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5th International Symposium on IT in Medicine and Education, ITME 2013

作者： Zhang, Yong Sheng Tian, Ming Lv, Shen Juan Zhang, Yan Dong School of Information Science and Engineering Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University Jinan 250014 China

ISBN: (纸本)9789400776173

This paper studies the characteristics of cloud computing environment, based on the traditional access control management technology, proposes a reference model for access control management in the cloud computing, designs a model based on trust grade of behaviors and role control, its basic thought is through entity behaviors, or the results which entity's behaviors produced, and integrated calculation processing generates the entity of credible value, with this as basis determines or changes entity of trust grade, as to change the entity of role and authority, to reach the purpose of supervision and protection about single entity or even the whole cloud system. © Springer Science+Business Media Dordrecht 2014.

关键词： Cloud computing

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Task assignment approach in a multi-agent system

Task assignment approach in a multi-agent system

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2012 IEEE 16th International Conference on computer Supported Cooperative Work in Design, CSCWD 2012

作者： Liu, Hong Sun, Yuling School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan City China

ISBN: (纸本)9781467312127

Task assignment problem is one of the important research topics in a multi-agent system. It is desired to assign each task to a suited agent with a minimum total cost. For the advantages of memory, multi-character, local search and the solution improvement mechanism in artificial bee colony (ABC) algorithm, a task assignment approach based on ABC in a multi-agent cooperative design system is proposed in this paper. Experimental results demonstrate that the optimal solutions obtained by the ABC algorithm are better than genetic algorithm and particle swarm optimization on solving some task assignment problems. © 2012 IEEE.

关键词： Multi agent systems

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Cloud computing and cloud security challenges

Cloud computing and cloud security challenges

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2012 International Symposium on Information Technologies in Medicine and Education, ITME 2012

作者： Zhang, Yandong Zhang, Yongsheng School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 250014 China

ISBN: (纸本)9781467321068

Cloud computing is setting off great changes in the IT industry. There are more and more researches on cloud computing. And this paper focuses on cloud computing too. At the beginning this paper describes the characteristics and definitions of cloud computing, and then introduced its services patterns (including SaaS, PaaS and IaaS) and deployment patterns(including public cloud, private cloud and hybrid cloud), at the end lists the cloud security challenges that cloud computing faces. © 2012 IEEE.

关键词： software as a service (SaaS)

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Deep Reinforcement Learning with Long-Time Memory Capability for Robot Mapless Navigation 25

Deep Reinforcement Learning with Long-Time Memory Capability...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Zhou, Qinglin Lyu, Lei Liu, Hong Shandong Normal University Jinan China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9781665405270

Achieving autonomous navigation of indoor robots in a mapless environment is a long-standing research problem. Deep Reinforcement Learning (DRL) is widely used for robot navigation by virtue of learning through interaction with the environment. However, the large number of trials for training need lengthy computation times. To address this issue, we propose an innovative DRL model with long-time memory capability for mobile robot's mapless navigation, which can achieve end-to-end navigation based only on laser-ranging data and target location. The long-time memory capability is realized by introducing a memory module based on the special structure of the Long Short-Term Memory (LSTM). The memory module ensures that the model derives information from previous navigation experiences and thus optimizes the model's decision-making. In addition, to enable the model to explore the environment more effectively, we design a novel dual-noise mechanism consisting of Gaussian noise and Ornstein-Uhlenbeck noise. Extensive experiments are conducted on the Gazebo simulation platform and validate that the proposed approach can generate a smoother navigation path and exceed the state-of-the-art performance with less computation cost. © 2022 IEEE.

关键词： Long short-term memory

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Processing of simulated biosensor data using artificial neural networks

Processing of simulated biosensor data using artificial neur...

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2011 IEEE International Symposium on IT in Medicine and Education, ITME 2011

作者： Ling, Gao Institute of Information Science and Engineering ShanDong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250014 China

ISBN: (纸本)9781612847023

Probability artificial neural network (PNN) and back propagation neural network (BPNN) are used in processing of the simulated data of heavy metals biosensors. The probability artificial neural network is used in pattern recognition to determine the kinds of heavy metals in the sample. The back propagation neural network is used in prediction of the heavy metal concentrations. The results shows that these two artificial neural networks work well. © 2011 IEEE.

关键词： Neural networks

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Application and research of improved probability matrix factorization techniques in collaborative filtering

International Journal of Control and Automation

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International Journal of Control and Automation 2014年第8期7卷 79-92页

作者： Zhang, Zhijun Liu, Hong School of Information Science and Engineering Shandong Normal University Jinan Shandong 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Jinan Shandong 250014 China School of Computer Science and Technology Shandong Jianzhu University Jinan Shandong 250101 China

The matrix factorization algorithms such as the matrix factorization technique (MF), singular value decomposition (SVD) and the probability matrix factorization (PMF) and so on, are summarized and compared. Based on the above research work, a kind of improved probability matrix factorization algorithm called MPMF is proposed in this paper. MPMF determines the optimal value of dimension D of both the user feature vector and the item feature vector through experiments. The complexity of the algorithm scales linearly with the number of observations, which can be applied to massive data and has very good scalability. Experimental results show that MPMF can not only achieve higher recommendation accuracy, but also improve the efficiency of the algorithm in sparse and unbalanced data sets compared with other related algorithms. © 2014 SERSC.

关键词： Singular value decomposition

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Visualization of fluid simulation: An SPH-based multi-resolution method

Visualization of fluid simulation: An SPH-based multi-resolu...

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作者： Zhang, Guijuan Lu, Dianjie Liu, Hong School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

Fluid simulation is an important research topic since it is widely used in virtual training, education, computer games, and digital entertainment. Generating fluid simulation results with high degree of visual realism is challenging in these applications. We present a visualization approach for fluid simulation in this paper. Our method allows students to understand the complex natural phenomena so as to solve the fluid engineering problems intuitively. In this study, an SPH (Smoothed Particle Hydrodynamics)-based multi-resolution method is proposed for fluid simulation. The method can keep the accuracy while improve the efficiency significantly. We construct a multi-resolution fluid model from bottom to top by combining fluid computation and surface reconstruction. We first compute multi-resolution particles with a novel adaptive SPH model in the bottom layer. Next, surface particles are extracted and pre-processed to provide samples for surface extraction. Then, the multi-resolution surface reconstruction model is built in the top layer. Meshes from different reconstruction resolutions are merged to obtain the final fluid surface. Experimental results show that our approach can effectively represent multi-scale fluid details and efficiently produce visual-pleasing fluid simulation results. © 2018 John Wiley & Sons, Ltd.

关键词： Visualization

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An elastic and distributed secret sharing scheme

An elastic and distributed secret sharing scheme

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International Conference on Informatics and Management Science, IMS 2012

作者： Wu, Lei School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Jinan 250014 China

ISBN: (纸本)9781447148104

How to ensure the security of important private data is the key problem in electric commerce. In this paper, we propose an elastic and distributed secret sharing scheme. The scheme improves the security of data and information by using technology of additive sharing and secret sharing. The information data is distributed and stored in a set of servers by additive sharing. Each data share is backed up to the sever sets. If an adversary could not corrupt more than the threshold number of servers, he could not obtain the secret information. The scheme has good elastic property;each server can freely join or quit the server group. The threshold value can be dynamically changed to adapt the environment in which the ability of adversary is alterable. © 2013 Springer-Verlag.

关键词： Security of data

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Research of knowledge transmission among the enterprise clusters based on cognitive perspective

Research of knowledge transmission among the enterprise clus...

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2012 International Symposium on Information Technologies in Medicine and Education, ITME 2012

作者： Niu, Jiayang Wang, Hongguo Dong, Shuxia Song, Chaochao Institute of Information Science and Engineering Shandong Normal University Shandong Province Key Laboratory for Distributed Computer Software Novel Technology Jinan China Institute of Information Technology Shandong Women's University Jinan China Institute of Management Science and Engineering Shandong Normal University Shandong Province Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9781467321068

The knowledge structure of the enterprise in cluster causes the difference among cognitive perspectives. In order to overcome the deficiency that the existing researches couldn't describe well the knowledge structure of enterprises, this paper proposed the knowledge transmission model among enterprise clusters based on cognitive perspective. The model proposed the definition of enterprise chromosome based on cognitive perspective, gave the learning strategies and knowledge interactive mechanism by referring to ant colony path selection. Simulations results indicate that the knowledge transmission within a cluster has the principle of cognitive proximity and the knowledge transfer probability is higher when the knowledge structure of two enterprises are similar. © 2012 IEEE.

关键词： Ant colony optimization

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Time series classification based on Unet with 2D transformation 12

Time series classification based on Unet with 2D transformat...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Du, Mingsen Wang, Yunxia Wei, Yanxuan Wu, Xiaona Ji, Cun Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798331510633

Time series classification has gained significant attention in the data mining field in recent years. Despite the proposal of numerous methods over the past decades, most of these approaches focus on feature extraction in one-dimensional space, which often results in lower classification accuracy. To address this issue, this paper proposes a Time Series Classification method based on U-Net with 2D transformation (TSC-Unet). This method first transforms univariate time series into 2D space and augments the data, effectively enhancing the diversity of the dataset. Subsequently, features are extracted from the 2D space using a fully convolutional network, fully exploiting multi-scale feature information. U-Netwith skip connections, effectively retains and transmits high-resolution features, significantly reducing information loss and improving segmentation performance. Additionally, the ability to learn multi-scale features enables the model to capture details at different levels, thereby enhancing its understanding of complex data. Experimental results demonstrate that TSC-Unet achieves high classification accuracy on the UCR datasets, validating the effectiveness and superiority of the proposed method. © 2024 IEEE.

关键词： Time series

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