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检索条件"机构=Fujian Provincial Key Laboratory of Data Intensive Computing"

共 138 条记录，以下是51-60 订阅

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An Analysis of Linear Algebra Teaching Problems and Solutions

An Analysis of Linear Algebra Teaching Problems and Solution...

引用

2015年人文学科和社会科学研究国际学术会议

作者： Jianming Liu College of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou Normal University

The paper aims at examining real mathematics teaching situations existing in local universities and colleges and exploring relative practical solutions to these problems. Based on a thorough study on mathematics teaching situations and related experts' research reports, the author's in-depth discussion with seasoned mathematics teachers and his own teaching experiences, the paper displays his advice on improving linear algebra teaching quality in higher educational system from three aspects: classroom teaching, exercise selection and explanation and after-class studying communication and guidance.

关键词： Mathematics teaching situations,Practical solutions,Linear algebra teaching quality

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An Interpretation of Information Technology Application in Mathematics Teaching

An Interpretation of Information Technology Application in M...

引用

2017第四届国际信息技术与就业教育大会

作者： Jianming Liu College of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou Normal University

With the increasing popularity of computers and fast development of computer technology, information technology has been widely used in teaching. Computer-assisted mathematics teaching can help create situations, improve teaching efficiency and make up for disadvantages of traditional teaching so as to make great achievements. When it comes to information technology application in mathematics teaching, the best combination between information technology and mathematics teaching should be made and corresponding teaching pattern should be found in order to improve the validity of information technology in mathematics teaching.

关键词： Information technology Teaching assistance Mathematics teaching

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Modeling and Simulation Experiment of Segway System

Modeling and Simulation Experiment of Segway System

引用

2017 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SMTA 2017)

作者： Jianming Liu College of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou Normal University

Based on Segway's movement diagram and Lagrangian equation, the author builds and linearizes a systematic mathematical model and dynamic structural diagram. And its dynamic situation is reflected through Matlab simulation. Moreover, with the pendulum angle as its inner loop, Segway displacement designs a PD control system for its outer loop as well as Matlab simulation. Due to its feedback control, the PD Control System acts as a powerful guarantee for Segway's balance in motion.

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A GA-Based Energy Aware Virtual Machine Placement Algorithm for Cloud data Centers

A GA-Based Energy Aware Virtual Machine Placement Algorithm ...

引用

International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Xiaodong Wu School of Mathmatics and Computer Science Quanzhou Normal University Quanzhou China Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou China

ISBN: (纸本)9781665496407

Virtual machine placement (VMP) is among the important techniques to improve the energy efficiency of green cloud data centers. To address this issue, bionic optimization algorithms have been widely used. One of the most effective methods is to use the genetic algorithm (GA) to find the best or near-optimal solution for the VMP problem. In this paper, a GGA-based algorithm is proposed to solve the energy-aware VMP problem. The algorithm regards the VMP problem as BPP and strives to reduce the number of active hosts in order to reduce the system energy consumption. Further, since it is important to find the solution of VMP in a short time especially in the online scenario, several optimization strategies are developed and applied in the proposed algorithm to improve the convergence speed of the algorithm and reduce the execution time of the algorithm. The experimental results demonstrate that compared with the traditional genetic algorithm, our method can effectively decrease the number of iterations and improve the speed of seeking optimal solutions.

关键词： data centers Cloud computing Energy consumption Heuristic algorithms Simulation Programming Virtual machining

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Echo State Network Based on L0 Norm Regularization for Chaotic Time Series Prediction 15th

Echo State Network Based on L0 Norm Regularization for Chaot...

引用

15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Li, Li Huang, Fangwan Yu, Zhiyong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

The echo state network introduces a large and sparse reservoir to replace the hidden layer of the traditional recurrent neural network, which can solve the gradient-based problem of most recurrent neural networks in the training process. However, there may be an ill-posed problem when the least square method is used to calculate the output weight. In this paper, we proposed an echo state network based on L0 norm regularization. The main idea is to limit the number of output connections to compute the output effectively by removing unimportant ones. The simulation results of the chaotic time series prediction show the effectiveness of the proposed model. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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An Improved Leaky-ESN for Electricity Load Forecasting 15th

An Improved Leaky-ESN for Electricity Load Forecasting

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Lin, Qiaoying Huang, Fangwan Yu, Zhiyong Li, Li College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

In the recent decade, recurrent neural networks become a hot research field, with the powerful capability to capture temporal information from time series. To avoid the vanishing/exploding gradient problem caused by the gradient descent algorithm involved in most recurrent networks, echo state network (ESN) was proposed to contain a large but sparse reservoir instead of traditional hidden layers. However, the performance of ESN is very sensitive to the parameters of the reservoir. In this paper, we focus on the improvements of ESN in the background of electricity load forecasting. With the goal of effectively and efficiently computation in the context of pervasive and cloud computing, two versions of adaptive echo state network (AESN) are designed to adopt a modular control strategy to automatically adjust some parameters of the reservoir. Applying AESN on two synthetic datasets and two real world electricity datasets, experimental results demonstrate that AESN is viable. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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The Design and Implementation of KMeans Based on Unified Batch and Streaming Processing 15th

The Design and Implementation of KMeans Based on Unified Bat...

引用

15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Chen, Hao Guo, Kun Chen, Yuzhong Guo, Hong College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

Clustering algorithms aim at gathering similar data points from a dataset in an unsupervised manner. Although the batch clustering algorithms have relatively high accuracy, they cannot make use of the dynamic clustering results efficiently. The requirement of using the whole dataset in calculation results in the problems of resource waste and high time cost. On the contrary, incremental clustering only needs to update the varied part of a model upon the arrival of new data, which makes it unnecessary to recluster the whole data all the time. The feature is very suitable for the streaming data process, but it decreases the accuracy of the algorithms and cannot satisfy the low latency requirement of real-time data processing. In response to this problem, the paper proposes a novel unified batch and streaming clustering model (UBSCM) based on streaming computation, which includes a streaming cluster feature updating mechanism (SCFUM). The Flink framework is used to implement a new streaming KMeans algorithm based on UBSCM (KMeansUBSP). The experiments on the real-world datasets validate that the new streaming KMeans algorithm is effective in clustering the batch and streaming data in a unified manner. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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An Improved Sparse Representation Classifier Based on data Augmentation for Time Series Classification 15th

An Improved Sparse Representation Classifier Based on Data A...

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Lu, Juhong Huang, Fangwan Yu, Zhiyong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

Sparse Representation-based Classification (SRC), which has achieved good performance in face recognition and other image classification, has been successfully extended to time series classification in recent years. As a generalization of the nearest subspace classifier, the performance of SRC depends on a rich set of training samples for each class, which can span as many variations of each class as possible under testing conditions. However, due to the difficulty of sample collection, many important applications can only provide a few or even a single sample for each class, which inevitably affects the performance of SRC. To address the problem of insufficient training samples, ESRC (Extended SRC) was proposed to put some samples that reflect the variations within the class into the dictionary, to obtain better generalization ability than SRC for under-sampled face recognition. In this paper, a new approach IESRC (Improved extend sparse representation based classification) is developed by splitting the dictionary into an original dictionary and auxiliary dictionary. The proposed model was first evaluated in 30 baseline data sets in the University of California, Riverside time series classification archive and then applied to classify the effects of aerobic exercise intervention in 24 young hypertensive patients based on the time series of the cardiopulmonary exercise test. Experimental results show that the proposed model can achieve better performance than SRC and ESRC in time series classification. © 2020, Springer Nature Switzerland AG.

关键词： Face recognition

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Using Deep Active Learning to Save Sensing Cost When Estimating Overall Air Quality 15th

Using Deep Active Learning to Save Sensing Cost When Estimat...

引用

15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Lei, Dehao Yu, Zhiyong Li, Peiguan Han, Lei Huang, Fangwan College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

Air quality is widely concerned by the governments and people. To save cost, air quality monitoring stations are deployed at only a few locations, and the stations are actuated at partial time. Therefore, it is necessary to study how to actively collect a subset of air quality data to maximize the estimation accuracy of air quality at other locations and time. In order to solve this challenge, we propose the active variational adversarial model (AVAM) that selects the most valuable unlabeled samples through two iterative phases of active learning. In the first phase of our model, a candidate set with unlabeled samples is selected through traditional active learning. In the second phase, variational auto-encoder (VAE) is used to obtain the compressed representation of the candidate set and the training set with labeled samples, then a discriminator based on three-layer neural network is trained from the compressed representation. Finally the discriminator can output the most valuable unlabeled samples from the candidate set. The experimental results show that the AVAM proposed in this paper is superior to active learning models with the first or second phase only. © 2020, Springer Nature Switzerland AG.

关键词： Air quality

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Electric Load Forecasting Based on Sparse Representation Model 13th

Electric Load Forecasting Based on Sparse Representation Mod...

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13th International Conference on Green, Pervasive, and Cloud computing, GPC 2018

作者： Huang, Fangwan Zheng, Xiangping Yu, Zhiyong Yang, Guanyi Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030150921

Accurate electric load forecasting can prevent the waste of power resources and plays a crucial role in smart grid. The time series of electric load collected by smart meters are non-linear and non-stationary, which poses a great challenge to the traditional forecasting methods. In this paper, sparse representation model (SRM) is proposed as a novel approach to tackle this challenge. The main idea of SRM is to obtain sparse representation coefficients by the training set and the part of over-complete dictionary, and the rest part of over-complete dictionary multiplied with sparse representation coefficients can be used to predict the future load value. Experimental results demonstrate that SRM is capable of forecasting the complex electric load time series effectively. It outperforms some popular machine learning methods such as Neural Network, SVM, and Random Forest. © 2019, Springer Nature Switzerland AG.

关键词： Electric load forecasting

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