Water scarcity and environment deterioration have become main constraints to sustainable economic and social development.Scientifically assessing Water Resources Carrying Capacity(WRCC)is essential for the optimal all...Water scarcity and environment deterioration have become main constraints to sustainable economic and social development.Scientifically assessing Water Resources Carrying Capacity(WRCC)is essential for the optimal allocation of regional water resources.The hilly area at the northern foot of Yanshan Mountains is a key water conservation zone and an important water source for Beijing,Tianjin and Hebei.Grasping the current status and temporal trends of water quality and WRCC in representative small watersheds within this region is crucial for supporting rational water resources allocation and environment protection efforts.This study focuses on Pingquan City,a typical watershed in northern Hebei Province.Firstly,evaluation index systems for surface water quality,groundwater quality and WRCC were estab-lished based on the Pressure-State-Response(PSR)framework.Then,comprehensive evaluations of water quality and WRCC at the sub-watershed scale were conducted using the Varying Fuzzy Pattern Recogni-tion(VFPR)model.Finally,the rationality of the evaluation results was verified,and future scenarios were projected.Results showed that:(1)The average comprehensive evaluation scores for surface water and groundwater quality in the sub-watersheds were 1.44 and 1.46,respectively,indicating that both met the national Class II water quality standard and reflected a high-quality water environment.(2)From 2010 to 2020,the region's WRCC steadily improved,with scores rising from 2.99 to 2.83 and an average of 2.90,suggesting effective water resources management in Pingquan City.(3)According to scenario-based predic-tion,WRCC may slightly decline between 2025 and 2030,reaching 2.92 and 2.94,respectively,relative to 2020 levels.Therefore,future efforts should focus on strengthening scientific management and promoting the efficient use of water resources.Proactive measures are necessary to mitigate emerging contradiction and ensure the long-term stability and sustainability of the water resources system in the region.The evalua-tion system and spatiotemporal evolution patterns proposed in this study can provide a scientific basis for refined water resource management and ecological conservation in similar hilly areas.展开更多
In our study, entropy weight coefficients, based on Shannon entropy, were determined for an attribute recognition model to model the quality of groundwater sources. The model follows the theory previously proposed by ...In our study, entropy weight coefficients, based on Shannon entropy, were determined for an attribute recognition model to model the quality of groundwater sources. The model follows the theory previously proposed by Chen Q S. In the model, firstly, the author establishes the attribute space matrix and determines the weight based on Shannon entropy theory; secondly, calculates attribute measure; thirdly, evaluates that with confidence criterion and score criterion; finally, an application example is given. The results show that the water quality of the groundwater sources for the city comes up to the grade II or III standard. There is no pollution that obviously exceeds the standard and the water can meet people’s needs .The results from an evaluation of this model are in basic agreement with the observed situation and with a set pair analysis (SPA) model.展开更多
The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that th...The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.展开更多
The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced usin...The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.展开更多
This paper proposes the novel computer aided volleyball training system based on pattern recognition model. In general the quality of volleyball training at the same time, we should strengthen coordination, mobile pow...This paper proposes the novel computer aided volleyball training system based on pattern recognition model. In general the quality of volleyball training at the same time, we should strengthen coordination, mobile power, quality training, strengthen mobile exercises, moving fast and flexible to overcome the sudden fall of soft volleyball on the defensive and pilling adverse, such practice can be used for mobile.Strengthen the strength training, enhance the strength of the upper and lower limbs, improve the team＇ s bounce and batting speed is conducive to compete for the advantage of the Internet, better undermine the defense. With the help of the proposed system, the training process will be more effective, and the performance will be verified in the later discussions.展开更多
Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model(RRM),and then...Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model(RRM),and then mod-ified it by using the wavelet transform to acquire the Modified Resonant Recognition Model(MRRM).The key characteris-tic of the new model is that it can predict directly the protein-protein interaction from the primary sequence,and the MRRM is more suitable than the RRM for this prediction.The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction.展开更多
As a major function of smart transportation in smart cities,vehicle model recognition plays an important role in intelligent transportation.Due to the difference among different vehicle models recognition datasets,the...As a major function of smart transportation in smart cities,vehicle model recognition plays an important role in intelligent transportation.Due to the difference among different vehicle models recognition datasets,the accuracy of network model training in one scene will be greatly reduced in another one.However,if you don’t have a lot of vehicle model datasets for the current scene,you cannot properly train a model.To address this problem,we study the problem of cold start of vehicle model recognition under cross-scenario.Under the condition of small amount of datasets,combined with the method of transfer learning,load the DAN(Deep Adaptation Networks)and JAN(Joint Adaptation Networks)domain adaptation modules into the convolutional neural network AlexNet and ResNet,and get four models:AlexNet-JAN,AlexNet-DAN,ResNet-JAN,and ResNet-DAN which can achieve a higher accuracy at the beginning.Through experiments,transfer the vehicle model recognition from the network image dataset(source domain)to the surveillance-nature dataset(target domain),both Top-1 and Top-5 accuracy have been improved by at least 20%.展开更多
Water inrush is one of the most serious geological hazards in underground engineering construction.In order to effectively prevent and control the occurrence of water inrush,a new attribute interval recognition theory...Water inrush is one of the most serious geological hazards in underground engineering construction.In order to effectively prevent and control the occurrence of water inrush,a new attribute interval recognition theory and method is proposed to systematically evaluate the risk of water inrush in karst tunnels.Its innovation mainly includes that the value of evaluation index is an interval rather than a certain value;the single-index attribute evaluation model is improved non-linearly based on the idea of normal distribution;the synthetic attribute interval analysis method based on improved intuitionistic fuzzy theory is proposed.The TFN-AHP method is proposed to analyze the weight of evaluation index.By analyzing geological factors and engineering factors in tunnel zone,a multi-grade hierarchical index system for tunnel water inrush risk assessment is established.The proposed method is applied to ventilation incline of Xiakou tunnel,and its rationality and practicability is verified by comparison with field situation and evaluation results of other methods.In addition,the results evaluated by this method,which considers that water inrush is a complex non-linear system and the geological conditions have spatial variability,are more accurate and reliable.And it has good applicability in solving the problem of certain and uncertain problem.展开更多
Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emp...Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emptive tactical opportunities for the fighter to gain air superiority. The existing methods to solve this problem have some defects such as dependence on empirical knowledge, difficulty in interpreting the recognition results, and inability to meet the requirements of actual air combat. So an online hierarchical recognition method for target tactical intention in BVR air combat based on cascaded support vector machine (CSVM) is proposed in this study. Through the mechanism analysis of BVR air combat, the instantaneous and cumulative feature information of target trajectory and relative situation information are introduced successively using online automatic decomposition of target trajectory and hierarchical progression. Then the hierarchical recognition model from target maneuver element, tactical maneuver to tactical intention is constructed. The CSVM algorithm is designed for solving this model, and the computational complexity is decomposed by the cascaded structure to overcome the problems of convergence and timeliness when the dimensions and number of training samples are large. Meanwhile, the recognition result of each layer can be used to support the composition analysis and interpretation of target tactical intention. The simulation results show that the proposed method can effectively realize multi-dimensional online accurate recognition of target tactical intention in BVR air combat.展开更多
As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.D...As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.展开更多
A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segme...A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.展开更多
Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new meth...Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.展开更多
Vehicle model recognition(VMR)benefits the parking,surveillance,and tolling system by automatically identifying the exact make and model of the passing vehicles.Edge computing technology enables the roadside facilitie...Vehicle model recognition(VMR)benefits the parking,surveillance,and tolling system by automatically identifying the exact make and model of the passing vehicles.Edge computing technology enables the roadside facilities and mobile cameras to achcieve VMR in realtime.Current work generally relies on a specific view of the vehicle or requires huge calculation capability to deploy the end-to-end deep learning network.This paper proposes a lightweight two-stage identification method based on object detection and image retrieval techniques,which empowers us the ability of recognizing the vehicle model from an arbitrary view.The first-stage model estimates the vehicle posture using object detection and similarity matching,which is cost-efficient and suitable to be programmed in the edge computing devices;the second-stage model retrieves the vehicle’s label from the dataset based on gradient boosting decision tree(GBDT)algorithm and VGGNet,which is flexible to the changing dataset.More than 8000 vehicle images are labeled with their components’information,such as headlights,windows,wheels,and logos.The YOLO network is employed to detect and localize the typical components of a vehicle.The vehicle postures are estimated by the spatial relationship between different segmented components.Due to the variety of the perspectives,a 7-dimensional vector is defined to represent the relative posture of the vehicle and screen out the images with a similar photographic perspective.Two algorithms are used to extract the features from each image patch:(1)the scale invariant feature transform(SIFT)combined with the bag-of-features(BoF)and(2)pre-trained deep neural network.The GBDT is applied to evaluate the weight of each component regarding its impact on VMR.The descriptors of each component are then aggregated to retrieve the best matching image from the database.The results showed its advantages in terms of accuracy(89.2%)and efficiency,demonstrating the vast potential of applying this method to large-scale vehicle model recognition.展开更多
A technique for the determination of tannin content in traditional Chinese medicine injections(TCMI)was developed based on ultraviolet(UV)spectroscopy.Chemometrics were used to construct a mathematical model of absorp...A technique for the determination of tannin content in traditional Chinese medicine injections(TCMI)was developed based on ultraviolet(UV)spectroscopy.Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections,and the model was veried and applied.The results showed that the established UV-based spectral partial least squares regression(PLS)tannin content model performed well with a correlation coefficient(r)of 0.952,root mean square error of calibration(RMSEC)of 0.476g/ml,root mean square error of validation(RMSEV)of 1.171g/ml,and root mean square error of prediction(RMSEP)of 0.465g/ml.Pattern recognition models using linear discriminant analysis(LDA)and k nearest neighbor(k-NN)classiers based on UV spectrum could successfully classify different types of injections and different manufacturers.The established method to measure tannin content based on UV spectroscopy is simple,rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections.展开更多
Based on the safety coefficient method,which assigns rock failure criteria to calculate the rock mass unit,the safety coefficient contour of surrounding rock is plotted to judge the distribution form of the fractured ...Based on the safety coefficient method,which assigns rock failure criteria to calculate the rock mass unit,the safety coefficient contour of surrounding rock is plotted to judge the distribution form of the fractured zone in the roadway.This will provide the basis numerical simulation to calculate the surrounding rock fractured zone in a roadway.Using the single factor and multi-factor orthogonal test method,the evolution law of roadway surrounding rock displacements,plastic zone and stress distribution under different conditions is studied.It reveals the roadway surrounding rock burst evolution process,and obtains five kinds of failure modes in deep soft rock roadway.Using the fuzzy mathematics clustering analysis method,the deep soft surrounding rock failure model in Zhujixi mine can be classified and patterns recognized.Compared to the identification results and the results detected by geological radar of surrounding rock loose circle,the reliability of the results of the pattern recognition is verified and lays the foundations for the support design of deep soft rock roadways.展开更多
With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available fro...With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.展开更多
After pointed the unreasonableness of the three basic assumptions contained in HMM, we introduce the theory and the advantage of Stochastic najectory Models (STMs) that possibly resolve these problems caused by HMM as...After pointed the unreasonableness of the three basic assumptions contained in HMM, we introduce the theory and the advantage of Stochastic najectory Models (STMs) that possibly resolve these problems caused by HMM assumptions. In STM, the acoustic observations of an acoustic unit are represented as clusters of trajectories in a parameter space.The trajectories are modelled by mixture of probability density functions of random sequence of states. After analyzing the characteristics of Chinese speech, the acoustic units for continuous Chinese speech recognition based on STM are discussed and phone-like units are suggested. The performance of continuous Chinese speech recognition based on STM is studied on VINICS system. The experimental results prove the efficiency of STM and the consistency of phone-like units.展开更多
In order to monitor dangerous areas in coal mines automatically,we propose to detect helmets from underground coal mine videos for detecting miners.This method can overcome the impact of similarity between the targets...In order to monitor dangerous areas in coal mines automatically,we propose to detect helmets from underground coal mine videos for detecting miners.This method can overcome the impact of similarity between the targets and their background.We constructed standard images of helmets,extracted four directional features,modeled the distribution of these features using a Gaussian function and separated local images of frames into helmet and non-helmet classes.Out experimental results show that this method can detect helmets effectively.The detection rate was 83.7%.展开更多
基金financially supported by China Geological Survey Project(No.DD20220954)Open Funding Project of the Key Laboratory of Groundwater Sciences and Engineering,Ministry of Natural Resources(No.SK202301-4)+2 种基金Science and Technology Innovation Foundation of Comprehensive Survey&Command Center for Natural Resources(No.KC20240003)Yanzhao Shanshui Science and Innovation Fund of Langfang Integrated Natural Resources Survey Center,China Geological Survey(No.YZSSJJ202401-001)Open Foundation of the Key Laboratory of Coupling Process and Effect of Natural Resources Elements(No.2022KFKTC009).
文摘Water scarcity and environment deterioration have become main constraints to sustainable economic and social development.Scientifically assessing Water Resources Carrying Capacity(WRCC)is essential for the optimal allocation of regional water resources.The hilly area at the northern foot of Yanshan Mountains is a key water conservation zone and an important water source for Beijing,Tianjin and Hebei.Grasping the current status and temporal trends of water quality and WRCC in representative small watersheds within this region is crucial for supporting rational water resources allocation and environment protection efforts.This study focuses on Pingquan City,a typical watershed in northern Hebei Province.Firstly,evaluation index systems for surface water quality,groundwater quality and WRCC were estab-lished based on the Pressure-State-Response(PSR)framework.Then,comprehensive evaluations of water quality and WRCC at the sub-watershed scale were conducted using the Varying Fuzzy Pattern Recogni-tion(VFPR)model.Finally,the rationality of the evaluation results was verified,and future scenarios were projected.Results showed that:(1)The average comprehensive evaluation scores for surface water and groundwater quality in the sub-watersheds were 1.44 and 1.46,respectively,indicating that both met the national Class II water quality standard and reflected a high-quality water environment.(2)From 2010 to 2020,the region's WRCC steadily improved,with scores rising from 2.99 to 2.83 and an average of 2.90,suggesting effective water resources management in Pingquan City.(3)According to scenario-based predic-tion,WRCC may slightly decline between 2025 and 2030,reaching 2.92 and 2.94,respectively,relative to 2020 levels.Therefore,future efforts should focus on strengthening scientific management and promoting the efficient use of water resources.Proactive measures are necessary to mitigate emerging contradiction and ensure the long-term stability and sustainability of the water resources system in the region.The evalua-tion system and spatiotemporal evolution patterns proposed in this study can provide a scientific basis for refined water resource management and ecological conservation in similar hilly areas.
文摘In our study, entropy weight coefficients, based on Shannon entropy, were determined for an attribute recognition model to model the quality of groundwater sources. The model follows the theory previously proposed by Chen Q S. In the model, firstly, the author establishes the attribute space matrix and determines the weight based on Shannon entropy theory; secondly, calculates attribute measure; thirdly, evaluates that with confidence criterion and score criterion; finally, an application example is given. The results show that the water quality of the groundwater sources for the city comes up to the grade II or III standard. There is no pollution that obviously exceeds the standard and the water can meet people’s needs .The results from an evaluation of this model are in basic agreement with the observed situation and with a set pair analysis (SPA) model.
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （50579009, 70471090） the National 10 th Five Year Scientific Project of China for Tackling the Key Problems （2004BA608B-02 - 02） and the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China （Department of Education and Personnel [2002] 350）.
文摘The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.
基金This research was supported by the Science and Technology Department of Jilin Province[20210202128NC http://kjt.jl.gov.cn]The People’s Republic of China Ministry of Science and Technology[2018YFF0213606-03 http://www.most.gov.cn]+1 种基金the Jilin Province Development and Reform Commission[2019C021 http://jldrc.jl.gov.cn]the Science and Technology Bureau of Changchun City[21ZGN27 http://kjj.changchun.gov.cn].
文摘The scale of deer breeding has gradually increased in recent years and better information management is necessary,which requires the identification of individual deer.In this paper,a deer face dataset is produced using face images obtained from different angles,and an improved residual neural network(ResNet)-based recognition model is proposed to extract the features of deer faces,which have high similarity.The model is based on ResNet-50,which reduces the depth of the model,and the network depth is only 29 layers;the model connects Squeeze-and-Excitation(SE)modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer.A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock.The Rectified Linear Unit(ReLU)activation function in the network is replaced by the Exponential Linear Unit(ELU)activation function to reduce information loss during forward propagation of the network.The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SEResnet,which is demonstrated to identify individuals accurately.By setting up comparative experiments under different structures,the model reduces the amount of parameters,ensures the accuracy of the model,and improves the calculation speed of the model.Using the improved method in this paper to compare with the classical model and facial recognition models of different animals,the results show that the recognition effect of this research method is the best,with an average recognition accuracy of 97.48%.The sika deer face recognition model proposed in this study is effective.The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.
文摘This paper proposes the novel computer aided volleyball training system based on pattern recognition model. In general the quality of volleyball training at the same time, we should strengthen coordination, mobile power, quality training, strengthen mobile exercises, moving fast and flexible to overcome the sudden fall of soft volleyball on the defensive and pilling adverse, such practice can be used for mobile.Strengthen the strength training, enhance the strength of the upper and lower limbs, improve the team＇ s bounce and batting speed is conducive to compete for the advantage of the Internet, better undermine the defense. With the help of the proposed system, the training process will be more effective, and the performance will be verified in the later discussions.
基金This work was supported by National High-Tech Research and Development Program of China(No.2002AA234021).
文摘Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model(RRM),and then mod-ified it by using the wavelet transform to acquire the Modified Resonant Recognition Model(MRRM).The key characteris-tic of the new model is that it can predict directly the protein-protein interaction from the primary sequence,and the MRRM is more suitable than the RRM for this prediction.The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction.
基金This work was supported by CETC Joint Research Program under Grant 6141B08020101,6141B08080101National Key R&D Program of China under Grant 2018ZX09201014the National Natural Science Foundation of China under Grant 61002011.
文摘As a major function of smart transportation in smart cities,vehicle model recognition plays an important role in intelligent transportation.Due to the difference among different vehicle models recognition datasets,the accuracy of network model training in one scene will be greatly reduced in another one.However,if you don’t have a lot of vehicle model datasets for the current scene,you cannot properly train a model.To address this problem,we study the problem of cold start of vehicle model recognition under cross-scenario.Under the condition of small amount of datasets,combined with the method of transfer learning,load the DAN(Deep Adaptation Networks)and JAN(Joint Adaptation Networks)domain adaptation modules into the convolutional neural network AlexNet and ResNet,and get four models:AlexNet-JAN,AlexNet-DAN,ResNet-JAN,and ResNet-DAN which can achieve a higher accuracy at the beginning.Through experiments,transfer the vehicle model recognition from the network image dataset(source domain)to the surveillance-nature dataset(target domain),both Top-1 and Top-5 accuracy have been improved by at least 20%.
基金Project(51722904)supported by the National Science Fund for Excellent Young Scholars,ChinaProject(51679131)supported by the National Natural Science Foundation of China+2 种基金Project(2019JZZY010601)supported by the Shandong Provincial Key Research and Development Program(Major Scientific and Technological Innovation Project),ChinaProject(KJ1712304)supported by the Science and Technology Research Program of Chongqing Municipal Education Commission,ChinaProject(2016XJQN13)supported by the Yangtze Normal University Research Project,China
文摘Water inrush is one of the most serious geological hazards in underground engineering construction.In order to effectively prevent and control the occurrence of water inrush,a new attribute interval recognition theory and method is proposed to systematically evaluate the risk of water inrush in karst tunnels.Its innovation mainly includes that the value of evaluation index is an interval rather than a certain value;the single-index attribute evaluation model is improved non-linearly based on the idea of normal distribution;the synthetic attribute interval analysis method based on improved intuitionistic fuzzy theory is proposed.The TFN-AHP method is proposed to analyze the weight of evaluation index.By analyzing geological factors and engineering factors in tunnel zone,a multi-grade hierarchical index system for tunnel water inrush risk assessment is established.The proposed method is applied to ventilation incline of Xiakou tunnel,and its rationality and practicability is verified by comparison with field situation and evaluation results of other methods.In addition,the results evaluated by this method,which considers that water inrush is a complex non-linear system and the geological conditions have spatial variability,are more accurate and reliable.And it has good applicability in solving the problem of certain and uncertain problem.
基金The authors gratefully acknowledge the support of the National Natural Science Foundation of China under Grant No.62076204 and Grant No.61612385in part by the Postdoctoral Science Foundation of China under Grants No.2021M700337in part by the Fundamental Research Funds for the Central Universities under Grant No.3102019ZX016.
文摘Online accurate recognition of target tactical intention in beyond-visual-range (BVR) air combat is an important basis for deep situational awareness and autonomous air combat decision-making, which can create pre-emptive tactical opportunities for the fighter to gain air superiority. The existing methods to solve this problem have some defects such as dependence on empirical knowledge, difficulty in interpreting the recognition results, and inability to meet the requirements of actual air combat. So an online hierarchical recognition method for target tactical intention in BVR air combat based on cascaded support vector machine (CSVM) is proposed in this study. Through the mechanism analysis of BVR air combat, the instantaneous and cumulative feature information of target trajectory and relative situation information are introduced successively using online automatic decomposition of target trajectory and hierarchical progression. Then the hierarchical recognition model from target maneuver element, tactical maneuver to tactical intention is constructed. The CSVM algorithm is designed for solving this model, and the computational complexity is decomposed by the cascaded structure to overcome the problems of convergence and timeliness when the dimensions and number of training samples are large. Meanwhile, the recognition result of each layer can be used to support the composition analysis and interpretation of target tactical intention. The simulation results show that the proposed method can effectively realize multi-dimensional online accurate recognition of target tactical intention in BVR air combat.
基金financially supported by the National Key Research and Development Program of China(No.2019YFC1805400)。
文摘As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.
基金This project is supported by General Electric Company and National Advanced Technology Project of China(No.863-511-942-018).
文摘A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.
基金Supported by the Ministerial Level Research Foundation(404040401)
文摘Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.
基金supported by the Scientific Research Project of Shanghai Science and Technology Commission of China(No.21DZ1200601)the National Natural Science Foundation of China(No.NSFC52108411).
文摘Vehicle model recognition(VMR)benefits the parking,surveillance,and tolling system by automatically identifying the exact make and model of the passing vehicles.Edge computing technology enables the roadside facilities and mobile cameras to achcieve VMR in realtime.Current work generally relies on a specific view of the vehicle or requires huge calculation capability to deploy the end-to-end deep learning network.This paper proposes a lightweight two-stage identification method based on object detection and image retrieval techniques,which empowers us the ability of recognizing the vehicle model from an arbitrary view.The first-stage model estimates the vehicle posture using object detection and similarity matching,which is cost-efficient and suitable to be programmed in the edge computing devices;the second-stage model retrieves the vehicle’s label from the dataset based on gradient boosting decision tree(GBDT)algorithm and VGGNet,which is flexible to the changing dataset.More than 8000 vehicle images are labeled with their components’information,such as headlights,windows,wheels,and logos.The YOLO network is employed to detect and localize the typical components of a vehicle.The vehicle postures are estimated by the spatial relationship between different segmented components.Due to the variety of the perspectives,a 7-dimensional vector is defined to represent the relative posture of the vehicle and screen out the images with a similar photographic perspective.Two algorithms are used to extract the features from each image patch:(1)the scale invariant feature transform(SIFT)combined with the bag-of-features(BoF)and(2)pre-trained deep neural network.The GBDT is applied to evaluate the weight of each component regarding its impact on VMR.The descriptors of each component are then aggregated to retrieve the best matching image from the database.The results showed its advantages in terms of accuracy(89.2%)and efficiency,demonstrating the vast potential of applying this method to large-scale vehicle model recognition.
基金the State Administration of Traditional Chinese Medicine of Zhejiang Province Project(Grant No.2015ZQ022)Zhejiang TCM Health Science and Technology Project(Grant No.2015KYB110)Zhejiang Provincial Natural Science Foundation of China(Grant No.LY17B020002).
文摘A technique for the determination of tannin content in traditional Chinese medicine injections(TCMI)was developed based on ultraviolet(UV)spectroscopy.Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections,and the model was veried and applied.The results showed that the established UV-based spectral partial least squares regression(PLS)tannin content model performed well with a correlation coefficient(r)of 0.952,root mean square error of calibration(RMSEC)of 0.476g/ml,root mean square error of validation(RMSEV)of 1.171g/ml,and root mean square error of prediction(RMSEP)of 0.465g/ml.Pattern recognition models using linear discriminant analysis(LDA)and k nearest neighbor(k-NN)classiers based on UV spectrum could successfully classify different types of injections and different manufacturers.The established method to measure tannin content based on UV spectroscopy is simple,rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections.
基金provided by the National Natural Science Foundation of China(Nos.51322401,51309222,51323004,51579239 and 51574223)the Opening Project Fund of Shandong Provincial Key Laboratory of Civil Engineering Disaster Prevention and Mitigation(No.CDPM2014KF03)+2 种基金the State Key Laboratory for GeoMechanics Opening Project Fund of Shandong Provincial Key Laboratory of Civil Engineering Disaster Prevention and MitigationDeep Underground Engineering,China University of Mining&Technology(No.SKLGDUEK1305)China Postdoctoral Science Foundation(Nos.2014M551700and 2013M531424)
文摘Based on the safety coefficient method,which assigns rock failure criteria to calculate the rock mass unit,the safety coefficient contour of surrounding rock is plotted to judge the distribution form of the fractured zone in the roadway.This will provide the basis numerical simulation to calculate the surrounding rock fractured zone in a roadway.Using the single factor and multi-factor orthogonal test method,the evolution law of roadway surrounding rock displacements,plastic zone and stress distribution under different conditions is studied.It reveals the roadway surrounding rock burst evolution process,and obtains five kinds of failure modes in deep soft rock roadway.Using the fuzzy mathematics clustering analysis method,the deep soft surrounding rock failure model in Zhujixi mine can be classified and patterns recognized.Compared to the identification results and the results detected by geological radar of surrounding rock loose circle,the reliability of the results of the pattern recognition is verified and lays the foundations for the support design of deep soft rock roadways.
基金supported by the National Key Research and Development Program(No.2016YFB0800302)
文摘With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.
文摘After pointed the unreasonableness of the three basic assumptions contained in HMM, we introduce the theory and the advantage of Stochastic najectory Models (STMs) that possibly resolve these problems caused by HMM assumptions. In STM, the acoustic observations of an acoustic unit are represented as clusters of trajectories in a parameter space.The trajectories are modelled by mixture of probability density functions of random sequence of states. After analyzing the characteristics of Chinese speech, the acoustic units for continuous Chinese speech recognition based on STM are discussed and phone-like units are suggested. The performance of continuous Chinese speech recognition based on STM is studied on VINICS system. The experimental results prove the efficiency of STM and the consistency of phone-like units.
基金provided by the National High Technology Research and Development Program of China (No.2008AA062202)
文摘In order to monitor dangerous areas in coal mines automatically,we propose to detect helmets from underground coal mine videos for detecting miners.This method can overcome the impact of similarity between the targets and their background.We constructed standard images of helmets,extracted four directional features,modeled the distribution of these features using a Gaussian function and separated local images of frames into helmet and non-helmet classes.Out experimental results show that this method can detect helmets effectively.The detection rate was 83.7%.
