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Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots 基于神经网络主成分分析、谐波约束和帕克图的电力变压器差动保护
Pub Date : 2012-08-28 DOI: 10.1155/2012/930740
M. Tripathy
This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.
提出了一种基于波形识别技术的电力变压器差动保护新方法。提出了一种基于反向传播学习的神经网络主成分分析(NNPCA)的电力变压器数字差动保护算法。采用主成分分析方法对电力系统数据进行预处理,消除冗余信息,增强差动电流的隐藏模式,以区分内部故障、励磁和过励磁。该算法考虑了隐藏层最优神经元数和输出层最优神经元数。该算法利用电压频率比和差动电流幅值对变压器运行状态进行检测。本文对基于谐波约束法、NNPCA、前馈反传播神经网络(FFBPNN)、差分信号的空间矢量分析及其在帕克平面上的时间特征形状的电力变压器差动保护算法进行了比较研究。比较了两种算法的响应速度、计算量以及区分励磁涌流和变压器内部故障的能力。简要描述了每种算法的数学基础。利用PSCAD/EMTDC和MATLAB进行了仿真,对所有算法进行了评估。
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引用次数: 19
Work Out the Semantic Web Search: The Cooperative Way 语义网络搜索的实现:合作方式
Pub Date : 2012-01-01 DOI: 10.1155/2012/867831
Dora Melo, I. Rodrigues, V. Nogueira
We propose a Cooperative Question Answering System that takes as input natural language queries and is able to return a cooperative answer based on semantic web resources, more specifically DBpedia represented in OWL/RDF as knowledge base and WordNet to build similar questions. Our system resorts to ontologies not only for reasoning but also to find answers and is independent of prior knowledge of the semantic resources by the user. The natural language question is translated into its semantic representation and then answered by consulting the semantics sources of information. The system is able to clarify the problems of ambiguity and helps finding the path to the correct answer. If there are multiple answers to the question posed (or to the similar questions for which DBpedia contains answers), they will be grouped according to their semantic meaning, providing a more cooperative and clarified answer to the user.
我们提出了一个协作问答系统,它以自然语言查询作为输入,并能够基于语义web资源返回协作答案,更具体地说,以OWL/RDF表示的DBpedia作为知识库和WordNet来构建类似的问题。我们的系统不仅依靠本体进行推理,而且还可以找到答案,并且不依赖于用户对语义资源的先验知识。将自然语言问题翻译成它的语义表示,然后通过查询语义信息源来回答。该系统能够澄清模棱两可的问题,并帮助找到通往正确答案的路径。如果提出的问题(或DBpedia包含答案的类似问题)有多个答案,则将根据它们的语义对它们进行分组,从而为用户提供更协作、更明确的答案。
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引用次数: 9
Development of Robots with Soft Sensor Flesh for Achieving Close Interaction Behavior 实现近距离交互行为的柔性传感肉机器人的研制
Pub Date : 2012-01-01 DOI: 10.1155/2012/157642
T. Yoshikai, Marika Hayashi, Yui Ishizaka, Hiroko Fukushima, Asuka Kadowaki, Takashi Sagisaka, Kazuya Kobayashi, Iori Kumagai, M. Inaba
In order to achieve robots' working around humans, safe contacts against objects, humans, and environments with broad area of their body should be allowed. Furthermore, it is desirable to actively use those contacts for achieving tasks. Considering that, many practical applications will be realized by whole-body close interaction of many contacts with others. Therefore, robots are strongly expected to achieve whole-body interaction behavior with objects around them. Recently, it becomes possible to construct wholebody tactile sensor network by the advancement of research for tactile sensing system. Using such tactile sensors, some research groups have developed robots with whole-body tactile sensing exterior. However, their basic strategy is making a distributed 1- axis tactile sensor network covered with soft thin material. Those are not sufficient for achieving close interaction and detecting complicated contact changes. Therefore, we propose "Soft Sensor Flesh." Basic idea of "Soft Sensor Flesh" is constructing robots' exterior with soft and thick foam with many sensor elements including multiaxis tactile sensors. In this paper, a constructing method for the robot systems with such soft sensor flesh is argued. Also, we develop some prototypes of soft sensor flesh and verify the feasibility of the proposed idea by actual behavior experiments.
为了实现机器人在人类周围工作,应该允许机器人与物体、人类和大面积的环境进行安全接触。此外,积极地利用这些联系来完成任务是可取的。考虑到这一点,许多实际应用将通过与他人的多次接触的全身密切互动来实现。因此,人们强烈期望机器人能够实现与周围物体的全身交互行为。近年来,随着触觉传感系统研究的不断深入,构建全身触觉传感器网络成为可能。利用这种触觉传感器,一些研究小组已经开发出具有全身触觉感知外部的机器人。然而,他们的基本策略是用柔软的薄材料覆盖一个分布式的1轴触觉传感器网络。这些不足以实现密切的相互作用和检测复杂的接触变化。因此,我们提出“软传感器肉”。“软传感器肉”的基本思想是用柔软厚实的泡沫构建机器人的外部,其中包含许多传感器元件,包括多轴触觉传感器。本文讨论了具有这种软传感肉的机器人系统的构造方法。此外,我们还开发了一些软传感器肉的原型,并通过实际行为实验验证了所提出思想的可行性。
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引用次数: 20
CardioSmart365: Artificial Intelligence in the Service of Cardiologic Patients CardioSmart365:为心脏病患者服务的人工智能
Pub Date : 2012-01-01 DOI: 10.1155/2012/585072
Efrosini Sourla, S. Sioutas, V. Syrimpeis, A. Tsakalidis, Giannis Tzimas
Artificial intelligence has significantly contributed in the evolution of medical informatics and biomedicine, providing a variety of tools available to be exploited, from rule-based expert systems and fuzzy logic to neural networks and genetic algorithms. Moreover, familiarizing people with smartphones and the constantly growing use of medical-related mobile applications enables complete and systematic monitoring of a series of chronic diseases both by health professionals and patients. In this work, we propose an integrated system for monitoring and early notification for patients suffering from heart diseases. CardioSmart365 consists of web applications, smartphone native applications, decision support systems, and web services that allow interaction and communication among end users: cardiologists, patients, and general doctors. The key features of the proposed solution are (a) recording and management of patients' measurements of vital signs performed at home on regular basis (blood pressure, blood glucose, oxygen saturation, weight, and height), (b) management of patients' EMRs, (c) cardiologic patient modules for the most common heart diseases, (d) decision support systems based on fuzzy logic, (e) integrated message management module for optimal communication between end users and instant notifications, and (f) interconnection to Microsoft Health Vault platform. CardioSmart365 contributes to the effort for optimal patient monitoring at home and early response in cases of emergency.
人工智能在医学信息学和生物医学的发展中做出了重大贡献,提供了各种可供利用的工具,从基于规则的专家系统和模糊逻辑到神经网络和遗传算法。此外,人们对智能手机的熟悉以及医疗相关移动应用程序的不断增加使用,使卫生专业人员和患者能够对一系列慢性疾病进行全面和系统的监测。在这项工作中,我们提出了一个对心脏病患者进行监测和早期通知的综合系统。CardioSmart365由web应用程序、智能手机原生应用程序、决策支持系统和web服务组成,允许最终用户(心脏病专家、患者和普通医生)之间的交互和通信。所提出的解决方案的主要特点是(a)记录和管理患者在家中定期进行的生命体征测量(血压、血糖、血氧饱和度、体重和身高),(b)管理患者的电子病历,(c)针对最常见心脏病的心脏病患者模块,(d)基于模糊逻辑的决策支持系统,(e)集成消息管理模块,用于最终用户和即时通知之间的最佳通信。以及(f)与Microsoft Health Vault平台的互连。CardioSmart365有助于在家中对患者进行最佳监测,并在紧急情况下做出早期反应。
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引用次数: 7
Chaotic Neural Network for Biometric Pattern Recognition 生物特征模式识别的混沌神经网络
Pub Date : 2012-01-01 DOI: 10.1155/2012/124176
Kushan Ahmadian, M. Gavrilova
Biometric pattern recognition emerged as one of the predominant research directions inmodern security systems. It plays a crucial role in authentication of both real-world and virtual reality entities to allow system to make an informed decision on granting access privileges or providing specialized services. The major issues tackled by the researchers are arising from the ever-growing demands on precision and performance of security systems and at the same time increasing complexity of data and/or behavioral patterns to be recognized. In this paper, we propose to deal with both issues by introducing the new approach to biometric pattern recognition, based on chaotic neural network (CNN). The proposed method allows learning the complex data patterns easily while concentrating on the most important for correct authentication features and employs a unique method to train different classifiers based on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate set of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature space. The experimental results show the superior performance of the proposed method.
生物特征模式识别已成为现代安防系统的主要研究方向之一。它在现实世界和虚拟现实实体的身份验证中起着至关重要的作用,使系统能够在授予访问权限或提供专门服务方面做出明智的决定。研究人员解决的主要问题是由于对安全系统的精度和性能的要求日益增长,同时需要识别的数据和/或行为模式也越来越复杂。在本文中,我们提出通过引入基于混沌神经网络(CNN)的生物特征模式识别新方法来解决这两个问题。该方法可以轻松地学习复杂的数据模式,同时专注于最重要的正确身份验证特征,并采用独特的方法基于每个特征集训练不同的分类器。聚合结果描述了对已识别标识的最终决策。为了训练出准确的分类器集,采用子空间聚类方法克服了特征空间的高维问题。实验结果表明了该方法的优越性。
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引用次数: 9
Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules 盆地跳跃作为生物大分子表征的通用和通用优化框架
Pub Date : 2012-01-01 DOI: 10.1155/2012/674832
Brian S. Olson, I. Hashmi, Kevin Molloy, Amarda Shehu
Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.
自引入以来,盆地跳跃(BH)框架已被证明对具有多变量和多模态的非线性优化问题非常有用。应用范围很广,从几何中的填充问题到统计物理中分子状态的表征。BH在计算结构生物学中重新出现,因为它能够根据局部极小值获得蛋白质能量表面的粗粒度表示。在本文中,我们证明了BH框架是通用的和通用的,由于其在高维变量空间中采样最小值的基本能力,可以解决与蛋白质结构、组装和运动表征相关的问题。我们展示了BH中主要组件的具体实现如何在从头计算蛋白质结构预测和刚性蛋白质-蛋白质对接的背景下获得最先进的结果的算法实现。我们还表明,BH可以映射与蛋白质分子中连接各种稳定功能相关状态的运动相关的中间最小值,从而作为表征连接这些状态的过渡轨迹的第一步。
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引用次数: 69
Crowd Evacuation for Indoor Public Spaces Using Coulomb's Law 基于库仑定律的室内公共空间人群疏散
Pub Date : 2012-01-01 DOI: 10.1155/2012/340615
Pejman Kamkarian, H. Hexmoor
This paper focuses on designing a tool for guiding a group of people out of a public building when they are faced with dangerous situations that require immediate evacuation. Despite architectural attempts to produce safe floor plans and exit door placements, people will still commit to fatal route decisions. Since they have access to global views, we believe supervisory people in the control room can use our simulation tools to determine the best courses of action for people. Accordingly, supervisors can guide people to safety. In this paper, we combine Coulomb's electrical law, graph theory, and convex and centroid concepts to demonstrate a computer-generated evacuation scenario that divides the environment into different safe boundaries around the locations of each exit door in order to guide people through exit doors safely and in the most expedient time frame. Our mechanism continually updates the safe boundaries at each moment based on the latest location of individuals who are present inside the environment. Guiding people toward exit doors depends on the momentary situations in the environment, which in turn rely on the specifications of each exit door. Our mechanism rapidly adapts to changes in the environment in terms of moving agents and changes in the environmental layout that might be caused by explosions or falling walls.
本文的重点是设计一个工具,当一群人面临需要立即疏散的危险情况时,引导他们离开公共建筑。尽管建筑设计试图设计出安全的楼层布局和出口位置,但人们仍然会做出致命的路线决定。由于他们可以获得全局视图,我们相信控制室的主管人员可以使用我们的模拟工具来确定人们的最佳行动方案。因此,管理者可以引导人们到安全的地方。在本文中,我们结合库仑电学定律、图论以及凸形和质心概念来演示计算机生成的疏散场景,该场景将每个出口门周围的环境划分为不同的安全边界,以便在最方便的时间框架内引导人们安全通过出口门。我们的机制每时每刻都在根据环境中个体的最新位置不断更新安全边界。引导人们走向出口门取决于环境中的瞬间情况,而瞬间情况又取决于每个出口门的规格。我们的机制可以快速适应环境的变化,比如移动的代理和环境布局的变化,这些变化可能是由爆炸或倒塌的墙壁引起的。
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引用次数: 12
Soccer Ball Detection by Comparing Different Feature Extraction Methodologies 比较不同特征提取方法的足球检测
Pub Date : 2012-01-01 DOI: 10.1155/2012/512159
P. Mazzeo, Marco Leo, P. Spagnolo, M. Nitti
This paper presents a comparison of different feature extraction methods for automatically recognizing soccer ball patterns through a probabilistic analysis. It contributes to investigate different well-known feature extraction approaches applied in a soccer environment, in order tomeasure robustness accuracy and detection performances. This work, evaluating differentmethodologies, permits to select the one which achieves best performances in terms of detection rate and CPU processing time. The effectiveness of the differentmethodologies is demonstrated by a huge number of experiments on real ball examples under challenging conditions.
本文通过概率分析,比较了不同特征提取方法在足球图案自动识别中的应用。它有助于研究在足球环境中应用的不同知名特征提取方法,以衡量鲁棒性,准确性和检测性能。这项工作,评估不同的方法,允许选择在检测率和CPU处理时间方面达到最佳性能的方法。在具有挑战性的条件下,对真实球实例进行了大量的实验,证明了不同方法的有效性。
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引用次数: 21
A Novel Approach to Improve the Performance of Evolutionary Methods for Nonlinear Constrained Optimization 一种改进非线性约束优化进化方法性能的新方法
Pub Date : 2012-01-01 DOI: 10.1155/2012/540861
A. Rowhanimanesh, S. Efati
Evolutionary methods are well-known techniques for solving nonlinear constrained optimization problems. Due to the exploration power of evolution-based optimizers, population usually converges to a region around global optimum after several generations. Although this convergence can be efficiently used to reduce search space, in most of the existing optimization methods, search is still continued over original space and considerable time is wasted for searching ineffective regions. This paper proposes a simple and general approach based on search space reduction to improve the exploitation power of the existing evolutionary methods without adding any significant computational complexity. After a number of generations when enough exploration is performed, search space is reduced to a small subspace around the best individual, and then search is continued over this reduced space. If the space reduction parameters (red_gen and red factor) are adjusted properly, reduced space will include global optimum. The proposed scheme can help the existing evolutionary methods to find better near-optimal solutions in a shorter time. To demonstrate the power of the new approach, it is applied to a set of benchmark constrained optimization problems and the results are compared with a previous work in the literature.
进化方法是解决非线性约束优化问题的著名技术。由于基于进化的优化器的探索能力,种群通常在几代之后收敛到全局最优附近的一个区域。虽然这种收敛性可以有效地减少搜索空间,但在现有的大多数优化方法中,仍然在原始空间上继续搜索,并且在搜索无效区域时浪费了相当多的时间。本文提出了一种基于搜索空间约简的简单通用方法,在不增加显著计算复杂度的前提下,提高了现有进化方法的开发能力。在进行了足够多的探索之后,搜索空间被简化为最佳个体周围的小子空间,然后在这个简化的空间上继续搜索。如果空间缩减参数(red_gen和red factor)被适当调整,缩减的空间将包括全局优化。该方案可以帮助现有的进化方法在更短的时间内找到更好的近最优解。为了证明新方法的强大功能,将其应用于一组基准约束优化问题,并将结果与文献中的先前工作进行了比较。
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引用次数: 4
RPCA: A Novel Preprocessing Method for PCA 一种新的PCA预处理方法
Pub Date : 2012-01-01 DOI: 10.1155/2012/484595
S. Yazdani, J. Shanbehzadeh, M. Shalmani
We propose a preprocessing method to improve the performance of Principal Component Analysis (PCA) for classification problems composed of two steps; in the first step, the weight of each feature is calculated by using a feature weighting method. Then the features with weights larger than a predefined threshold are selected. The selected relevant features are then subject to the second step. In the second step, variances of features are changed until the variances of the features are corresponded to their importance. By taking the advantage of step 2 to reveal the class structure, we expect that the performance of PCA increases in classification problems. Results confirm the effectiveness of our proposed methods.
针对由两个步骤组成的分类问题,提出了一种改进主成分分析(PCA)性能的预处理方法;第一步,使用特征加权法计算每个特征的权重。然后选择权重大于预定义阈值的特征。然后将所选的相关特征置于第二步。在第二步中,改变特征的方差,直到特征的方差与它们的重要性相对应。通过利用步骤2揭示类结构,我们期望PCA在分类问题中的性能得到提高。结果证实了所提方法的有效性。
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引用次数: 7
期刊
Adv. Artif. Intell.
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