The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the...The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging.Here,a pedestrian re-identification method based on the fusion of local features and gait energy image(GEI)features is proposed.In this method,the human body is divided into four regions according to joint points.The color and texture of each region of the human body are extracted as local features,and GEI features of the pedestrian gait are also obtained.These features are then fused with the local and GEI features of the person.Independent distance measure learning using the cross-view quadratic discriminant analysis(XQDA)method is used to obtain the similarity of the metric function of the image pairs,and the final similarity is acquired by weight matching.Evaluation of experimental results by cumulative matching characteristic(CMC)curves reveals that,after fusion of local and GEI features,the pedestrian re-identification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.展开更多
In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy ...In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life.展开更多
Aiming at the problems of low target image resolution,insufficient target feature extraction,low detection accuracy and poor real time in remote engineering vehicle detection,an improved CenterNet target detection mod...Aiming at the problems of low target image resolution,insufficient target feature extraction,low detection accuracy and poor real time in remote engineering vehicle detection,an improved CenterNet target detection model is proposed in this paper.Firstly,EfficientNet-B0 with Efficient Channel Attention(ECA)module is used as the basic network,which increases the quality and speed of feature extraction and reduces the number of model parameters.Then,the proposed Adaptive Fusion Bidirectional Feature Pyramid Network(AF-BiFPN)module is applied to fuse the features of different feature layers.Furthermore,the feature information of engineering vehicle targets is added by making full use of the high-level semantic and low-level fine-grained feature information of the target,which overcomes the problem that the original CenterNet network did not perform well in small target detection and improve the detection accuracy of the network.Finally,the tag coding strategy and bounding box regression method of CenterNet are optimized by introducing positioning quality loss.The accuracy of target prediction is increased by joint prediction of center position and target size.Experimental results show that the mean Average Precision(mAP)of the improved CenterNet model is 94.74%on the engineering vehicle dataset,and the detection rate is 29 FPS.Compared with the original CenterNet model based on ResNet-18,the detection accuracy of this model is improved by 16.29%,the detection speed is increased by 9 FPS,and the memory usage is reduced by 43 MB.Compared with YOLOv3 and YOLOv4,the mAP of this model is improved by 19.9%and 5.61%respectively.The proposed method can detect engineering vehicles more quickly and accurately in far distance.It has obvious advantages in target detection compared with traditional methods.展开更多
Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentic...Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.展开更多
Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Ga...Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Gaze-tracking systems are an important research topic in the human-computer interaction field.As one of the core modules of the head-mounted gaze-tracking system,pupil positioning affects the accuracy and stability of the system.By tracking eye movements to better locate the center of the pupil,this paper proposes a method for pupil positioning based on the starburst model.The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye.In this paper,we propose a method for detecting the feature points of the pupil edge based on the starburst model,which clusters feature points and uses the RANdom SAmple Consensus(RANSAC)algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center.Our experimental results show that the algorithm has higher precision,higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.展开更多
Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes.The current strategies for observation of ornamental plant bo...Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes.The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience,which have problems such as inaccurate recognition time,time-consuming and energy sapping.Therefore,this paper proposes a neural network-based method for predicting the flowering phase of pear tree.Firstly,based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019,three principal components(the temperature factor,weather factor,and humidity factor)with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method.Then,the three components are used as input factors for the BP neural network.A BP neural network prediction model is constructed based on genetic algorithm optimization.The crossover operator and mutation operator in the adaptive genetic algorithm are improved.Finally,the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper.The results demonstrate that,the model can solve the local optimization problem of the BP neural network model.The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy.Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method.This method can provide more reliable forecast information for the blooming period,which can provide decision-making reference for improving the development of tourism industry.展开更多
Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do ...Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.展开更多
To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,an...To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.展开更多
Since late 1970s, worldwide EFL classrooms have witnessed the change from teacher-centered learning to learner-centered and more effective language learning. Research findings have shown repeatedly the importance of d...Since late 1970s, worldwide EFL classrooms have witnessed the change from teacher-centered learning to learner-centered and more effective language learning. Research findings have shown repeatedly the importance of developing learner autonomy. Based on the review of the relevant literature both in China and around the world, the present paper discusses the main factors affecting the promotion of EFL learner autonomy in China, including motivation, learners＇ metacognitive knowledge and learning environment. Some strategies for fostering learner autonomy are also recommended.展开更多
Growing evidence suggests that extensional/transtensional settings are favorable for the formation of tin deposits,yet the underlying geodynamic mechanism remains equivocal.The Pingna W-Sn deposit,found in the underex...Growing evidence suggests that extensional/transtensional settings are favorable for the formation of tin deposits,yet the underlying geodynamic mechanism remains equivocal.The Pingna W-Sn deposit,found in the underexplored interior of the giant tin belt within the Youjiang Basin,South China,offers a unique opportunity to explore and better constrain the current geodynamic model for tin mineralization.This deposit,composed of NW-to NWN-striking vein swarms with W-Sn mineralization,is hosted in the Middle Triassic clastic rocks without igneous rocks near its mineralization.Structural analysis indicates that the Youjiang fold-and-fault belt and the ore-related structures in the Pingna deposit experienced five deformation phases(D_(1)–D_(5)).The pre-ore NE-striking compression(D_(1);σv=σ3)initiated fault-fracture meshes,followed by NE-striking extension(D_(2)),while NW-striking compression(D_(3);σv=σ2)enhanced the vertical connectivity of the meshes.Syn-mineralization E-W extension(D_(4);σv=σ1)facilitated upward through-going flow and hydrothermal fluids infilled the meshes,forming a fault-vein system.The mineralized veins were cut across by post-ore WNW-striking oblique fault with sinistral and normal components(D_(5)).The meshes dictated Sn-W orebodies localization.Hydrothermal veins formed in three stages:(I)muscovitization-bordered tin-dominated quartz vein swarms along the Pingna fault;(II)W-dominated lit-par-lit vein system;and(III)barren calcite veins crosscutting the former veins.The Pingna W-Sn mineralization formed during the Late Cretaceous as constrained by the cassiterite(Cst1)U-Pb age of 95.6±2.4 Ma(2σ,MSWD=1.2),muscovite(Ms1)^(40)Ar-^(39)Ar plateau age of 93.9±0.1 Ma(2σ,MSWD=1.7),and molybdenite Re-Os age of 92.9±1.2 Ma(2σ,MSWD=0.3).Outward lateral zoning of the Sn-W mineralization,as well as associated muscovitization and silicification implies the epicenter of hydrothermal fluid is near the No.II vein swarm.Contemporaneous felsic dykes coupling with the inferred intrusions demonstrate that the Pingna deposit is a distal hydrothermal W-Sn deposit.The releasing bend of the NW-striking Pingna fault controlled the distribution of tin-dominated mineralization,while the anticlines controlled the tungsten-dominated mineralization.Our findings suggest that the localization and formation of the Pingna W-Sn veins were controlled by Late-Cretaceous regional transtensional stress field and polyphase deformation,rather than previously proposed local extension of the Youjiang Basin.The discovery of the Pingna W-Sn deposit highlights the interior of the Youjiang Basin as a promising area for tungsten-tin exploration.展开更多
Cassiterite(SnO_(2))is the main ore mineral of tin in magmatic-hydrothermal tin deposits,but tin transport and precipitation mechanisms from hydrothermal fluids remain poorly understood.We critically evalu-ated aqueou...Cassiterite(SnO_(2))is the main ore mineral of tin in magmatic-hydrothermal tin deposits,but tin transport and precipitation mechanisms from hydrothermal fluids remain poorly understood.We critically evalu-ated aqueous tin speciation in hydrothermal fluids from extensive experimental data and thermody-namic modeling.Sn(II)chloride complexes in hydrothermal fluids exist mainly as SnCl^(+),SnCl_(2)(aq),and SnCl_(3).The revised Helgeson-Kirkham-Flowers model parameters of these three tin species and two tin ions(Sn^(4+)and Sn^(2+))were derived from the correlation algorithms among these parameters,and the standard molar properties of cassiterite were optimized to be internally consistent with the available thermodynamic dataset.These thermodynamic parameters,together with the available equilibrium con-stant equation of Sn(IV)chloride complexes,could reproduce the available solubility data of cassiterite in acidic solutions at 400-700℃under oxygen fugacity(f_(o2))levels buffered by hematite-magnetite(HM)or nickel-nickel oxide(NNO).These comparisons allow modeling chemical systems of SnO_(2)-NaCl-HCI-H_(2)O(liquid phase)to examine tin transport and cassiterite precipitation mechanisms under tin-mineralizing conditions:300--500℃,50-150 MPa,2 molal NaCI,and fo。levels from QFM(quartz-fayalite-magnetite)to HM.Sn(I)chloride complexes are commonly interpreted to dominate in aqueous tin speciation under f_(o2)=NNO,but our modeling results indicate that considerable contents of Sn(IV)chloride complexes also exist in those reduced fluids with high HCI contents,consistent with recent in situ high-temperature experiments and molecular dynamic simulations.The Sn(I)/Sn(IV)ratios in fluids depends onfo,temperature,and HCl contents.A considerable amount of Sn(IV)possibly exist in an early mineralization stage even under f_(o2)=NNO;if so,redox reactions are unnecessary to precipitate cassiterite from these mineralizing fluids.We find that even if the f_(o2)levels are constant,simple cooling can alter mineralizing fluids to be more oxidized(e.g.,from QFM to HM)and cause cassiterite precipitation,indicating that oxidizing agents are not necessary as previously thought.This explains why cassiterite can precipitate in host rocks(e.g.,sandstone or quartzite)that do not provide oxidizing agents.A simple rise in f_(o2),levels and pH neutralization(e.g.,greisenization)also cause cassiterite precipitation.Cassiterite solubility in oxidized acidic hydrothermal fluids(NNO<f_(o2),<HM)is high enough to account for the tin contents of fluid inclusions from typical tin deposits,but the mineralization potential of oxdized fluids is inferior to reduced fluids(f_(o2),≤NNO)under the same conditions.展开更多
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(grant numbers 17210803D and 19273703D)the Science and Technology Spark Project of the Hebei Seismological Bureau(grant number DZ20180402056)+1 种基金the Education Department of Hebei Province(grant number QN2018095)the Polytechnic College of Hebei University of Science and Technology.
文摘The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging.Here,a pedestrian re-identification method based on the fusion of local features and gait energy image(GEI)features is proposed.In this method,the human body is divided into four regions according to joint points.The color and texture of each region of the human body are extracted as local features,and GEI features of the pedestrian gait are also obtained.These features are then fused with the local and GEI features of the person.Independent distance measure learning using the cross-view quadratic discriminant analysis(XQDA)method is used to obtain the similarity of the metric function of the image pairs,and the final similarity is acquired by weight matching.Evaluation of experimental results by cumulative matching characteristic(CMC)curves reveals that,after fusion of local and GEI features,the pedestrian re-identification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(grant numbers 17210803D and 19273703D)the Science and Technology Spark Project of the Hebei Seismological Bureau(grant number DZ20180402056)+1 种基金the Education Department of Hebei Province(grant number QN2018095)the Polytechnic College of Hebei University of Science and Technology.
文摘In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life.
基金This research was funded by College Student Innovation and Entrepreneurship Training Program,Grant Number 2021055Z and S202110082031the Special Project for Cultivating Scientific and Technological Innovation Ability of College and Middle School Students in Hebei Province,Grant Number 2021H011404.
文摘Aiming at the problems of low target image resolution,insufficient target feature extraction,low detection accuracy and poor real time in remote engineering vehicle detection,an improved CenterNet target detection model is proposed in this paper.Firstly,EfficientNet-B0 with Efficient Channel Attention(ECA)module is used as the basic network,which increases the quality and speed of feature extraction and reduces the number of model parameters.Then,the proposed Adaptive Fusion Bidirectional Feature Pyramid Network(AF-BiFPN)module is applied to fuse the features of different feature layers.Furthermore,the feature information of engineering vehicle targets is added by making full use of the high-level semantic and low-level fine-grained feature information of the target,which overcomes the problem that the original CenterNet network did not perform well in small target detection and improve the detection accuracy of the network.Finally,the tag coding strategy and bounding box regression method of CenterNet are optimized by introducing positioning quality loss.The accuracy of target prediction is increased by joint prediction of center position and target size.Experimental results show that the mean Average Precision(mAP)of the improved CenterNet model is 94.74%on the engineering vehicle dataset,and the detection rate is 29 FPS.Compared with the original CenterNet model based on ResNet-18,the detection accuracy of this model is improved by 16.29%,the detection speed is increased by 9 FPS,and the memory usage is reduced by 43 MB.Compared with YOLOv3 and YOLOv4,the mAP of this model is improved by 19.9%and 5.61%respectively.The proposed method can detect engineering vehicles more quickly and accurately in far distance.It has obvious advantages in target detection compared with traditional methods.
基金This research was funded by the Hebei Science and Technology Support Program Project(Grant No.19273703D)the Hebei Higher Education Science and Technology Research Project(Grant No.ZD2020318).
文摘Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(grant numbers 17210803D and 19273703D)the Science and Technology Spark Project of the Hebei Seismological Bureau(grant number DZ20180402056)+1 种基金the Education Department of Hebei Province(grant number QN2018095)the Polytechnic College of Hebei University of Science and Technology.
文摘Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Gaze-tracking systems are an important research topic in the human-computer interaction field.As one of the core modules of the head-mounted gaze-tracking system,pupil positioning affects the accuracy and stability of the system.By tracking eye movements to better locate the center of the pupil,this paper proposes a method for pupil positioning based on the starburst model.The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye.In this paper,we propose a method for detecting the feature points of the pupil edge based on the starburst model,which clusters feature points and uses the RANdom SAmple Consensus(RANSAC)algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center.Our experimental results show that the algorithm has higher precision,higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(Grant Number 19273703D)the Science and Technology Research Project of Hebei Province(Grant Number ZD2020318).
文摘Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes.The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience,which have problems such as inaccurate recognition time,time-consuming and energy sapping.Therefore,this paper proposes a neural network-based method for predicting the flowering phase of pear tree.Firstly,based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019,three principal components(the temperature factor,weather factor,and humidity factor)with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method.Then,the three components are used as input factors for the BP neural network.A BP neural network prediction model is constructed based on genetic algorithm optimization.The crossover operator and mutation operator in the adaptive genetic algorithm are improved.Finally,the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper.The results demonstrate that,the model can solve the local optimization problem of the BP neural network model.The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy.Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method.This method can provide more reliable forecast information for the blooming period,which can provide decision-making reference for improving the development of tourism industry.
基金This work is supported by the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)Weihai Science and Technology Development Program(2016DXGJMS15)Key Research and Development Program in Shandong Provincial(2017GGX90103).
文摘Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.
基金This research was funded by the Hebei Science and Technology Support Program Project(19273703D)the Hebei Higher Education Science and Technology Research Project(ZD2020318).
文摘To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.
文摘Since late 1970s, worldwide EFL classrooms have witnessed the change from teacher-centered learning to learner-centered and more effective language learning. Research findings have shown repeatedly the importance of developing learner autonomy. Based on the review of the relevant literature both in China and around the world, the present paper discusses the main factors affecting the promotion of EFL learner autonomy in China, including motivation, learners＇ metacognitive knowledge and learning environment. Some strategies for fostering learner autonomy are also recommended.
基金supported by the National Science and Technology Major Project of China(Grants:2024ZD1001701)the China Geological Survey(Grants:DD20240127,DD20230344,DD20190161)+1 种基金the National Natural Science Foundation of China(Grants:41702095)the CGS Research Fund(Grants:DZLXJK202203).
文摘Growing evidence suggests that extensional/transtensional settings are favorable for the formation of tin deposits,yet the underlying geodynamic mechanism remains equivocal.The Pingna W-Sn deposit,found in the underexplored interior of the giant tin belt within the Youjiang Basin,South China,offers a unique opportunity to explore and better constrain the current geodynamic model for tin mineralization.This deposit,composed of NW-to NWN-striking vein swarms with W-Sn mineralization,is hosted in the Middle Triassic clastic rocks without igneous rocks near its mineralization.Structural analysis indicates that the Youjiang fold-and-fault belt and the ore-related structures in the Pingna deposit experienced five deformation phases(D_(1)–D_(5)).The pre-ore NE-striking compression(D_(1);σv=σ3)initiated fault-fracture meshes,followed by NE-striking extension(D_(2)),while NW-striking compression(D_(3);σv=σ2)enhanced the vertical connectivity of the meshes.Syn-mineralization E-W extension(D_(4);σv=σ1)facilitated upward through-going flow and hydrothermal fluids infilled the meshes,forming a fault-vein system.The mineralized veins were cut across by post-ore WNW-striking oblique fault with sinistral and normal components(D_(5)).The meshes dictated Sn-W orebodies localization.Hydrothermal veins formed in three stages:(I)muscovitization-bordered tin-dominated quartz vein swarms along the Pingna fault;(II)W-dominated lit-par-lit vein system;and(III)barren calcite veins crosscutting the former veins.The Pingna W-Sn mineralization formed during the Late Cretaceous as constrained by the cassiterite(Cst1)U-Pb age of 95.6±2.4 Ma(2σ,MSWD=1.2),muscovite(Ms1)^(40)Ar-^(39)Ar plateau age of 93.9±0.1 Ma(2σ,MSWD=1.7),and molybdenite Re-Os age of 92.9±1.2 Ma(2σ,MSWD=0.3).Outward lateral zoning of the Sn-W mineralization,as well as associated muscovitization and silicification implies the epicenter of hydrothermal fluid is near the No.II vein swarm.Contemporaneous felsic dykes coupling with the inferred intrusions demonstrate that the Pingna deposit is a distal hydrothermal W-Sn deposit.The releasing bend of the NW-striking Pingna fault controlled the distribution of tin-dominated mineralization,while the anticlines controlled the tungsten-dominated mineralization.Our findings suggest that the localization and formation of the Pingna W-Sn veins were controlled by Late-Cretaceous regional transtensional stress field and polyphase deformation,rather than previously proposed local extension of the Youjiang Basin.The discovery of the Pingna W-Sn deposit highlights the interior of the Youjiang Basin as a promising area for tungsten-tin exploration.
基金financially funded by CGS Research Fund(DZLXJK202103,DZLXJK202206,DZLXJK202203)China Geologi-cal Survey project(DD20230344)+1 种基金Guizhou Provincial Science and Technology Project(Qiankehezhicheng[2021]408)major project of Guizhou Bureau of Geology and Mineral Resources Exploration and Development(Qiandikuangkehe[2021]1).
文摘Cassiterite(SnO_(2))is the main ore mineral of tin in magmatic-hydrothermal tin deposits,but tin transport and precipitation mechanisms from hydrothermal fluids remain poorly understood.We critically evalu-ated aqueous tin speciation in hydrothermal fluids from extensive experimental data and thermody-namic modeling.Sn(II)chloride complexes in hydrothermal fluids exist mainly as SnCl^(+),SnCl_(2)(aq),and SnCl_(3).The revised Helgeson-Kirkham-Flowers model parameters of these three tin species and two tin ions(Sn^(4+)and Sn^(2+))were derived from the correlation algorithms among these parameters,and the standard molar properties of cassiterite were optimized to be internally consistent with the available thermodynamic dataset.These thermodynamic parameters,together with the available equilibrium con-stant equation of Sn(IV)chloride complexes,could reproduce the available solubility data of cassiterite in acidic solutions at 400-700℃under oxygen fugacity(f_(o2))levels buffered by hematite-magnetite(HM)or nickel-nickel oxide(NNO).These comparisons allow modeling chemical systems of SnO_(2)-NaCl-HCI-H_(2)O(liquid phase)to examine tin transport and cassiterite precipitation mechanisms under tin-mineralizing conditions:300--500℃,50-150 MPa,2 molal NaCI,and fo。levels from QFM(quartz-fayalite-magnetite)to HM.Sn(I)chloride complexes are commonly interpreted to dominate in aqueous tin speciation under f_(o2)=NNO,but our modeling results indicate that considerable contents of Sn(IV)chloride complexes also exist in those reduced fluids with high HCI contents,consistent with recent in situ high-temperature experiments and molecular dynamic simulations.The Sn(I)/Sn(IV)ratios in fluids depends onfo,temperature,and HCl contents.A considerable amount of Sn(IV)possibly exist in an early mineralization stage even under f_(o2)=NNO;if so,redox reactions are unnecessary to precipitate cassiterite from these mineralizing fluids.We find that even if the f_(o2)levels are constant,simple cooling can alter mineralizing fluids to be more oxidized(e.g.,from QFM to HM)and cause cassiterite precipitation,indicating that oxidizing agents are not necessary as previously thought.This explains why cassiterite can precipitate in host rocks(e.g.,sandstone or quartzite)that do not provide oxidizing agents.A simple rise in f_(o2),levels and pH neutralization(e.g.,greisenization)also cause cassiterite precipitation.Cassiterite solubility in oxidized acidic hydrothermal fluids(NNO<f_(o2),<HM)is high enough to account for the tin contents of fluid inclusions from typical tin deposits,but the mineralization potential of oxdized fluids is inferior to reduced fluids(f_(o2),≤NNO)under the same conditions.
