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2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering最新文献

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An approach for incremental updating approximations in Variable precision rough sets while attribute generalized 属性广义化时变精度粗糙集的增量更新逼近方法
Junbo Zhang, Tianrui Li, Dun Liu
Rough set theory (RST) for knowledge updating have been successfully applied in data mining and it's correlative domains. As a special type of probabilistic rough set model, Variable precision rough sets (VPRS) model is an extension of RST. For an information system, the VPRS model allows a flexible approximation boundary region by using a precision variable and has a better tolerance ability for inconsistent data. However, the approximations of a concept may change when an information system varies. The approach for incremental updating of approximations while attribute generalizing in VPRS should be considered. In this paper, an incremental model and its algorithm for updating approximations of a concept based on VPRS are proposed when attribute generalized. Examples are employed to validate the feasibility of this approach.
粗糙集理论已经成功地应用于数据挖掘及其相关领域。变精度粗糙集(VPRS)模型作为一种特殊类型的概率粗糙集模型,是RST的扩展。对于信息系统而言,VPRS模型通过使用精度变量实现了灵活的近似边界区域,对数据不一致具有较好的容忍能力。但是,当信息系统变化时,概念的近似值可能会改变。在VPRS中,应考虑属性泛化时逼近的增量更新方法。本文提出了一种基于VPRS的增量模型及其算法,用于属性广义化时概念的逼近更新。通过算例验证了该方法的可行性。
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引用次数: 8
Combination of acceleration procedures for solving stochastic shortest-path Markov decision processes 求解随机最短路径马尔可夫决策过程的组合加速程序
M. G. García-Hernández, J. Ruiz-Pinales, S. Ledesma-Orozco, J. Aviña-Cervantes, E. Onaindía, A. Reyes-Ballesteros
In this paper we propose the combination of accelerated variants of value iteration with improved prioritized sweeping for the solution of stochastic shortest path Markov decision processes. For the fastest solution, asynchronous updates, prioritization and prioritized sweeping have been tested. A topological reordering algorithm was also compared with a static reordering algorithm. Experimental results obtained on afinite state and action-space stochastic shortest path problem are presented.
针对随机最短路径马尔可夫决策过程的求解问题,提出了一种结合加速变量迭代和改进优先扫描的方法。为了获得最快的解决方案,我们测试了异步更新、优先级划分和优先级清理。并将拓扑排序算法与静态排序算法进行了比较。给出了有限状态和动作空间随机最短路径问题的实验结果。
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引用次数: 2
A new method and instrument for measurement of plant leaf area 一种测量植物叶面积的新方法和仪器
Derong Zhang, Yong He
A algorithm with minimum memory consumption for labeling connected components in a binary image is presented in this paper. Based on embedded system technology, the algorithm is used in calculating the area of leaves, the high resolution images for this feature is provided by cheap scanner. Using the algorithm, a corresponding image processing program is developed, and it is ported successfully in embedded Linux & QT platform. With higher precision and lower cost, a new portable measuring instrument is developed for leaf area measurement.
提出了一种最小内存消耗的二值图像连通分量标记算法。该算法基于嵌入式系统技术,用于树叶面积的计算,该特征的高分辨率图像由廉价的扫描仪提供。利用该算法开发了相应的图像处理程序,并成功移植到嵌入式Linux和QT平台上。研制了一种具有较高精度和较低成本的便携式叶面积测量仪。
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引用次数: 0
Computing with words in linguistic decision making: Analysis of linguistic computing models 语言决策中的词计算:语言计算模型分析
L. Martínez
Decision Making is a core area in different fields in the real world. This plenary lecture focuses mainly on those problems dealing with vague and uncertain information, that often is based on perceptions. In such problems the linguistic information is a very helpful and flexible tool to model such a type of information but it implies the accomplishment of processes of computing with words. In the literature there exist different linguistic computing models to deal with linguistic information. This contribution reviews, analyzes and discusses different features of computing models in linguistic decision making, to verify if they can be branded as computing with words models.
决策是现实世界中各个领域的核心问题。这次全体会议演讲主要集中在那些处理模糊和不确定信息的问题,这些信息往往是基于感知的。在这类问题中,语言信息是一种非常有用和灵活的建模工具,但它意味着用词来完成计算过程。文献中存在不同的语言计算模型来处理语言信息。这篇文章回顾、分析和讨论了语言决策中计算模型的不同特征,以验证它们是否可以被称为单词计算模型。
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引用次数: 5
Cyclo-stationary detection of the Spectrum holes under blind parameters in cognitive radio 认知无线电盲参数下频谱空穴的循环平稳检测
Chengkai Tang, B. Lian, Lingling Zhang
While real-time monitoring the primary users' channel in cognitive radio, most secondary users can't obtain the signal parameters, as for primary user's information security and their own aspirations. In this paper, we proposed to estimate primary user signal parameters by the highest spectral correlation function at the non-zero frequency without any knowledge about primary signal, making use of the cyclo-stationary properties of the primary users. Based on the estimated parameters, we constructed a new decision threshold under the largest SNR in the detect channel. Finally, we compared the performance between our method and conventional energy detection method, and analysis the relationship between sampling points and the performance by simulation. The results verified that the performance of single cyclo-stationary users is equal to the energy detection with multi-users.
认知无线电在对主用户信道进行实时监控的同时,考虑到主用户的信息安全和用户自身的愿望,大多数次用户无法获取信号参数。本文提出在不了解主用户信号的情况下,利用主用户信号的周期平稳特性,利用非零频率处的最高谱相关函数估计主用户信号参数。在估计参数的基础上,构造了检测信道中信噪比最大的新判决阈值。最后,比较了该方法与传统能量检测方法的性能,并通过仿真分析了采样点与性能的关系。结果表明,单循环稳态用户的能量检测性能与多用户的能量检测性能相当。
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引用次数: 2
Short-term load forecasting: Learning in the feature space based on local temperature sensitive information 短期负荷预测:基于局部温度敏感信息的特征空间学习
Huanda Lu, Kangsheng Liu
A novel hybrid method based on feature extraction and neural network for short-term load forecasting was presented. It is well known that temperature information is very important for load forecasting, but the local structure of temperature sensitive information is not adopted in the literature. The proposed model adopts an integrated architecture to handle the local temperature sensitive information. Firstly, the input load data set is clustered into several temperature similar days subsets by the k-means algorithm in an unsupervised manner, Then compute max temperature factor in each subsets and split the time point (5 minutes, 288/day) into several time range, in each time range, we extract the features (coefficients) from load data using flourier basis system, and then learn the function in the feature space using artificial neural network. Finally, we smooth the whole forecasted load curve using linear programming. The empirical results indicate that our hybrid method results in better forecasting performance than the original generic support vector regression.
提出了一种基于特征提取和神经网络的短期负荷预测混合方法。众所周知,温度信息对负荷预测非常重要,但文献中没有采用温度敏感信息的局部结构。该模型采用集成架构处理局部温度敏感信息。首先,将输入负荷数据集以无监督的k-means算法聚类成多个温度相似天数子集,然后计算每个子集的最大温度因子,并将时间点(5分钟,288天)划分为多个时间范围,在每个时间范围内,利用fourier基系统提取负荷数据的特征(系数),然后利用人工神经网络学习特征空间中的函数。最后利用线性规划对整个预测负荷曲线进行平滑处理。实证结果表明,我们的混合方法比原始的通用支持向量回归具有更好的预测效果。
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引用次数: 1
Automated composition system based on GA 基于遗传算法的自动作文系统
Min Jiang, Changle Zhou
Researches on computer application to musical creation are actively performed in artificial intelligence. In this paper, we propose a hybrid method that adopts BP neural network for evaluation of emotions in music and genetic algorithm as an appropriate method for nominating creativity. Compared to other GAs used in this field, emotional element is used and combined with some musical rules as fitness function. The experiment results show that our method can yield music which is pleasant to ordinary listeners.
在人工智能领域,计算机应用于音乐创作的研究正在积极进行。在本文中,我们提出了一种混合方法,采用BP神经网络评估音乐中的情绪和遗传算法作为提名创造力的合适方法。与该领域使用的其他GAs相比,该GAs采用了情感元素,并结合一些音乐规则作为适应度函数。实验结果表明,该方法可以产生普通听众喜欢的音乐。
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引用次数: 7
Synchronization of phase oscillators as a model of synergy in sensor networks 作为传感器网络协同模型的相位振荡器同步
A. J. V. D. Wal
One of the most challenging phenomena that can be observed in an ensemble of interacting agents is that of self-organisation, viz. emergent, collective behaviour, also known as synergy. The concept of synergy is well-known in the artificial intelligence community, in social science, and in management and economic sciences. The paradigm may be expressed by identifying an ensemble performance measure that yields more than the sum of the individual performance measures of the constituents. The aim of the present study is to discuss in a simple conceptual model system under what circumstances self-organization is feasible and to discuss what type of agents and interactions are minimally required to induce synergy among agents. As a case in point we discuss the emergent phase coherence of a multi-oscillator system with non-linear all-to-all coupling between the oscillators. In the thermodynamic limit of infinitely many interacting agents this system shows spontaneous organization. Simulations indicate that also for finite populations that are not completely connected partial phase synchronization spontaneously emerges if the interaction strength is strong enough.
在相互作用的代理集合中可以观察到的最具挑战性的现象之一是自组织,即涌现的集体行为,也称为协同作用。协同的概念在人工智能领域、社会科学、管理和经济科学中众所周知。范例可以通过确定一个整体性能度量来表达,该度量产生的结果大于组成部分的单个性能度量的总和。本研究的目的是在一个简单的概念模型系统中讨论在什么情况下自组织是可行的,并讨论什么类型的主体和相互作用是诱导主体之间协同作用的最低要求。作为一个恰当的例子,我们讨论了一个多振子之间具有非线性全对全耦合的系统的涌现相位相干性。在无穷多个相互作用因子的热力学极限下,系统表现为自发组织。仿真结果表明,对于非完全连通的有限种群,如果相互作用强度足够强,则会自发地出现部分相位同步。
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引用次数: 0
On the architecture and address mapping mechanism of IoT 物联网的体系结构和地址映射机制
Bin Xu, Yangguang Liu, Xiaoqi He, Yanping Tao
The Internet of Things has been widespread concerned in recent years. This paper introduces the concept and current status of Internet of Things technology, analyzes the key technologies in research, including RFID, sensor technologies, embedded intelligence and nanotechnology, and discusses the existing architecture. Finally, we proposed a new address mapping mechanism.
近年来,物联网受到了广泛关注。本文介绍了物联网技术的概念和现状,分析了研究中的关键技术,包括RFID、传感器技术、嵌入式智能和纳米技术,并对现有的体系结构进行了讨论。最后,我们提出了一种新的地址映射机制。
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引用次数: 12
Building complete Collaborative Filtering Method System 构建完整的协同过滤方法系统
Li Yu, Xiaoping Yang
Collaborative filtering (CF) is a key technique in recommender system. Recently, general neighborhood problem existing in collaborative filtering is identified in our previous work, which could result into fatal wrong under multi-community or multi-interest case. In order to overcome it, collaborative filtering based on community (CFC) is presented. Unfortunately, CFC suffers from severer sparsity, which could result into worse performance. Various improved methods are proposed to enhance it. Based on a series of above methods, a complete and hierarchical Collaborative Filtering Method System (CFMS) is build. CFMS extend collaborative filtering, adapting to various different cases. Experiments are made to empirically valuate and compare various methods of CFMS.
协同过滤是推荐系统中的一项关键技术。近年来,我们在以往的工作中发现了协同过滤中存在的一般邻域问题,在多社区或多利益的情况下可能导致致命的错误。为了克服这一问题,提出了基于社区的协同过滤(CFC)。不幸的是,CFC存在严重的稀疏性,这可能导致更差的性能。提出了各种改进方法来增强它。基于上述一系列方法,构建了一个完整的分层协同过滤方法系统(CFMS)。CFMS扩展了协同过滤,适应各种不同的情况。通过实验对各种CFMS方法进行了实证评价和比较。
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2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering
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