.The artificial intelligence techniques are becoming useful as an alternate method of classical techniques or as a component of an integrated system.They are used to solve complicated problems in various fields and becoming increasingly popular nowadays.Especially,the investigation of human brain will promote the artificial intelligence techniques,utilizing the accumulating knowledge of neuroscience,brain-machine interface techniques,algorithms of spiking neural networks and neuromorphic supercomputers.Consequently,we provide a comprehensive survey of the research and motivations for brain-inspired artificial intelligence and its engineering over its history.The goals of this work are to provide a brief review of the research associated with brain-inspired artificial intelligence and its related engineering techniques,and to motivate further work by elucidating challenges in the field where new researches are required.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.32101612,No.61871283).
文摘The ongoing data explosion introduced unprecedented challenges to the information security of communication networks.As images are one of the most commonly used information transmission carriers;therefore,their data redundancy analysis and screening are of great significance.However,most of the current research focus on the algorithm improvement of commonly used image datasets.Thus,we should consider an important question:Is there data redundancy in the open datasets?Considering the factors of model structures and data distribution to ensure the generalization,we conducted extensive experiments to compare the average accuracy based on few random data to the baseline accuracy based on all data.The results show serious data redundancy in the open datasets from different domains.For instance,with the aid of deep model,only 20%data can achieve more than 90%of the baseline accuracy.Further,we proposed a novel entropy-based information screening method,which outperforms the random sampling under many experimental conditions.In particular,considering 20%of data,for the shallow model,the improvement is approximately 10%,and for the deep model,the ratio to the baseline accuracy increases to greater than 95%.Moreover,this work can also serve as a new way of learning from a few valuable samples,compressing the size of existing datasets and guiding the construction of high-quality datasets in the future.
文摘Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.
基金supported by the National Natural Science Foundation of China (No.31872399)Advantage Discipline Construction Project (PAPD,No.6-2018)of Jiangsu University。
文摘Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12104414,12122412,12104464,and 12104413)the China Postdoctoral Science Foundation(Grant No.2021M702955).
文摘The recently developed magic-intensity trapping technique of neutral atoms efficiently mitigates the detrimental effect of light shifts on atomic qubits and substantially enhances the coherence time. This technique relies on applying a bias magnetic field precisely parallel to the wave vector of a circularly polarized trapping laser field. However, due to the presence of the vector light shift experienced by the trapped atoms, it is challenging to precisely define a parallel magnetic field, especially at a low bias magnetic field strength, for the magic-intensity trapping of85Rb qubits. In this work, we present a method to calibrate the angle between the bias magnetic field and the trapping laser field with the compensating magnetic fields in the other two directions orthogonal to the bias magnetic field direction. Experimentally, with a constantdepth trap and a fixed bias magnetic field, we measure the respective resonant frequencies of the atomic qubits in a linearly polarized trap and a circularly polarized one via the conventional microwave Rabi spectra with different compensating magnetic fields and obtain the corresponding total magnetic fields via the respective resonant frequencies using the Breit–Rabi formula. With known total magnetic fields, the angle is a function of the other two compensating magnetic fields.Finally, the projection value of the angle on either of the directions orthogonal to the bias magnetic field direction can be reduced to 0(4)° by applying specific compensating magnetic fields. The measurement error is mainly attributed to the fluctuation of atomic temperature. Moreover, it also demonstrates that, even for a small angle, the effect is strong enough to cause large decoherence of Rabi oscillation in a magic-intensity trap. Although the compensation method demonstrated here is explored for the magic-intensity trapping technique, it can be applied to a variety of similar precision measurements with trapped neutral atoms.
基金supported by the National Key Research and Development Programs(2021YFB2400400)Major Science and Technology Innovation Project of Hunan Province(2020GK10102020GK1014-4)+7 种基金National Natural Science Foundation of China(32201162)the 70th general grant of China Postdoctoral Science Foundation(2021M702947)Natural Science Foundation of Henan(232300420404)Key Scientific and Technological Project of Henan Province(232102320290,232102311156)Key Research Project Plan for Higher Education Institutions in Henan Province(24A150009,23B430011)Doctor Foundation of Henan University of Engineering(D2022002)the Science and Technology Innovation Program of Hunan Province(2023RC3154)the scientific research projects of Education Department of Hunan Province(23A0188)。
文摘Aqueous Zn metal batteries(AZMBs)with intrinsic safety,high energy density and low cost have been regarded as promising electrochemical energy storage devices.However,the parasitic reaction on metallic Zn anode and the incompatibility between electrode and electrolytes lead to the deterioration of electrochemical performance of AZMBs during the cycling.The critical point to achieve the stable cycling of AZMBs is to properly regulate the zinc ion solvated structure and transfer behavior between metallic Zn anode and electrolyte.In recent years,numerous achievements have been made to resolve the formation of Zn dendrite and interface incompatible issues faced by AZMBs via optimizing the sheath structure and transport capability of zinc ions at electrode-electrolyte interface.In this review,the challenges for metallic Zn anode and electrode-electrolyte interface in AZMBs including dendrite formation and interface characteristics are presented.Following the influences of different strategies involving designing advanced electrode structu re,artificial solid electrolyte interphase(SEI)on Zn anode and electrolyte engineering to regulate zinc ion solvated sheath structure and transport behavior are summarized and discussed.Finally,the perspectives for the future development of design strategies for dendrite-free Zn metal anode and long lifespan AZMBs are also given.
基金Key Science and Technology Projects of Jilin Province,China,Grant/Award Number:20230204081YYChangchun Science and Technology Project,Grant/Award Number:21ZY41National Natural Science Foundation of China,Grant/Award Numbers:61873304,62173048,62106023。
文摘In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ameliorated.Specially,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be addressed.To deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI control.The upper limb-robotic exoskeleton system is re-modelled as an artificial system.Depending on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical system.Furthermore,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and compared.Theoretical results substantiate the global convergence and robustness of the proposed controller in the presence of different external disturbances.In addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.
基金National Natural Science Foundation of China(Nos.51767017,51867015,62063016)Fundamental Research Innovation Group Project of Gansu Province(18JR3RA133)Gansu Provincial Science and Technology Program(20JR5RA048,20JR10RA177).
文摘During the operation of a DC microgrid,the nonlinearity and low damping characteristics of the DC bus make it prone to oscillatory instability.In this paper,we first establish a discrete nonlinear system dynamic model of a DC microgrid,study the effects of the converter sag coefficient,input voltage,and load resistance on the microgrid stability,and reveal the oscillation mechanism of a DC microgrid caused by a single source.Then,a DC microgrid stability analysis method based on the combination of bifurcation and strobe is used to analyze how the aforementioned parameters influence the oscillation characteristics of the system.Finally,the stability region of the system is obtained by the Jacobi matrix eigenvalue method.Grid simulation verifies the feasibility and effectiveness of the proposed method.
基金supported by the Science and Technology Project of State Grid Jiangxi Electric Power Corporation Limited‘Research on Key Technologies for Non-Intrusive Load Identification for Typical Power Industry Users in Jiangxi Province’(521852220004)。
文摘Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns.The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment,agricultural drainage irrigation,port shore power,and electric vehicles.Finally,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and compared.Experiments showed that the optimal number of clusters is 4.This study demonstrates the potential of using a fuzzy C-means clustering algorithmin identifying emerging types of electricity consumption behavior,which can help power system operators and policymakers to make informed decisions and improve energy efficiency.
基金supported by the National Natural Science Foundation of China(62302047,62203250)the Science and Technology Development Fund of Macao SAR(0093/2023/RIA2,0050/2020/A1).
文摘THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real environments.In this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].
基金supported in part by the Hong Kong Polytechnic University via the project P0038447The Science and Technology Development Fund,Macao SAR(0093/2023/RIA2)The Science and Technology Development Fund,Macao SAR(0145/2023/RIA3).
文摘AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.
文摘Information-Centric Networking(ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution as a service of the network with lower delay and higher security in comparison with the current IP network. Applying ICN in current IP infrastructure leads to major complexities. One approach to deploy ICN with less complexity is to integrate ICN with Software Defined Networking(SDN). The SDN controller manages the content distribution, caching, and routing based on the users' requests. In this paper, we extend these context by addressing the ICN topology management problem over the SDN network to achieve an improved user experience as well as network performance. In particular, a centralized controller is designed to construct and manage the ICN overlay. Experimental results indicate that this adopted topology management strategy achieves high performance, in terms of low failure in interest satisfaction and reduced download time compared to a plain ICN.
基金Sponsored by the National Science and Technology Innovation Fund for Small and Medium Enterprises(Grant No.10C26211200144)Tianjin Science and Technology Key Supporting Projects(Grant No.10ZCGYGX18300)
文摘Visible Light Communication( VLC) based on LED is a new wireless communication technology with high response rate and good modulation characteristics in the wavelengths of 380- 780 nm. Compared with conventional methods,the waveband of VLC is harmless to human and safe to communication because of no magnetism radiation. An audio information transmission system using LED traffic lights is presented based on VLC technology. The system is consisted of transmitting terminal,receiving terminal and communication channel. Some experiments were made under real communication environment. The experimental results showed that the traffic information transmission system works steadily with good communication quality and achieves the purpose of transmitting audio information through LED traffic lights,with a data transfer rate up to 250 kbps over a distance of 5 meters.
文摘Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.
基金Fujian Provincial Health Technology Project,No.2020GGA079Natural Science Foundation of Fujian Province,No.2021J011380National Natural Science Foundation of China,No.62276146.
文摘Acute pancreatitis(AP)is a potentially life-threatening inflammatory disease of the pancreas,with clinical management determined by the severity of the disease.Diagnosis,severity prediction,and prognosis assessment of AP typically involve the use of imaging technologies,such as computed tomography,magnetic resonance imaging,and ultrasound,and scoring systems,including Ranson,Acute Physiology and Chronic Health Evaluation II,and Bedside Index for Severity in AP scores.Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity,while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications.Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild,moderate,or severe categories,guiding treatment decisions,such as intensive care unit admission,early enteral feeding,and antibiotic use.Despite the central role of imaging technologies and scoring systems in AP management,these methods have limitations in terms of accuracy,reproducibility,practicality and economics.Recent advancements of artificial intelligence(AI)provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data.AI algorithms can analyze large amounts of clinical and imaging data,identify scoring system patterns,and predict the clinical course of disease.AI-based models have shown promising results in predicting the severity and mortality of AP,but further validation and standardization are required before widespread clinical application.In addition,understanding the correlation between these three technologies will aid in developing new methods that can accurately,sensitively,and specifically be used in the diagnosis,severity prediction,and prognosis assessment of AP through complementary advantages.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant No.62071306in part by Shenzhen Science and Technology Program under Grants JCYJ202001091-13601723,JSGG20210802154203011 and JSGG-20210420091805014。
文摘In this paper,a new compact ultrawideband(UWB)circularly polarized(CP)antenna array for vehicular communications is proposed.The antenna array consists of a 2×2 sequentially rotated T-shaped cross dipole,four parasitic elements,and a feeding network.By loading the T-shaped cross dipoles with parasitic rectangular elements with cut corners,the bandwidth can be expanded.On this basis,the radiation pattern can be improved by the topology with sequential rotation of four T-shaped cross-dipole antennas,and the axial ratio(AR)bandwidth of the antenna also can be further enhanced.In addition,due to the special topology that the vertical arms of all Tshaped cross dipoles are all oriented toward the center of the antenna array,the gain of proposed antenna is improved while the size of the antenna is almost the same as the traditional cross dipole.Simulated and measured results show that the proposed antenna has good CP characteristics,an impedance bandwidth for S11<-10 d B of about 106.1%(3.26:1,1.57-5.12 GHz)and the 3-d B AR bandwidth of about 104.1%(3.17:1,1.57-4.98 GHz),a wide 3-d B gain bandwidth of 73.3%as well as the peak gain of 8.6 d Bic at 3.5 GHz.The overall size of antenna is 0.56λ×0.56λ×0.12λ(λrefers to the wavelength of the lowest operating frequency in free space).The good performance of this compact UWB CP antenna array is promising for applications in vehicular communications.
基金supported by the National Natural Science Foundation of China(41974139,42274148,42074142)。
文摘Horizontal gas-liquid two-phase flows widely exist in chemical engineering,oil/gas production and other important industrial processes.Slug flow pattern is the main form of horizontal gas-liquid flows and characterized by intermittent motion of film region and slug region.This work aims to develop the ultrasonic Doppler method to realize the simultaneous measurement of the velocity profile and liquid film thickness of slug flow.A single-frequency single-channel transducer is adopted in the design of the field-programmable gate array based ultrasonic Doppler system.A multiple echo repetition technology is used to improve the temporal-spatial resolution for the velocity profile.An experiment of horizontal gas-liquid two-phase flow is implemented in an acrylic pipe with an inner diameter of 20 mm.Considering the aerated characteristics of the liquid slug,slug flow is divided into low-aerated slug flow,high-aerated slug flow and pseudo slug flow.The temporal-spatial velocity distributions of the three kinds of slug flows are reconstructed by using the ultrasonic velocity profile measurement.The evolution characteristics of the average velocity profile in slug flows are investigated.A novel method is proposed to derive the liquid film thickness based on the instantaneous velocity profile.The liquid film thickness can be effectively measured by detecting the position and the size of the bubbles nearly below the elongated gas bubble.Compared with the time of flight method,the film thickness measured by the Doppler system shows a higher accuracy as a bubble layer occurs in the film region.The effect of the gas distribution on the film thickness is uncovered in three kinds of slug flows.
基金the Collaborative Innovation Center of Mine Intelligent Equipment and Technology,Anhui University of Science&Technology(CICJMITE202203)National Key R&D Program of China(2018YFC0604503)Anhui Province Postdoctoral Research Fund Funding Project(2019B350).
文摘The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disaster.The research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological progress.It is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine production.Therefore,timely and accurate identification and control of mine water source is very important to ensure mine production safety.Laser-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical methods.In this experiment,sandstone water and old air water were collected from the Huainan mining area as original samples.Five types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for testing.Four preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and interference.CT was determined as the optimal preprocessing method based on class discrimination,data distribution,and data range.To maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality reduction.Through comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction method.Finally,two neural network models,CT+PCA+CNN and CT+PCA+ResNet,were constructed by combining Convolutional Neural Networks(CNN)and Residual Neural Networks(ResNet),respectively.When selecting the neural network models,the training time,number of iterative parameters,accuracy,and cross-entropy loss function in the classification problem were compared to determine the model best suited for water source data.The results indicated that CT+PCA+ResNet was the optimal approach for water source identification in this study.
基金supported in part by the National Natural Science Fund for Outstanding Young Scholars of China (61922072)the National Natural Science Foundation of China (62176238, 61806179, 61876169, 61976237)+2 种基金China Postdoctoral Science Foundation (2020M682347)the Training Program of Young Backbone Teachers in Colleges and Universities in Henan Province (2020GGJS006)Henan Provincial Young Talents Lifting Project (2021HYTP007)。
文摘Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.
基金This research was supported by National Natural Science Foundation of China (No. 51061011 ).
文摘Wire electrical explosion may result in the existence of micro-sized large particles in powders while current injection ways may influence the size and content of micro-sized large particles. Therefore, two kinds of electrical explosion devices with different electrodes by gas discharge were designed in this paper. The pole-board electrodes and the cone electrodes were used respectively for studying copper wire electrical explosion process. The current and voltage data were measured with the Rogowski coil and high voltage probe. The results show that the pulverizing process of electrical explosion is more efficient when the wire electrode current density injected into the cone electrodes is approximately twice as much as the pole-board electrodes. The content of micro-sized large particles is the least among the products of the electrical explosion, when the total deposition energy of the wire prior to vaporization stage is 2. 5 times larger than that of the theoretical value of the completed vaporization.
文摘Exploring the human brain is perhaps the most challenging and fascinating scientific issue in the 21st century.It will facilitate the development of various aspects of the society,including economics,education,health care,national defense and daily life.The artificial intelligence techniques are becoming useful as an alternate method of classical techniques or as a component of an integrated system.They are used to solve complicated problems in various fields and becoming increasingly popular nowadays.Especially,the investigation of human brain will promote the artificial intelligence techniques,utilizing the accumulating knowledge of neuroscience,brain-machine interface techniques,algorithms of spiking neural networks and neuromorphic supercomputers.Consequently,we provide a comprehensive survey of the research and motivations for brain-inspired artificial intelligence and its engineering over its history.The goals of this work are to provide a brief review of the research associated with brain-inspired artificial intelligence and its related engineering techniques,and to motivate further work by elucidating challenges in the field where new researches are required.