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Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach 认领
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作者 Raogo Kabore Adlès Kouassi +3 位作者 Rodrigue N’goran Olivier Asseu Yvon Kermarrec Philippe Lenca 《工程(英文)(1947-3931)》 2021年第1期30-44,共15页
Industrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-attacks as their architectures shifted from proprietary hardware, software and protocols to standard and open sources ones. Further... Industrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-attacks as their architectures shifted from proprietary hardware, software and protocols to standard and open sources ones. Furthermore, these systems which used to be isolated are now interconnected to corporate networks and to the Internet. Among the countermeasures to mitigate the threats, anomaly detection systems play an important role as they can help detect even unknown attacks. Deep learning which has gained a great attention in the last few years due to excellent results in image, video and natural language processing is being used for anomaly detection in information security, particularly in SCADA networks. The salient features of the data from SCADA networks are learnt as hierarchical representation using deep architectures, and those learnt features are used to classify the data into normal or anomalous ones. This article is a review of various architectures such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Stacked Autoencoder (SAE), Long Short Term Memory (LSTM), or a combination of those architectures, for anomaly detection purpose in SCADA networks. 展开更多
关键词 ICS SCADA Unsupervised Feature Learning Deep Learning Anomaly Detection
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Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network 认领
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作者 Shiming Liu Yifan Xia +3 位作者 Zhusheng Shi Hui Yu Zhiqiang Li Jianguo Lin 《自动化学报:英文版》 SCIE EI 2021年第3期565-581,共17页
Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components.To surmount the springback occurring in sheet metal forming processes,numerous ... Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components.To surmount the springback occurring in sheet metal forming processes,numerous studies have been performed to develop compensation methods.However,for most existing methods,the development cycle is still considerably time-consumptive and demands high computational or capital cost.In this paper,a novel theory-guided regularization method for training of deep neural networks(DNNs),implanted in a learning system,is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter,e.g.,loading stroke,in sheet metal bending processes.By directly bridging the workpiece shape to the process parameter,issues concerning springback in the process design would be circumvented.The novel regularization method utilizes the well-recognized theories in material mechanics,Swift’s law,by penalizing divergence from this law throughout the network training process.The regularization is implemented by a multi-task learning network architecture,with the learning of extra tasks regularized during training.The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base,respectively.One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface.In this research,the neural models were found to outperform a traditional machine learning model,support vector regression model,in experiments with different amount of training data.Through a series of studies with varying conditions of training data structure and amount,workpiece material and applied bending processes,the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs,especially when only scarce and scattered experiment data are available for training which is often the c 展开更多
关键词 Data-driven deep learning deep learning deep neural network(DNN) intelligent manufacturing machine learning sheet metal forming SPRINGBACK theory-guided deep learning theoryguided regularization
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教育,如何成为他自己?——从Learning to be说起 认领
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作者 邓璐 《阿坝师范学院学报》 2021年第1期107-113,共7页
《学会生存——教育世界的今天和明天》和《教育——财富蕴藏其中》是20世纪后半叶国际社会的两个重要的教育报告,对二战后世界发展新格局背景下教育、社会、人三者的关系进行了讨论,其核心就是教育应该如何发展的问题。《学会生存》的... 《学会生存——教育世界的今天和明天》和《教育——财富蕴藏其中》是20世纪后半叶国际社会的两个重要的教育报告,对二战后世界发展新格局背景下教育、社会、人三者的关系进行了讨论,其核心就是教育应该如何发展的问题。《学会生存》的英文标题Learning to Be其本质含义就是认识自己、成为自己,而其主语包括国家、教育、人(个体)。Learning to be就是要通过“教育如何成为他自己”来解决“国家如何成为他自己”“个体如何成为他自己”的问题。在教育改革的宏观层面,要遵循教育自身的发展规律,处理好教育与社会(国家)的关系,让教育的力量弥散到整个社会而不仅仅是作为一个社会子系统,即建设学习型社会;在教育改革的微观层面,要杜绝完全的“拿来主义”,体现教育中的文化自信,重视来自教育系统内部的革新力量。 展开更多
关键词 Learning to be 教育改革 学习型社会 文化自信 教育与社会的关系 教育与人的发展
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Knowing Your Dog Breed:Identifying a Dog Breed with Deep Learning 认领
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作者 Punyanuch Borwarnginn Worapan Kusakunniran +1 位作者 Sarattha Karnjanapreechakorn Kittikhun Thongkanchorn 《国际自动化与计算杂志:英文版》 EI 2021年第1期45-54,共10页
Dog breed identification is essential for many reasons,particularly for understanding individual breeds′conditions,health concerns,interaction behavior,and natural instinct.This paper presents a solution for identify... Dog breed identification is essential for many reasons,particularly for understanding individual breeds′conditions,health concerns,interaction behavior,and natural instinct.This paper presents a solution for identifying dog breeds using their images of their faces.The proposed method applies a deep learning based approach in order to recognize their breeds.The method begins with a transfer learning by retraining existing pretrained convolutional neural networks(CNNs)on the public dog breed dataset.Then,the image augmentation with various settings is also applied on the training dataset,in order to improve the classification performance.The proposed method is evaluated using three different CNNs with various augmentation settings and comprehensive experimental comparisons.The proposed model achieves a promising accuracy of 89.92%on the published dataset with 133 dog breeds. 展开更多
关键词 Computer vision deep learning dog breed classification transfer learning image augmentation
Machine intelligence for precision oncology 认领
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作者 Nelson S Yee 《世界转化医学杂志》 2021年第1期1-10,共10页
Despite various advances in cancer research,the incidence and mortality rates of malignant diseases have remained high.Accurate risk assessment,prevention,detection,and treatment of cancer tailored to the individual a... Despite various advances in cancer research,the incidence and mortality rates of malignant diseases have remained high.Accurate risk assessment,prevention,detection,and treatment of cancer tailored to the individual are major challenges in clinical oncology.Artificial intelligence(AI),a field of applied computer science,has shown promising potential of accelerating evolution of healthcare towards precision oncology.This article focuses on highlights of the application of data-driven machine learning(ML)and deep learning(DL)in translational research for cancer diagnosis,prognosis,treatment,and clinical outcomes.MLbased algorithms in radiological and histological images have been demonstrated to improve detection and diagnosis of cancer.DL-based prediction models in molecular or multi-omics datasets of cancer for biomarkers and targets enable drug discovery and treatment.ML approaches combining radiomics with genomics and other omics data enhance the power of AI in improving diagnosis,prognostication,and treatment of cancer.Ethical and regulatory issues involving patient confidentiality and data security impose certain limitations on practical implementation of ML in clinical oncology.However,the ultimate goal of application of AI in cancer research is to develop and implement multi-modal machine intelligence for improving clinical decision on individualized management of patients. 展开更多
关键词 Artificial intelligence Deep learning Machine learning Precision oncology Radiomics Radiogenomics
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Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms:sourced from retrospective studies 认领
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作者 Yu Lei Xin Zhang +7 位作者 Wei Ni Heng Yang Jia-Bin Su Bin Xu Liang Chen Jin-Hua Yu Yu-Xiang Gu Ying Mao 《中国神经再生研究:英文版》 SCIE CAS 2021年第5期830-835,共6页
Although intracranial hemorrhage in moyamoya disease can occur repeatedly,predicting the disease is difficult.Deep learning algorithms developed in recent years provide a new angle for identifying hidden risk factors,... Although intracranial hemorrhage in moyamoya disease can occur repeatedly,predicting the disease is difficult.Deep learning algorithms developed in recent years provide a new angle for identifying hidden risk factors,evaluating the weight of different factors,and quantitatively evaluating the risk of intracranial hemorrhage in moyamoya disease.To investigate whether convolutional neural network algorithms can be used to recognize moyamoya disease and predict hemorrhagic episodes,we retrospectively selected 460 adult unilateral hemispheres with moyamoya vasculopathy as positive samples for diagnosis modeling,including 418 hemispheres with moyamoya disease and 42 hemispheres with moyamoya syndromes.Another 500 hemispheres with normal vessel appearance were selected as negative samples.We used deep residual neural network(ResNet-152)algorithms to extract features from raw data obtained from digital subtraction angiography of the internal carotid artery,then trained and validated the model.The accuracy,sensitivity,and specificity of the model in identifying unilateral moyamoya vasculopathy were 97.64±0.87%,96.55±3.44%,and 98.29±0.98%,respectively.The area under the receiver operating characteristic curve was 0.990.We used a combined multi-view conventional neural network algorithm to integrate age,sex,and hemorrhagic factors with features of the digital subtraction angiography.The accuracy of the model in predicting unilateral hemorrhagic risk was 90.69±1.58%and the sensitivity and specificity were 94.12±2.75%and 89.86±3.64%,respectively.The deep learning algorithms we proposed were valuable and might assist in the automatic diagnosis of moyamoya disease and timely recognition of the risk for re-hemorrhage.This study was approved by the Institutional Review Board of Huashan Hospital,Fudan University,China(approved No.2014-278)on January 12,2015. 展开更多
关键词 brain central nervous system deep learning diagnosis HEMORRHAGE machine learning moyamoya disease moyamoya syndrome prediction REBLEEDING
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思维导图在高中英语语法教学中的应用研究 认领
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作者 李瑾瑾 《成才之路》 2021年第5期48-49,共2页
思维导图作为一种可视化的教学辅助工具,能够加深人们对信息的理解和记忆。在高中英语语法知识教学中,教师可以根据学生的学情有计划地运用思维导图,帮助学生更好地学习语法知识。文章结合教学实践,对思维导图如何有效应用于语法学习阶... 思维导图作为一种可视化的教学辅助工具,能够加深人们对信息的理解和记忆。在高中英语语法知识教学中,教师可以根据学生的学情有计划地运用思维导图,帮助学生更好地学习语法知识。文章结合教学实践,对思维导图如何有效应用于语法学习阶段、练习阶段与复习阶段分别进行探讨,以提高学生学习英语语法知识的效率。 展开更多
关键词 思维导图 高中英语 语法教学 学习 练习 复习 阶段 学习效率
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Landslide identification using machine learning 认领
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作者 Haojie Wang Limin Zhang +2 位作者 Kesheng Yin Hongyu Luo Jinhui Li 《地学前缘:英文版》 SCIE CAS 2021年第1期351-364,共14页
Landslide identification is critical for risk assessment and mitigation.This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases.First,l... Landslide identification is critical for risk assessment and mitigation.This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases.First,landslide-related data are compiled,including topographic data,geological data and rainfall-related data.Then,three integrated geodatabases are established;namely,Recent Landslide Database(Rec LD),Relict Landslide Database(Rel LD)and Joint Landslide Database(JLD).After that,five machine learning and deep learning algorithms,including logistic regression(LR),support vector machine(SVM),random forest(RF),boosting methods and convolutional neural network(CNN),are utilized and evaluated on each database.A case study in Lantau,Hong Kong,is conducted to demonstrate the application of the proposed method.From the results of the case study,CNN achieves an identification accuracy of 92.5%on Rec LD,and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing.Boosting methods come second in terms of accuracy,followed by RF,LR and SVM.By using machine learning and deep learning techniques,the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem. 展开更多
关键词 Landslide risk Landslide identification Machine learning Deep learning Big data Convolutional neural networks
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Absence of Ketamine Effects on Learning &Memory Following Exposure during Early Adolescence: A Preliminary Report 认领
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作者 Shannon O’Brien David Compton +2 位作者 Julianna M. Davis Jennifer Elvir Adrien Albritton 《行为与脑科学期刊(英文)》 2021年第1期27-47,共21页
Traditionally, ketamine was considered useful as a dissociative anesthetic. More recently, ketamine has been examined for its effects as a fast-acting antidepressant, for treatment-resistant depression, and as a non-o... Traditionally, ketamine was considered useful as a dissociative anesthetic. More recently, ketamine has been examined for its effects as a fast-acting antidepressant, for treatment-resistant depression, and as a non-opiate treatment of chronic pain. Unfortunately, ketamine has enjoyed popularity as a recreational drug among both adolescents and young adults. While some research suggests the use of this drug during neurodevelopment is not without consequence, relatively little work has been conducted to examine the chronic effects of ketamine on the adolescent brain at different stages of neural development. Using a rodent model of development, we probed the effects of early adolescent exposure to ketamine. Between postnatal days 22 to 40, a period comprising early to mid-adolescence, rats were exposed to one of two doses of ketamine or saline. Beginning at 90 days of age and drug free for 50 days, a series of neuropsychological assessments were employed to examine general activity, spatial navigation, as well as nonspatial response learning. Contrary to prediction, except for differences in general activity levels, no spatial or nonspatial impairments were found among the drug- and saline-treated animals. The present results are considered in light of ketamine-associated effects found in a related study with older adolescent rats and the role of drug exposure during different points in adolescent brain development. 展开更多
关键词 KETAMINE Neuropsychological Assessment Learning MEMORY Rat
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Comparison of Spatio-Spectral Properties of Zen-Meditation and Resting EEG Based on Unsupervised Learning 认领
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作者 Pei-Chen Lo Nasir Hussain 《行为与脑科学期刊(英文)》 2021年第2期58-72,共15页
This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of... This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a <i>detached</i> brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce <i>beta buzz</i>. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation. 展开更多
关键词 Electroencephalograph (EEG) Continuous Wavelet Transform (CWT) Unsupervised Learning Self-Organizing Map (SOM) Spatio-Spectral Property Zen Meditation
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Ultrasonographic Segmentation of Fetal Lung with Deep Learning 认领
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作者 Jintao Yin Jiawei Li +6 位作者 Qinghua Huang Yucheng Cao Xiaoqian Duan Bing Lu Xuedong Deng Qingli Li Jiangang Chen 《生物科学与医学(英文)》 2021年第1期146-153,共8页
<div style="text-align:justify;"> The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. Th... <div style="text-align:justify;"> The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively. </div> 展开更多
关键词 Fetal Lung Fetal Heart Ultrasound Image SEGMENTATION Deep Learning
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Lightweight FaceNet Based on MobileNet 认领
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作者 Xinzheng Xu Meng Du +2 位作者 Huanxiu Guo Jianying Chang Xiaoyang Zhao 《智能科学国际期刊(英文)》 2021年第1期1-16,共16页
Face recognition is a kind of biometric technology that recognizes identities through human faces. At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition.... Face recognition is a kind of biometric technology that recognizes identities through human faces. At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition. With the rapid development of deep learning and the application of Convolutional Neural Network (CNN) in the field of face recognition, the accuracy of face recognition has greatly improved. FaceNet is a deep learning framework commo</span><span><span style="font-family:Verdana;">nly used in face recognition in recent years. FaceNet uses the deep learning model GoogLeNet, which has </span><span style="font-family:Verdana;">a high</span><span style="font-family:Verdana;"> accuracy in face recognition. However, its network structure is too large, which causes the </span><span style="font-family:Verdana;">FaceNet</span><span style="font-family:Verdana;"> to run at a low speed. Therefore, to improve the running speed without affecting the recognition accuracy of FaceNet, this paper proposes a lightweight FaceNet model based on MobileNet. This article mainly does the following works:</span></span></span><span style="font-family:""> </span><span style="font-family:Verdana;">Based on the analysis of the low running speed of FaceNet and the principle of MobileNet, a lightweight FaceNet model based on MobileNet is proposed. The model would reduce the overall calculation of the network by using deep separable convolutio</span><span style="font-family:""><span style="font-family:Verdana;">ns. In this paper, the model is trained on the CASIA-WebFace and VGGFace2 </span><span style="font-family:Verdana;">datasets,</span><span style="font-family:Verdana;"> and tested on the LFW dataset. Experimental results show that the model reduces the network parameters to a large extent while ensuring </span><span style="font-family:Verdana;">the accuracy</span><span style="font-family:Verdana;"> and hence an increase in system computing speed. The model can also perform face recognition on a specific person in the video. 展开更多
关键词 Face Recognition Deep Learning FaceNet MobileNet
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Research on Personal Credit Risk Assessment Model Based on Instance-Based Transfer Learning 认领
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作者 Maoguang Wang Hang Yang 《智能科学国际期刊(英文)》 2021年第1期44-55,共12页
Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and ... Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. <span style="font-family:Verdana;">In view of</span><span style="font-family:Verdana;"> these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, </span><span style="font-family:Verdana;">xgboost</span> <span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model;on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.</span> 展开更多
关键词 Personal Credit Risk Big Data Credit Investigation Instance-Based Transfer Learning
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On Theoretical Practice of Cooperative Learning Teaching Approach 认领
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作者 Hongping Wang 《教育理论综述(英文)》 2021年第1期33-35,共3页
Cooperative learning is a kind of teaching theory and strategy initiated by new curriculum reform as well as a new learning style proposed by new curriculum standard.In recent years,with the constant deepening of educ... Cooperative learning is a kind of teaching theory and strategy initiated by new curriculum reform as well as a new learning style proposed by new curriculum standard.In recent years,with the constant deepening of educational reform,cooperative learning teaching approach has aroused more and more attention.This study introduces cooperative learning teaching approach from three aspects:the definition,history and methods,in order to understand the deep meaning of theoretical practice of cooperative learning teaching approach. 展开更多
关键词 Cooperative learning Theoretical practice Learning together
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Maneuvering target tracking of UAV based on MN-DDPG and transfer learning 认领
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作者 Bo Li Zhi-peng Yang +2 位作者 Da-qing Chen Shi-yang Liang Hao Ma 《Defence Technology(防务技术)》 SCIE EI CAS 2021年第2期457-466,共10页
Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control proble... Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of taskdecomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments. 展开更多
关键词 UAVS Maneuvering target tracking Deep reinforcement learning MN-DDPG Mixed noises Transfer learning
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The Overview of Database Security Threats’ Solutions: Traditional and Machine Learning 认领
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作者 Yong Wang Jinsong Xi Tong Cheng 《信息安全(英文)》 2021年第1期34-55,共22页
As an information-rich collective, there are always some people who choose to take risks for some ulterior purpose and others are committed to finding ways to deal with database security threats. The purpose of databa... As an information-rich collective, there are always some people who choose to take risks for some ulterior purpose and others are committed to finding ways to deal with database security threats. The purpose of database security research is to prevent the database from being illegally used or destroyed. This paper introduces the main literature in the field of database security research in recent years. First of all, we classify these papers, the classification criteria </span><span style="font-size:12px;font-family:Verdana;">are</span><span style="font-size:12px;font-family:Verdana;"> the influencing factors of database security. Compared with the traditional and machine learning (ML) methods, some explanations of concepts are interspersed to make these methods easier to understand. Secondly, we find that the related research has achieved some gratifying results, but there are also some shortcomings, such as weak generalization, deviation from reality. Then, possible future work in this research is proposed. Finally, we summarize the main contribution. 展开更多
关键词 Database Security Threat Agent Traditional Approaches Machine Learning
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Constrained voting extreme learning machine and its application 认领
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作者 MIN Mengcan CHEN Xiaofang XIE Yongfang 《系统工程与电子技术:英文版》 SCIE EI 2021年第1期209-219,共11页
Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.Wit... Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods. 展开更多
关键词 extreme learning machine(ELM) majority voting ensemble method sample based learning superheat degree(SD)
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Strategies on Promoting Transformative Learning of College English Teachers 认领
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作者 Wenbo Zhao 《当代教育研究(百图)》 2021年第1期53-56,共4页
Based on the previous case study in promoting transformative learning of college English teachers,who participated in a three-month online training courses,the article made a further research and concluded four strate... Based on the previous case study in promoting transformative learning of college English teachers,who participated in a three-month online training courses,the article made a further research and concluded four strategies on promoting transformative learning of college English Teachers. 展开更多
关键词 Transformative learning Teacher learning Mesirow’s theoretical model STRATEGIES
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English Learning under the Context of Globalization 认领
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作者 杨柳枫 熊伟 《海外英语》 2021年第2期275-276,共2页
In contemporary,globalization is advancing at an unprecedented rate in multitude arenas.Globalization has brought us to contact with the culture,customs and thinking of countries around the world.English learning unde... In contemporary,globalization is advancing at an unprecedented rate in multitude arenas.Globalization has brought us to contact with the culture,customs and thinking of countries around the world.English learning under the context of globalization has been changed to some extent.Globalization is exuberant,specific learning instead of systematic learning is what is necessitated. 展开更多
关键词 GLOBALIZATION English learning learning methods
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Physics-Aware Deep Learning on Multiphase Flow Problems 认领
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作者 Zipeng Lin 《通讯与网络(英文)》 2021年第1期1-11,共11页
In this article, a physics aware deep learning model is introduced for multiphase flow problems. The deep learning model is shown to be capable of capturing complex physics phenomena such as saturation front, which is... In this article, a physics aware deep learning model is introduced for multiphase flow problems. The deep learning model is shown to be capable of capturing complex physics phenomena such as saturation front, which is even challenging for numerical solvers due to the instability. We display the preciseness of the solution domain delivered by deep learning models and the low cost of deploying this model for complex physics problems, showing the versatile character of this method and bringing it to new areas. This will require more allocation points and more careful design of the deep learning model architectures and residual neural network can be a potential candidate. 展开更多
关键词 Deep Learning Neural Network MULTI-PHASE Oil Incompressible Fluid Physics Partial Differential Equation
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