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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2003年 2773卷 V页

作者： Palade, Vasile Howlett, Bob Jain, Lalthmi Oxford University Computing Laboratory Parks Road Oxford United Kingdom University of Blighton Intelligent Systems and Signal Processing Labs Moulsecoomb Brighton United Kingdom University of South Australia Knowledge-Based Intelligent Engineering Systems Centre Mawson Lakes AdelaideSA Australia

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Designing a Top—Level Ontology of Human Beings：A Multi—Perspective Approach

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Journal of Computer Science & Technology 2002年第5期17卷 636-656页

作者：田雯顾芳曹存根 Key Laboratory of Intelligent Information Processing Institute of Computing Technology The Chinese Academy of Sciences Beijing 100080 P.R. China

Knowledge about human beings is an integral part of any intelligent agent ofconsiderable significance. Delimiting, modeling and acquiring such knowledge are the centraltopics of this paper. Because of the tremendous complexity in knowledge of human beings, weintroduce a top-level ontology of human beings from the perspectives of psychology, sociology,physiology and pathology. This ontology is not only an explicit conceptualization of humanbeings, but also an efficient way of acquiring and organizing relevant knowledge.

关键词： knowledge of human beings ontology top-level ontology of human beings psychology sociology physiology pathology knowledge acquisition

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Progress in the Development of National Knowledge Infrastructure

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Journal of Computer Science & Technology 2002年第5期17卷 523-534页

作者：曹存根丰强泽高颖顾芳司晋新眭跃飞田雯王海涛王丽丽曾庆田张春霞郑宇飞周肖彬 Knowledge Acquisition and Sharing Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology The Chinese Academy of Sciences Beijing 100080 P.R. China

This paper presents the recent process in a long-term research project, calledNational Knowledge Infrastructure (or NKI). Initiated in the early 2000, the project aims todevelop a multi-domain shareable knowledge base for knowledge-intensive applications. Todevelop NKI, we have used domain-specific ontologies as a solid basis, and have built morethan 600 ontologies. Using these ontologies and our knowledge acquisition methods, we haveextracted about 1.1 millions of domain assertions. For users to access our NKI knowledge,we have developed a uniform multi-modal human-knowledge interface. We have also imple-mented a knowledge application programming interface for various applications to share theNKI knowledge.

关键词： national knowledge infrastructure knowledge acquisition domain-specificontology human-knowledge interface knowledge application programming interface

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A self-adjusting and probabilistic decision-making classifier based on the constructive covering algorithm in neural networks

A self-adjusting and probabilistic decision-making classifie...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yan-Ping Zhang Tao Wu Ling Zhang Ministry of Education Key Laboratory of Intelligent computing & Signal Processing Anhui University Hefei China

ISBN: (纸本)0780375084

It usually needs complicated nonlinear operations to get the characteristics from the raw information inputted, and it is very difficult to find this kind of algorithm directly. The geometrical meaning of the multilayer perceptron's neuron model indicates that classifying samples according to the requirements by constructing neural networks is equal to finding a collection of domains with which vectors of the preset sample sets are partitioned. But in some applications, such as time series forecasting including stock share forecasting, due to their preset sample sets may contain some exceptions and erroneous results, it is desired to introduce some self-adjusting and probabilistic decision-making mechanism to enhance the accuracy of classification. At the same time the mechanism can reduce the size of neural networks and speed up the recognition process. We discuss a self-adjusting and probabilistic decision-making mechanism for the covering algorithm. Based on the method, we developed a self-adjusting and probabilistic decision-making classifier and applied the software package to forecast the share index of Shanghai's stock market.

关键词： Decision making intelligent networks Neural networks signal processing algorithms Support vector machines Neurons Solid modeling Stock markets Kernel Economic forecasting

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Advances in simulated modeling of vibration systems based on computational intelligence

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自然科学进展(英文版) 2002年第12期12卷 881-886页

作者： LIANG Yanchun LEE Heow Pueh LIN Wuzhong LIM Siak Piang LEE Kwok Hong Zhou Chunguang College of Computer Science and Technology Jilin University Changchun 130012 China Institute of High Performance Computing Singapore 117528 Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sci Institute of High Performance Computing Singapore 117528 Department of Mechanical Engineering National University of Singapore Singapore 119260 Singapore College of Computer Science and Technology Jilin University Changchun 130012 China

Computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art in the field of simulated modeling of vibration systems using methods of computational intelligence, based on some relevant subjects and the authors' own research work. First, contributions to the applications of computational intelligence to the identification of nonlinear characteristics of packaging are reviewed. Subsequently, applications of the newly developed training algorithms for feedforward neural networks to the identification of restoring forces in multi-degree-of-freedom nonlinear systems are discussed. Finally, the neural-network-based method of model reduction for the dynamic simulation of microelectromechanical systems (MEMS) using generalized Hebbian algorithm (GHA) and robust GHA is outlined. The prospects of the simulated modeling of vibration systems using techniques of computational intelligence are also indicated.

关键词： computational intelligence artificial neural network fuzzy system evolutionary computation vibration system

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The classification method based on hyper surface

The classification method based on hyper surface

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International Joint Conference on Neural Networks (IJCNN)

作者： Qing He Zhong-Zhi Shi Li-An Ren The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Graduate College of University of Science and Technology of China Beijing China

The main idea of SVM, i.e. Support Vector Machine, is mapping nonlinear separable data into higher dimension linear space where the data can be separated by hyper plane. Based on Jordan Curve Theorem, a general classification method HSC, Classification based on Hyper Surface, is put forward in this paper. The separating hyper surface is directly made to classify large database. The data are classified according to whether the intersecting number is odd or even. It is a novel approach which has no need of either mapping from lower dimension space to higher dimension space or considering kernel function. It can directly solve the nonlinear classification problem. The experiments show that the new method can efficiently and accurately classify large data.

关键词： Support vector machines Support vector machine classification Machine learning Machine learning algorithms Space technology Databases Artificial intelligence Neural networks Helium Laboratories

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Panoramic mosaicing with image interpolation

Panoramic mosaicing with image interpolation

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2001 International Symposium on intelligent Multimedia, Video and Speech processing, ISIMP 2001

作者： Ruan, Zong-Cai Liang, Dong Wei, Sui Key Lab. of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China

ISBN: (纸本)9628576623

Panoramic mosaics methods based on 8-paramter planar homography matrix have to overcome the accumulated errors, when a sequence of images loops back on itself. Usual methods are computationally intensive, and can not ensure complete consistency of homographies. This paper presents a simple method, which do not require the consistency of homographies. The method mainly exploits un-calibrated image perspective interpolation technique [1]. So it is of simple calculation and easy realization.

关键词： Interpolation

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Panoramic mosaicing with image interpolation

Panoramic mosaicing with image interpolation

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International Symposium on intelligent Multimedia, Video and Speech processing

作者： Zong-Cai Ruan Dong Liang Sui Wei Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University of Science and Technology Hefei China

ISBN: (纸本)9628576623

Panoramic mosaics methods based on an 8-parameter planar homography matrix have to overcome the accumulated errors when a sequence of images loops back on itself. Usual methods are computationally intensive, and cannot ensure complete consistency of homographies. The paper presents a simple method which does not require the consistency of homographies. The method mainly exploits an un-calibrated image perspective interpolation technique (B. Yuan et al., 1998). It is therefore simple to calculate and easy to implement.

关键词： Interpolation Transmission line matrix methods Matrix decomposition Equations Error correction Computational intelligence signal processing Nonlinear optics Optical signal processing Image motion analysis

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GAHE: Geometry-Aware Embedding for Hyper-Relational Knowledge Graph Representation

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ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Zongsheng Cao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security SKLOIS) Institute of Information Engineering Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Security Department of Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China

Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capabilities. Each fact in an HKG consists of a main triple supplemented by attribute-value qualifiers that provide additional contextual information. Due to the complexity of hyper-relations, HKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, often mixed together. However, previous work mainly embeds HKGs into Euclidean space, limiting their ability to capture these complex geometric structures simultaneously. To address this challenge, we propose a novel model called Geometry Aware Hyper-relational Embedding (GAHE). Specifically, GAHE adopts a multi-curvature geometry-aware approach by modeling HKGs in Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature) in a unified framework. In this way, it can integrate space-invariant and space-specific features to accurately capture the diverse structures in HKGs. In addition, GAHE introduces a module termed hyper-relational subspace learning, which allocates multiple sub-relations for each hyper-relation. It enables the exploitation of abundant latent semantic interactions and facilitates the exploration of fine-grained semantics between attribute-value pairs and hyper-relations across multiple subspaces. Furthermore, we provide theoretical guarantees that GAHE is fully expressive and capable of modeling a wide range of semantic patterns for hyper-relations. Empirical evaluations demonstrate that GAHE achieves state-of-the-art results on both hyper-relational and binary-relational benchmarks.

关键词： Knowledge Graph Hyper-relational Learning Multicurvature Spaces Geometry Aware

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An indirect convergent Jacobi spectral collocation method for fractional optimal control problems

An indirect convergent Jacobi spectral collocation method fo...

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作者： Yang, Yin Zhang, Jiaqi Liu, Huan O. Vasilev, Aleksandr Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Xiangtan Hunan China Ammosov North-Eastern Federal University Yakutsk Russia

In this paper, we present a novel indirect convergent Jacobi spectral collocation method for fractional optimal control problems governed by a dynamical system including both classical derivative and Caputo fractional derivative. First, we present some necessary optimality conditions. Then we suggest a new Jacobi spectral collocation method to discretize the obtained conditions. By the proposed method, we get a system of algebraic equations by solving of which we can approximate the optimal solution of the main problem. Finally, we present a convergence analysis for our method and solve three numerical examples to show the efficiency and capability of the method. © 2019 John Wiley & Sons, Ltd.

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