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Effect of exercise prescription teaching on exercise quality and mental health status of college students 被引量:4
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作者 Xing-Long Zhong Da-Li Sheng +1 位作者 Tong-Zhou Cheng Zhe-Wei Zhang 《World Journal of Psychiatry》 SCIE 2023年第5期191-202,共12页
BACKGROUND The teaching mode of fitness exercise prescriptions for college students in physical education conforms to the scientific principles and rules of fitness,which can adapt to the characteristics of students’... BACKGROUND The teaching mode of fitness exercise prescriptions for college students in physical education conforms to the scientific principles and rules of fitness,which can adapt to the characteristics of students’individual physiological functions and stimulate their interest in learning.AIM To analyze the effect of prescribed exercise teaching on the sports quality and mental health of college students.METHODS The participants of the study were 240 students in our class of 2021,of which 142 were men and 98 were women.The 240 students were randomly divided into an experimental group using the exercise prescription teaching model and a control group using the conventional teaching model.The experimental and control groups were divided into four classes of 30 students each.The teaching activities of the two teaching mode groups were strictly controlled,and the same tests were used before and after the experiment to test the subjects’exercise quality(including standing long jump,50 m race,800 m race,sit-ups,sit-and-reach),physical form(including height,weight,Ketorolai index),cardiopulmonary function(including heart rate,blood pressure,spirometry,12-min running distance,maximum oxygen intake)and mental health(SCL-90,including somatization,obsessive-compulsive,interpersonal,depression,anxiety,hostility,phobia,paranoia,psychotic symptoms)to understand the effects of the exercise prescription teaching mode on students’physical and mental health status.RESULTS There were differences in the exercise scores of standing long jump,50 m,800 m/1000 m running,sit-ups,and sit-and-reach in the experimental group after the experiment compared with those before the experiment,and the above indices of the experimental group were different from those of the control group after the experiment(P<0.05).There were differences in body weight and Ketorolai index in the experimental group after the experiment compared to those before the experiment,and the indices of the experimental group were also different from those of the control group after the experiment(P<0.05).After the experiment,there were differences in spirometry,12-min running distance,and maximum oxygen intake in the experimental group compared to those before the experiment,and the indices of the experimental group were also different from those of the control group after the experiment(P<0.05).After the experiment,the indicators of somatization,interpersonal sensitivity,depression,anxiety,and hostility in the experimental group were different from those in the pre-experimental group,and the indexes of the experimental group were also different from those of the control group after the experiment(P<0.05).CONCLUSION Exercise prescription teaching can mobilize college students’consciousness,enthusiasm,and initiative;expand personalities;enhance physical fitness and improve their mental health more than the conventional fitness exercise prescription teaching method. 展开更多
关键词 TEACHING PRESCRIPTION INTAKE
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Image Recognition Model of Fraudulent Websites Based on Image Leader Decision and Inception-V3 Transfer Learning
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作者 Shengli Zhou Cheng Xu +3 位作者 Rui Xu Weijie Ding Chao Chen Xiaoyang Xu 《China Communications》 SCIE CSCD 2024年第1期215-227,共13页
The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current re... The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models. 展开更多
关键词 fraudulent website image leaders telecom fraud transfer learning
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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion
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作者 Jianfeng Lu Caijin Li +2 位作者 Xiangye Huang Chen Cui Mahmoud Emam 《Computers, Materials & Continua》 SCIE EI 2024年第8期3047-3065,共19页
The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.Howeve... The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach. 展开更多
关键词 Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network
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Monitoring and Early Warning of New Cyber-Telecom Crime Platform Based on BERT Migration Learning 被引量:6
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作者 Shengli Zhou Xin Wang Zerui Yang 《China Communications》 SCIE CSCD 2020年第3期140-148,共9页
The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation... The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method. 展开更多
关键词 NEW cyber-telecom CRIME BERT model deep LEARNING monitoring and WARNING text analysis
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A Privacy-Based SLA Violation Detection Model for the Security of Cloud Computing 被引量:4
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作者 Shengli Zhou Lifa Wu Canghong Jin 《China Communications》 SCIE CSCD 2017年第9期155-165,共11页
A Service Level Agreement(SLA) is a legal contract between any two parties to ensure an adequate Quality of Service(Qo S). Most research on SLAs has concentrated on protecting the user data through encryption. However... A Service Level Agreement(SLA) is a legal contract between any two parties to ensure an adequate Quality of Service(Qo S). Most research on SLAs has concentrated on protecting the user data through encryption. However, these methods can not supervise a cloud service provider(CSP) directly. In order to address this problem, we propose a privacy-based SLA violation detection model for cloud computing based on Markov decision process theory. This model can recognize and regulate CSP's actions based on specific requirements of various users. Additionally, the model could make effective evaluation to the credibility of CSP, and can monitor events that user privacy is violated. Experiments and analysis indicate that the violation detection model can achieve good results in both the algorithm's convergence and prediction effect. 展开更多
关键词 SECURITY and PRIVACY markovchain cloud computing REPUTATION manage-ment SLA
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Research on Multi-Authority CP-ABE Access Control Model in Multicloud 被引量:3
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作者 Shengli Zhou Guangxuan Chen +2 位作者 Guangjie Huang Jin Shi Ting Kong 《China Communications》 SCIE CSCD 2020年第8期220-233,共14页
In order to solve the problems of data sharing security and policy conflict in multicloud storage systems(MCSS), this work designs an attribute mapping mechanism that extends ciphertext policy attribute-based encrypti... In order to solve the problems of data sharing security and policy conflict in multicloud storage systems(MCSS), this work designs an attribute mapping mechanism that extends ciphertext policy attribute-based encryption(CP-ABE), and proposes a multi-authority CP-ABE access control model that satisfies the need for multicloud storage access control. The mapping mechanism mainly involves the tree structure of CP-ABE and provides support for the types of attribute values. The framework and workflow of the model are described in detail. The effectiveness of the model is verified by building a simple prototype system, and the performance of the prototype system is analyzed. The results suggest that the proposed model is of theoretical and practical significance for access control research in MCSS. The CP-ABE has better performance in terms of computation time overhead than other models. 展开更多
关键词 CP-ABE access control multicloud multi-authority TRUST
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HASN:A Hierarchical Attack Surface Network for System Security Analysis 被引量:1
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作者 Kangyu Huang Lin Yang +2 位作者 Renfang Fu Shengli Zhou Zheng Hong 《China Communications》 SCIE CSCD 2019年第5期137-157,共21页
Attack surfaces, as one of the security models, can help people to analyse the security of systems in cyberspace, such as risk assessment by utilizing various security metrics or providing a cost-effective network har... Attack surfaces, as one of the security models, can help people to analyse the security of systems in cyberspace, such as risk assessment by utilizing various security metrics or providing a cost-effective network hardening solution. Numerous attack surface models have been proposed in the past decade,but they are not appropriate for describing complex systems with heterogeneous components. To address this limitation, we propose to use a two-layer Hierarchical Attack Surface Network(HASN) that models the data interactions and resource distribution of the system in a component-oriented view. First, we formally define the HASN by extending the entry point and exit point framework. Second, in order to assess data input risk and output risk on the HASN, we propose two behaviour models and two simulation-based risk metrics. Last, we conduct experiments for three network systems. Our experimental results show that the proposed approach is applicable and effective. 展开更多
关键词 ATTACK SURFACE SECURITY ANALYSIS SECURITY model RISK assessment
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Fast Distributed Demand Response Algorithm in Smart Grid 被引量:2
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作者 Qifen Dong Li Yu +3 位作者 Wenzhan Song Junjie Yang Yuan Wu Jun Qi 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第2期280-296,共17页
This paper proposes a fast distributed demand response U+0028 DR U+0029 algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation U+0028 GaBP U+0029 solver. At the beginning o... This paper proposes a fast distributed demand response U+0028 DR U+0029 algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation U+0028 GaBP U+0029 solver. At the beginning of each time slot, each end-user U+002F energysupplier exchanges limited rounds of messages that are not private with its neighbors, and computes the amount of energy consumption U+002F generation locally. The proposed demand response algorithm converges rapidly to a consumption U+002F generation decision that yields the optimal social welfare when the demands of endusers are low. When the demands are high, each end-user U+002F energysupplier estimates its energy consumption U+002F generation quickly such that a sub-optimal social welfare is achieved and the power system is ensured to operate within its capacity constraints. The impact of distributed computation errors on the proposed algorithm is analyzed theoretically. The simulation results show a good performance of the proposed algorithm. © 2017 Chinese Association of Automation. 展开更多
关键词 Electric power transmission networks Energy utilization
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A Holographic Diffraction Label Recognition Algorithm Based on Fusion Double Tensor Features 被引量:1
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作者 Li Li Chen Cui +2 位作者 Jianfeng Lu Shanqing Zhang Ching-Chun Chang 《Computer Systems Science & Engineering》 SCIE EI 2021年第9期291-303,共13页
As an efficient technique for anti-counterfeiting,holographic diffraction labels has been widely applied to various fields.Due to their unique feature,traditional image recognition algorithms are not ideal for the hol... As an efficient technique for anti-counterfeiting,holographic diffraction labels has been widely applied to various fields.Due to their unique feature,traditional image recognition algorithms are not ideal for the holographic diffraction label recognition.Since a tensor preserves the spatiotemporal features of an original sample in the process of feature extraction,in this paper we propose a new holographic diffraction label recognition algorithm that combines two tensor features.The HSV(Hue Saturation Value)tensor and the HOG(Histogram of Oriented Gradient)tensor are used to represent the color information and gradient information of holographic diffraction label,respectively.Meanwhile,the tensor decomposition is performed by high order singular value decomposition,and tensor decomposition matrices are obtained.Taking into consideration of the different recognition capabilities of decomposition matrices,we design a decomposition matrix similarity fusion strategy using a typical correlation analysis algorithm and projection from similarity vectors of different decomposition matrices to the PCA(Principal Component Analysis)sub-space,then,the sub-space performs KNN(K-Nearest Neighbors)classification is performed.The effectiveness of our fusion strategy is verified by experiments.Our double tensor recognition algorithm complements the recognition capability of different tensors to produce better recognition performance for the holographic diffraction label system. 展开更多
关键词 Label recognition holographic diffraction fusion double tensor matrix similarity
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着火位置与车窗破裂耦合作用下的车厢火灾机理研究
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作者 孔杰 游温娇 +1 位作者 徐志胜 刘辉 《Journal of Central South University》 SCIE EI CAS CSCD 2023年第2期654-664,共11页
为了研究着火位置和车窗破裂对车厢火灾蔓延的影响机理,本文通过火灾动力学软件(FDS)对火车车厢火灾开展了一系列的数值模拟。研究考虑了四个着火位置,车窗玻璃破裂的触发温度被设定为400℃。火灾模型监测了温度、热释放速率(HRR)、破... 为了研究着火位置和车窗破裂对车厢火灾蔓延的影响机理,本文通过火灾动力学软件(FDS)对火车车厢火灾开展了一系列的数值模拟。研究考虑了四个着火位置,车窗玻璃破裂的触发温度被设定为400℃。火灾模型监测了温度、热释放速率(HRR)、破窗时间和火焰传播等参数。研究发现,火源位置和窗户破裂通过改变车厢的通风来影响火灾的发展。当火灾发生在车厢中部时,热释放速率和燃烧强度都比火源位于车厢两端时大。火灾释放的高温会导致窗户破损,增加通风口面积,进而加剧车厢内可燃物的燃烧。通风是车厢火灾演变的一个关键因素,引入通风系数变化率来描述热释放率和窗户破裂之间的关系。因此,应尽可能避免车厢与外界的空气流通,并加强车厢内的火灾探测和报警,以提高灭火和应急救援能力。 展开更多
关键词 车厢火灾 车窗破损 火源位置 通风系数
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Multimodal Fraudulent Website Identification Method Based on Heterogeneous Model Ensemble
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作者 Shengli Zhou Linqi Ruan +1 位作者 Qingyang Xu Mincheng Chen 《China Communications》 SCIE CSCD 2023年第5期263-274,共12页
The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimensi... The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimension and venerability to anti-reconnaissance,this paper adopts the Stacking,the ensemble learning algorithm,combines multiple modalities such as text,image and URL,and proposes a multimodal fraudulent website identification method by ensembling heterogeneous models.Crossvalidation is first used in the training of multiple largely different base classifiers that are strong in learning,such as BERT model,residual neural network(ResNet)and logistic regression model.Classification of the text,image and URL features are then performed respectively.The results of the base classifiers are taken as the input of the meta-classifier,and the output of which is eventually used as the final identification.The study indicates that the fusion method is more effective in identifying fraudulent websites than the single-modal method,and the recall is increased by at least 1%.In addition,the deployment of the algorithm to the real Internet environment shows the improvement of the identification accuracy by at least 1.9%compared with other fusion methods. 展开更多
关键词 telecom fraud crime fraudulent website data fusion deep learning
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Textual Content Prediction via Fuzzy Attention Neural Network Model without Predefined Knowledge
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作者 Canghong Jin Guangjie Zhang +2 位作者 Minghui Wu Shengli Zhou Taotao Fu 《China Communications》 SCIE CSCD 2020年第6期211-222,共12页
Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there... Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user. 展开更多
关键词 judgment content understanding pre-trained model FUZZIFICATION content representation vectors
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Electrochemical detection of hydrogen peroxide at a waxed graphite electrode modified with platinum-decorated carbon nanotubes
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作者 时巧翠 曾文芳 朱玉奴 《Journal of Shanghai University(English Edition)》 CAS 2009年第1期63-66,共4页
Platinum-decorated carbon nanotubes (CNT-Pt) were produced by the chemical reduction method. A novel modified electrode was fabricated by intercalated CNT-Pt in the surface of waxed graphite, which provided excellen... Platinum-decorated carbon nanotubes (CNT-Pt) were produced by the chemical reduction method. A novel modified electrode was fabricated by intercalated CNT-Pt in the surface of waxed graphite, which provided excellent electrocatalytic activity and selectivity for both oxidation and reduction of hydrogen peroxide. The current response of the modified electrode for hydrogen peroxide was very rapid and the detection limits in amperometry are 2.5×10^-6 mol/L at reduction potential and 4.8×10^-6 mol/L at oxidation potential. It was desmonstrated that the electrode with high electro-activity was a suitable basic electrode for preparing enzyme electrode. 展开更多
关键词 platinum-decorated carbon nanotubes (CNT-Pt) hydrogen peroxide ELECTROANALYSIS
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A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network
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作者 Jianfeng Lu Xinyi Liu +2 位作者 Mengtao Shi Chen Cui Mahmoud Emam 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期2865-2882,共18页
Ceramic tiles are one of the most indispensable materials for interior decoration.The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures.In this... Ceramic tiles are one of the most indispensable materials for interior decoration.The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures.In this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile textures.The proposed method consists of four steps.Firstly,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.Secondly,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated sketches.Thirdly,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training speed.Finally,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images dataset.To measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)metrics.The experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44. 展开更多
关键词 Ceramic tile pattern design cross-domain learning deep learning GAN generative adversarial networks ResNet Block
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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection
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作者 Jie Chen Chengsheng Yuan +3 位作者 Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan 《Computers, Materials & Continua》 SCIE EI 2022年第10期719-733,共15页
Fingerprint identification systems have been widely deployed in many occasions of our daily life.However,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit finger... Fingerprint identification systems have been widely deployed in many occasions of our daily life.However,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit fingerprints.To address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s attention.The vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model generalization.Aiming at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint detection.Next,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection model.Finally,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and cross-sensor.The size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available. 展开更多
关键词 FLD LIGHTWEIGHT MULTI-SCALE RSC blind materials
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HPLC-MS/MS Determination of Oleandrin and Adynerin in Blood with Solid Phase Supported Liquid-Liquid Extraction
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作者 Jianbo YING Fanglin WANG +1 位作者 Yujing LUAN Weixuan YAO 《Medicinal Plant》 CAS 2018年第3期5-8,共4页
[Objectives]To optimize the determination method of oleandrin and adynerin in blood. [Methods]High performance liquid chromatography-mass spectrometry( HPLC-MS/MS) was applied to determine oleandrin and adynerin in bl... [Objectives]To optimize the determination method of oleandrin and adynerin in blood. [Methods]High performance liquid chromatography-mass spectrometry( HPLC-MS/MS) was applied to determine oleandrin and adynerin in blood. The blood sample was dispersed and fixed on a solid phase supported liquid-liquid extraction column and eluted with ethyl acetate. The resulting eluent was used for chromatographic separation with Kinetex C_(18) column as the separation column and gradient elution was performed using 10 mmol/L ammonium formate solution containing 0. 1%( volume fraction) formic acid and acetonitrile as the mobile phase. In the tandem mass spectrometry analysis,the detection was carried out using the electrospray positive ion source multiple reaction monitoring mode. [Results] The mass concentration of oleandrin and adynerin showed linear relationship in the range of 2-100 μg/L. The limit of detection( 3 S/N) of the method was 0. 5 μg/L.A blank sample was used as the substrate for the spike recovery test. The recovery rate was in the range of 90. 0%-98. 0%,and the relative standard deviation( RSD) of the measured values( n = 6) was in the range of 2. 1%-7. 3%. [Conclusions]The method established in this experiment has the benefits of simple pretreatment,good recovery,high sensitivity and strong specificity,and is expected to provide an ideal method for the determination of such drugs in blood. 展开更多
关键词 High performance liquid chromatography-mass spectrometry(HPLC-MS/MS) BLOOD Oleandrin Adynerin
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An alert-situation text data augmentation method based on MLM
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作者 DING Weijie MAO Tingyun +3 位作者 CHEN Lili ZHOU Mingwei YUAN Ying HU Wentao 《High Technology Letters》 EI CAS 2024年第4期389-396,共8页
The performance of deep learning models is heavily reliant on the quality and quantity of train-ing data.Insufficient training data will lead to overfitting.However,in the task of alert-situation text classification,i... The performance of deep learning models is heavily reliant on the quality and quantity of train-ing data.Insufficient training data will lead to overfitting.However,in the task of alert-situation text classification,it is usually difficult to obtain a large amount of training data.This paper proposes a text data augmentation method based on masked language model(MLM),aiming to enhance the generalization capability of deep learning models by expanding the training data.The method em-ploys a Mask strategy to randomly conceal words in the text,effectively leveraging contextual infor-mation to predict and replace masked words based on MLM,thereby generating new training data.Three Mask strategies of character level,word level and N-gram are designed,and the performance of each Mask strategy under different Mask ratios is analyzed and studied.The experimental results show that the performance of the word-level Mask strategy is better than the traditional data augmen-tation method. 展开更多
关键词 deep learning text data augmentation masked language model(MLM) alert-sit-uation text classification
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Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter 被引量:6
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作者 Sheng JIN Dian-hai WANG +1 位作者 Cheng XU Dong-fang MA 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2013年第4期231-243,共13页
In this paper,a prediction model is developed that combines a Gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision(TTC) samples are divided into... In this paper,a prediction model is developed that combines a Gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision(TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood(ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization(EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts(PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems. 展开更多
关键词 Forecasting Traffic safety Gaussian mixture model Kalman filter
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NERank+: a graph-based approach for entity ranking in document collections 被引量:1
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作者 Chengyu WANG Guomin ZHOU +1 位作者 Xiaofeng HE Aoying ZHOU 《Frontiers of Computer Science》 SCIE EI CSCD 2018年第3期504-517,共14页
Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. Howev... Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. However, entities can be ranked directly based on their relative importance in a document collection, independent of any queries. In this paper, we introduce an entity ranking algorithm named NERank+. Given a document collection, NERank+ first constructs a graph model called Topical Tripartite Graph, consisting of document, topic and entity nodes. We design separate ranking functions to compute the prior ranks of entities and topics, respectively. A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph model. We evaluate NERank+ over real-life datasets and compare it with baselines. Experimental results illustrate the effectiveness of our approach. 展开更多
关键词 entity ranking Topical Tripartite Graph priorrank estimation meta-path constrained random walk
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A numerical study on smoke behaviors in inclined tunnel fires under natural ventilation 被引量:1
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作者 Jie Kong Wenjiao You +2 位作者 Zhisheng Xu Hui Liu Haihang Li 《Journal of Safety Science and Resilience》 CSCD 2022年第2期169-178,共10页
To investigate the effect of tunnel slope on hot gas movement and smoke distribution in a slopping tunnel fire,a series of tunnel fire models are built by fire dynamics simulator(FDS),with a slope varies from 0 to 10%... To investigate the effect of tunnel slope on hot gas movement and smoke distribution in a slopping tunnel fire,a series of tunnel fire models are built by fire dynamics simulator(FDS),with a slope varies from 0 to 10%.Parameters such as ceiling temperature and airflow velocity are measured.The results indicate that the relationship between smoke back-layering length and tunnel slope can be described as an exponential function.The smoke temperature at the downstream exit first increased and then decreased with a higher slope.The airflow velocity at downstream outlet increased nonlinearity when tunnel slope was less than 8%.In the slope tunnel,the fire smoke spread process can be divided into three stages.Fire smoke spreads upstream to the peak distance,subsequently,the upstream smoke layer decreases gradually,the tunnel fire reaches a quasi-steady state.The backflow characteristics of smoke in sloped tunnels are coupled with the downstream length and outlet smoke temperature.In the initial stage of a slope tunnel fire,smoke spreads upstream for a long distance,endangering human health. 展开更多
关键词 Tunnel fire SLOPE Stack effect Smoke movement Back-layering length
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