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Advances in simulated modeling of vibration systems based on computational intelligence

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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Understanding the Robustness of Deep Recommendation under Adversarial Attacks

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ACM Transactions on Knowledge Discovery from Data 1000年

作者： Fulan Qian Wenbin Chen Hai Chen Yan Cui Shu Zhao Yanping Zhang Artificial Intelligence Institute Anhui University China the Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University China and The Key Laboratory of Intelligent Computing and Signal ProcessingAnhui University) China School of Computer Science and Technology Anhui University China Anhui University School of Computer Science and Technology China Artificial Intelligence Institute Anhui University China The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University China and The Key Laboratory of Intelligent Computing and Signal ProcessingAnhui University) China

It has been shown that deep recommendation models are susceptible to adversarial attacks, with this vulnerability potentially leading to significant economic losses in the e-commerce field. However, the robustness of deep recommendation models in response to adversarial attacks has not been systematically investigated. In this paper, therefore, we comprehensively evaluate the adversarial robustness of various representative deep models in different settings, aiming to analyze their performance impact under adversarial attacks and compare it with traditional collaborative filtering models. Notably, we examine poisoning attacks under different proportions of fake users and various popularity conditions to understand why certain deep recommendation models perform exceptionally or sub-optimally. On this basis, we further proposed practical robustness improvement strategy for the problems found in the evaluation and fully verified it through rigorous experiments. key findings include: 1) the sparser the training dataset, the weaker the robustness of a recommendation model's performance under adversarial attacks; 2) deep recommendation models exhibit greater robustness in recommending popular items under adversarial attacks, while they are more vulnerable when attacked with non-popular items; 3) the robustness of deep recommendation models is not consistently weaker than that of traditional collaborative filtering models across all attack settings. These findings highlight the security concerns in deep recommendation systems and contribute to developing more reliable models.

关键词： Adversarial examples recommendation system deep learning

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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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Advances in Multimedia Information processing - PCM 2010 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Guoping Qiu Kin Man Lam Hitoshi Kiya Xiang-Yang Xue C.-C. Jay Kuo Michael S. Lew

ISBN: (数字)9783642157028

ISBN: (纸本)9783642157011

The 2010 Pacific-Rim Conference on Multimedia (PCM 2010) was held in Shanghai at Fudan University, during September 21–24, 2010. Since its inauguration in 2000, PCM has been held in various places around the Pacific Rim, namely Sydney (PCM 2000), Beijing (PCM 2001), Hsinchu (PCM 2002), Singapore (PCM 2003), Tokyo (PCM 2004), Jeju (PCM 2005), Zhejiang (PCM 2006), Hong Kong (PCM 2007), Tainan (PCM 2008), and Bangkok (PCM 2009). PCM is a major annual international conference organized as a forum for the dissemination of state-of-the-art technological advances and research results in the fields of theoretical, experimental, and applied multimedia analysis and processing. PCM 2010 featured a comprehensive technical program which included 75 oral and 56 poster presentations selected from 261 submissions from Australia, Canada, China, France, Germany, Hong Kong, India, Iran, Italy, Japan, Korea, Myanmar, Norway, Singapore, Taiwan, Thailand, the UK, and the USA. Three distinguished researchers, Prof. Zhi-Hua Zhou from Nanjing University, Dr. Yong Rui from Microsoft, and Dr. Tie-Yan Liu from Microsoft Research Asia delivered three keynote talks to the conference. We are very grateful to the many people who helped to make this conference a s- cess. We would like to especially thank Hong Lu for local organization, Qi Zhang for handling the publication of the proceedings, and Cheng Jin for looking after the c- ference website and publicity. We thank Fei Wu for organizing the special session on large-scale multimedia search in the social network settings.

关键词： Computer Applications Multimedia Information Systems Computer Communication Networks Information Systems Applications (incl. Internet) Information Storage and Retrieval User Interfaces and Human Computer Interaction

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The Semantic Web – ASWC 2006 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Riichiro Mizoguchi Zhongzhi Shi Fausto Giunchiglia

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Comparative Analysis of Personality Recognition in Response to Virtual Reality and Two-Dimensional Emotional Stimulus Using ECG signals

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ACM Transactions on Interactive intelligent Systems 1000年

作者： Jialan Xie Ping Lan Zhaonian Hu Guangyuan Liu College of Electronic and Information Engineering Southwest University Chongqing China Institute of Affective Computing and Information Processing Southwest University Chongqing China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing China

Personality primarily refers to the unique and stable way of a person’s thinking and behavior. A few studies have recently been conducted on personality recognition using physiological signals, most of which have used two-dimensional (2D) emotional stimulus materials. Virtual reality (VR) has been utilized in many fields, and its superiority over 2D in emotion recognition has been proven. However, relevant research on VR scenes is lacking in the field of personality recognition. In this study, based on the psychological principle that emotional arousal can expose an individual’s personality, we attempt to explore the feasibility and effect of using electrocardiogram (ECG) signals in response to VR emotional stimuli for personality identification. For this purpose, a VR-2D emotion-induction experiment was conducted in which ECG signals were collected, and physiological datasets of emotional personalities were constructed through preprocessing and feature extraction. Statistical analysis of the emotion scale scores and ECG features of the participants showed that the VR group had a higher number of significantly correlated features. Meanwhile, VR- and 2D-based personality recognition models were constructed using machine learning algorithms. The results showed that the VR-based personality recognition model achieved better results for the four personality dimensions, with a maximum accuracy of 79.76%. These findings indicate that VR not only enhances the physiological correlation between emotion and personality but also improves the classification accuracy of personality recognition.

关键词： Big Five personality Electrocardiogram Emotion Personality recognition Virtual reality

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