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论情境教学在小学数学算理学习中的有效应用
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作者 符辰 《数学学习与研究》 2020年第19期150-151,共2页
目前,小学数学运算教学中对算理的学习非常重视,而情境教学在算理教学中颇具意义.笔者通过大量的课堂观摩,结合相关教学理论,对情境教学在算理学习中的应用展开分析,指出小学数学算理教学中存在的问题,并提出立足课标、创设学生需要的... 目前,小学数学运算教学中对算理的学习非常重视,而情境教学在算理教学中颇具意义.笔者通过大量的课堂观摩,结合相关教学理论,对情境教学在算理学习中的应用展开分析,指出小学数学算理教学中存在的问题,并提出立足课标、创设学生需要的真实情境、在情境教学中要保证学生的有效参与、关注高阶认知培养、将情境教学应用在单元学习中和通过持续性评价引领情境教学的展开,对如何促进情境教学进行深入思考. 展开更多
关键词 小学数学 情境教学 算理学习
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小学数学计算算理教学刍议
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作者 薛丽香 《福建教育学院学报》 2015年第11期74-75,125,共3页
在小学数学教学中,计算教学直接关系到学生对基础性目标的掌握。为了能有效提高学生的计算能力,文章结合课堂教学实践提出:改进教师的教学,主要有借助新旧知识的关系,在学生的动手操作中,算法多样化与最优化的结合,多种计算方法使用结合... 在小学数学教学中,计算教学直接关系到学生对基础性目标的掌握。为了能有效提高学生的计算能力,文章结合课堂教学实践提出:改进教师的教学,主要有借助新旧知识的关系,在学生的动手操作中,算法多样化与最优化的结合,多种计算方法使用结合,灵活应用促进学生对计算算理的学习;完善学生的学习,主要让学生多讲,针对错题多分析,多练促进学生对计算算理的学习。 展开更多
关键词 小学 教学 算理学习 促进
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小学数学教学中提高学生计算正确率的有效措施
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作者 黄凤鲜 《电脑乐园》 2021年第2期0089-0089,共1页
计算是数学学习中最为基础的学习内容之一,如果学生基础计算的正确率都得不到保证,那么更为复杂的应用题计算正确率就更加没有保障,如此一来学生的数学成绩就会直线下滑。教师需要学生重视计算能力的培养,因为在后续的数学学习中仍然需... 计算是数学学习中最为基础的学习内容之一,如果学生基础计算的正确率都得不到保证,那么更为复杂的应用题计算正确率就更加没有保障,如此一来学生的数学成绩就会直线下滑。教师需要学生重视计算能力的培养,因为在后续的数学学习中仍然需要学生拥有一定的数学计算能力,所以从现在开始教师就要着手准备提高学生计算能力的相关措施,保障学生计算正确率得到提高。 展开更多
关键词 小学数学 正确率 增加练习质量 算理学习 长期坚持 进行检验
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A new decision tree learning algorithm 被引量:3
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作者 方勇 戚飞虎 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第6期684-689,共6页
In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decisi... In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decision trees is analyzed, and the assessment rule is proposed. Under the direction of the assessment rule, the MMDT algorithm is implemented. The algorithm maps training examples from an original space to a high dimension feature space, and constructs a decision tree in it. In the feature space, a new decision node splitting criterion, the max-min rule, is used, and the margin of each decision node is maximized using a support vector machine, to improve the generalization performance. Experimental results show that the new learning algorithm is much superior to others such as C4. 5 and OCI. 展开更多
关键词 machine learning decision tree statistical learning theory splitting criteria
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WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS 被引量:1
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作者 Liu Ting Lu Zhimao Li Sheng 《Journal of Electronics(China)》 2006年第3期394-398,共5页
Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in prac... Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%. 展开更多
关键词 Word Sense Disambiguation (WSD) Natural Language Processing (NLP) Unsupervised learning algorithm Dependency Grammar (DG) Bayesian classifier
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Opportunities and challenges for developing closed-loop bioelectronic medicines 被引量:1
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作者 Patrick D.Ganzer Gaurav Sharma 《Neural Regeneration Research》 SCIE CAS CSCD 2019年第1期46-50,共5页
The peripheral nervous system plays a major role in the maintenance of our physiology. Several peripheral nerves intimately regulate the state of the brain, spinal cord, and visceral systems. A new class of therapeuti... The peripheral nervous system plays a major role in the maintenance of our physiology. Several peripheral nerves intimately regulate the state of the brain, spinal cord, and visceral systems. A new class of therapeutics, called bioelectronic medicines, are being developed to precisely regulate physiology and treat dysfunction using peripheral nerve stimulation. In this review, we first discuss new work using closed-loop bioelectronic medicine to treat upper limb paralysis. In contrast to open-loop bioelectronic medicines, closed-loop approaches trigger ‘on demand' peripheral nerve stimulation due to a change in function(e.g., during an upper limb movement or a change in cardiopulmonary state). We also outline our perspective on timing rules for closedloop bioelectronic stimulation, interface features for non-invasively stimulating peripheral nerves, and machine learning algorithms to recognize disease events for closed-loop stimulation control. Although there will be several challenges for this emerging field, we look forward to future bioelectronic medicines that can autonomously sense changes in the body, to provide closed-loop peripheral nerve stimulation and treat disease. 展开更多
关键词 spinal cord injury STROKE PLASTICITY CLOSED-LOOP bioelectronic medicine machine learning nerve stimulation vagus nerve
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Spectrum sensing sequence prediction in cognitive radio networks
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作者 An Chunyan Ji Hong +1 位作者 Si Pengbo Maoxu 《High Technology Letters》 EI CAS 2011年第4期371-376,共6页
Spectrum sensing is one of the key issues in cognitive radio networks. Most of previous work concenates on sensing the spectrum in a single spectrum band. In this paper, we propose a spectrum sensing sequence predicti... Spectrum sensing is one of the key issues in cognitive radio networks. Most of previous work concenates on sensing the spectrum in a single spectrum band. In this paper, we propose a spectrum sensing sequence prediction scheme for cognitive radio networks with multiple spectrum bands to decrease the spectrum sensing time and increase the throughput of secondary users. The scheme is based on recent advances in computational learning theory, which has shown that prediction is synonymous with data compression. A Ziv-Lempel data compression algorithm is used to design our spectrum sensing sequence prediction scheme. The spectrum band usage history is used for the prediction in our proposed scheme. Simulation results show that the proposed scheme can reduce the average sensing time and improve the system throughput significantly. 展开更多
关键词 spectrum sensing sequence prediction cognitive radio network Ziv-Lempel algorithm
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Evidential Reasoning in Air Battle Systems
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作者 王欣 管纪文 +1 位作者 于晓 王钲旋 《Journal of Donghua University(English Edition)》 EI CAS 2007年第2期301-304,共4页
Bayesian statistics assigns basic probabilities to singletons (single element sets). The Dempster-Shafer evidence theory generalizes Bayesian statistics by assigning basic probabilities to subsets to represent evide... Bayesian statistics assigns basic probabilities to singletons (single element sets). The Dempster-Shafer evidence theory generalizes Bayesian statistics by assigning basic probabilities to subsets to represent evidence and to develop evidential reasoning. This paper discusses what is the strength of evidence theory. As an application of evidence theory, evidential reasoning in air battle systems is discussed. In the air battle system, evidential reasoning is applied to fuse the muitisensor iaformation and identify the type of aircraft. The effectiveness of this fusion approach is evaluated by simulated data. 展开更多
关键词 Dempster-Shafer theory evidential reasoning air battle system
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Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering
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作者 Abhrajit Ghosh Yitzchak M. Gottlieb +5 位作者 Aditya Naidu Akshay Vashist Alexander Poylisher Ayumu Kubota Yukiko Sawaya Akira Yamada 《China Communications》 SCIE CSCD 2013年第3期56-66,共11页
In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to hi... In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks. 展开更多
关键词 network security intrusion detection SCALING
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HGR 2.0: A New Rule Induction Algorithm
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作者 谌卫军 Lin +4 位作者 Fuzong Li Jianmin Zhang Bo 《High Technology Letters》 EI CAS 2003年第3期24-28,共5页
This paper presents a new inductive learning algorithm, HGR (Version 2.0), based on the newly-developed extension matrix theory. The basic idea is to partition the positive examples of a specific class in a given exam... This paper presents a new inductive learning algorithm, HGR (Version 2.0), based on the newly-developed extension matrix theory. The basic idea is to partition the positive examples of a specific class in a given example set into consistent groups, and each group corresponds to a consistent rule which covers all the examples in this group and none of the negative examples. Then a performance comparison of the HGR algorithm with other inductive algorithms, such as C4.5, OC1, HCV and SVM, is given in the paper. The authors not only selected 15 databases from the famous UCI machine learning repository, but also considered a real world problem. Experimental results show that their method achieves higher accuracy and fewer rules as compared with other algorithms. 展开更多
关键词 inductive learning algorithm machine learning extension matrix theory
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An Information Systems Project Management Course Using a Service-Learning Model
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作者 Randall McCoy Scott Wymer 《Computer Technology and Application》 2012年第5期335-341,共7页
This paper describes the implementation of an Information Systems (IS) capstone project management course that is a requirement for graduating seniors in an undergraduate Computer Information Systems (CIS) program... This paper describes the implementation of an Information Systems (IS) capstone project management course that is a requirement for graduating seniors in an undergraduate Computer Information Systems (CIS) program at a regional university. The description provides a model which includes the culmination of students' academic training in an IS curriculum which is part of a Bachelor of Business Administration (BBA) program in an accredited college of business. The course requires an application of technical and business skills, as well as systems development and project management skills--while students are working on an actual IS project for an external sponsoring organization. Rationale for implementing this type of course includes the benefits it provides to the students, the project sponsors, and the IS department providing the course. Feedback from the course is used as integral part of the C1S curriculum assessment process used for accreditation purposes. 展开更多
关键词 Information systems project management service-learning capstone course EVALUATION assessment.
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Research on Optimizing Computer Network Structure based on Genetic Algorithm and Modified Convex Optimization Theory
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作者 Jinyu WANG 《International Journal of Technology Management》 2015年第7期95-97,共3页
In this paper, we report in-depth analysis and research on the optimizing computer network structure based on genetic algorithm and modified convex optimization theory. Machine learning method has been widely used in ... In this paper, we report in-depth analysis and research on the optimizing computer network structure based on genetic algorithm and modified convex optimization theory. Machine learning method has been widely used in the background and one of its core problems is to solve the optimization problem. Unlike traditional batch algorithm, stochastic gradient descent algorithm in each iteration calculation, the optimization of a single sample point only losses could greatly reduce the memory overhead. The experiment illustrates the feasibility of our proposed approach. 展开更多
关键词 Computer Network Genetic Algorithm Convex Optimization Structure Feature.
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Artificial intelligence in drug design 被引量:14
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作者 Feisheng Zhong Jing Xing +13 位作者 Xutong Li Xiaohong Liu Zunyun Fu Zhaoping Xiong Dong Lu Xiaolong Wu Jihui Zhao Xiaoqin Tan Fei Li Xiaomin Luo Zhaojun Li Kaixian Chen Mingyue Zheng Hualiang Jiang 《Science China(Life Sciences)》 SCIE CAS CSCD 2018年第10期1191-1204,共14页
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage... Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design. 展开更多
关键词 drug design artificial intelligence deep learning QSAR ADME/T
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Seeing permeability from images: fast prediction with convolutional neural networks 被引量:11
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作者 Jinlong Wu Xiaolong Yin Heng Xiao 《Science Bulletin》 SCIE EI CSCD 2018年第18期1215-1222,共8页
Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) g... Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article presents a framework that includes (1) generation of porous media samples,(2) computation of permeability via fluid dynamics simulations,(3) training of convolutional neural networks (CNN) with simulated data, and (4) validations against simulations. Comparison of machine learning results and the ground truths suggests excellent predictive performance across a wide range of porosities and pore geometries, especially for those with dilated pores. Owning to such heterogeneity, the permeability cannot be estimated using the conventional Kozeny–Carman approach. Computational time was reduced by several orders of magnitude compared to fluid dynamic simulations. We found that, by including physical parameters that are known to affect permeability into the neural network, the physics-informed CNN generated better results than regular CNN. However, improvements vary with implemented heterogeneity. 展开更多
关键词 Porous media Convolutional neural network Machine learning PERMEABILITY Image processing
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A TRANSFER FORECASTING MODEL FOR CONTAINER THROUGHPUT GUIDED BY DISCRETE PSO 被引量:4
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作者 XIAO Jin XIAO Yi +1 位作者 FU Julei LAI Kin Keung 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2014年第1期181-192,共12页
Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete par... Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm (TF-DPSO). It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model. 展开更多
关键词 Analog complexing container throughput forecasting discrete particle swarm optimiza-tion transfer forecasting model.
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