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IEEE International Conference on Data Mining (ICDM)

作者： Dacheng Tao Xuelong Li Weiming Hu S. Maybank Xindong Wu School of Computer Science and Information Systems Birkbeck College University of London UK National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China Department of Computer Science University of Vermont USA

This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learning machines can be generalized to take n/sup th/-order tensors as inputs. We also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). These generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.

关键词： Tensile stress Machine learning Support vector machines Supervised learning Data mining Support vector machine classification computer science Minimax techniques Feature extraction Linear discriminant analysis

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Novel Multivariate Calibration Methods for X-Ray Analyzer

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IFAC Proceedings Volumes 2003年第24期36卷 221-226页

作者： Marko Mattila Kari Koskinen Kari Saloheimo Information and Computer Systems in Automation Helsinki University of Technology P.O.Box 5400 FIN-02015 HUT Finland Outokumpu Mintec Oy P.O.Box 84 FIN-02201 Espoo Finland

The key measurement instruments in the control of the base metal flotation are the on-line elemental analyzers like the Courier X-ray fluorescence analyzer. The calibration of the analyser is one of the key factors in the operation of the X-ray fluorescence analyser. The quality of the analyses is limited by the quality of the calibration of the assay calculation model. The co-linearity of the measurement information is problematic for the multi-linear regression used in the assay calculation and calibration. In this study two new regression methods are introduced for the assay calculation and calibration: Principal Component Regression and Partial Least Squares regression. These methods utilize the idea of internal structure of data - latent structure that handles the co-linearity internally.

关键词： PCR PLS MLR X-ray Analyzer

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Situation detection based on knowledge fusion patterns 2

Situation detection based on knowledge fusion patterns

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2nd International Workshop on Ontologies and information systems, WOIS 2014

作者： Levashova, Tatiana Sandkuhl, Kurt Shilov, Nikolay Smirnov, Alexander University of Rostock Institute of Computer Science Department Business Information Systems Albert-Einstein-Str. 22 Rostock18059 Germany St.Petersburg Institute for Informatics and Automation Russian Academy of Sciences 39 14 Line St-Petersburg199178 Russia ITMO University 49 Kronverkskiy pr. St-Petersburg197101 Russia

With the increasing use of sensors and actuators in technical systems and knowledge-intensive services the need for processing the information captured by these sensors and "making sense" out of it increases. Knowledge fusion is supposed to contribute to this field since it aims at integrating knowledge from different sources. Development of knowledge fusion solutions is a complex task which can be compared to systems and software development. As in other development areas there is a need for efficient development processes which can be supported by reusing solution parts, such as patterns or components. The paper brings together experiences from knowledge fusion subsystem development and from design of knowledge fusion patterns. The main contributions of this paper are (1) a real-world application scenario presenting typical requirements to knowledge fusion systems, (2) application of knowledge fusion patterns from context-based decision support to situation recognition, (3) recommendations from this application case.

关键词： Software design

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Composite systems control by combined math-analytical and FS/NN computing approach 3

Composite systems control by combined math-analytical and FS...

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3rd IFAC Workshop on Automatic systems for Building the Infrastructure in Developing Countries, DECOM-TT 2003

作者： Dimirovski, Georgi M. Jing, Yuan-Wei Zhao, Jun Zhang, Si-Ying Dogus University Department of Computer Engineering Faculty of Engineering Acibadem Kadiköy IstanbulTR-34722 Turkey SS Cyril and Methodius University Institute of Automation and Systems Eng Faculty of Electrical Engineering SkopjeMK-1000 Macedonia Northeastern University Institute of Control Science School of Information Science and Eng. Shenyang Liaoning China

A control systems engineering approach, employing a two-level overall system architecture and different but compatible formalisms for system representation on the upper and lower levels, has been investigated in detail. One design alternative is based on employing fuzzy-system approximators and solving for the adaptive tracking of the given, arbitrary, desired system outputs. The other alternative is based on state equations of composite systems and the use of neural-network approximators to deal with uncertainties and control adaptation. In both alternatives similarity property of subsystems has been exploited. Both designs can be implemented within the standard computer process control technology, and are therefore believed to be promising in applied systems engineering. Copyright © 2003 IFAC.

关键词： Fuzzy systems

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Analysis of outage probability for opportunistic decode-and-forward relaying network over asymmetric fading channels

Analysis of outage probability for opportunistic decode-and-...

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3rd International Conference on Ubiquitous and Future Networks, ICUFN 2011

作者： Majhi, Sudhan Qian, Hua Xiang, Weidong Addepalli, Sateesh Gao, Zhenguo School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China Electrical and Computer Engineering University of Michigan Dearborn United States Advanced Architecture and Research Cisco Systems Inc. United States College of Automation Harbin Engineering University Harbin China

ISBN: (纸本)9781457711763

This paper analyzes the outage probability of opportunistic decode-and-forward relaying network over asymmetric fading channels where cooperative nodes are located at distributed locations and signals experience asymmetric and independent Rayleigh and Rician channels. The outage probability of such a wireless relaying network is theoretically derived in high SNR scenarios, which is thereafter verified through Monte-Carlo simulations. It is shown that the variation of the outage probability of a relaying network under different asymmetric channels is independent to the number of relay nodes. © 2011 IEEE.

关键词： Fading channels

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ICSGD-Momentum: SGD Momentum based on Inter-gradient Collision 22

ICSGD-Momentum: SGD Momentum based on Inter-gradient Collisi...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zou, Weidong Cao, Weipeng Xia, Yuanqing Zhong, Bineng Li, Dachuan School of Automation Beijing Institute of Technology Beijing China Shenzhen China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9798331527471

Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ResNet and DenseNet, due to its simplicity and effectiveness. However, its slow convergence rate limits its use. To overcome this, we introduce inter-gradient collision into SGD-Momentum, inspired by the elastic collision model in physics. This new method, called ICSGD-Momentum, aims to improve convergence. We provide theoretical proof of convergence and establish a regret bound for ICSGD-Momentum. Experiments on benchmarks including function optimization, CIFAR-100, ImageNet, Penn Treebank, COCO, and YCB-Video show that ICSGD-Momentum accelerates training and enhances the generalization performance of DNNs compared to optimizers like SGD-Momentum, Adam, RAdam, Adabound, and AdaBelief. © 2024 IEEE.

关键词： Deep neural networks

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Data-Based Condition Monitoring of an X-Ray Analyzer

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IFAC Proceedings Volumes 2001年第18期34卷 89-94页

作者： Marko Mattila Heikki Hyötyniemi Kari Koskineo Kari Saloheimo Information and Computer Systems in Automation Helsinki University of Technology P.O. Box 5400 FIN-02015 HUT Finland Outokumpu Mintec Oy P.O. Box 84 FIN-02201 Espoo Finland

Fault diagnosis and condition monitoring can be based on analytical process models, explicit knowledge or direct process measurements. When the structure of the process is unknown, the data-based approach based on the measurements is the most practical one. This is the case also with X-ray fluorescence analyzers. Appropriate analyzer calibration is a key factor in obtaining the best performance from the analysis system. As the mineralogy of the processed material is slowly changing the calibration must be maintained over time. The possible indicators for the calibration validity are studied in this paper.

关键词： calibration condition monitoring dynamic modeling fault diagnosis MLR PCA PLS subspace identification

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Transfer Learning for Russian Handwriting Recognition 46

Transfer Learning for Russian Handwriting Recognition

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Levkov, Andrey Kaplun, Dmitry Safonova, Anastasiia St. Petersburg Electrotechnical University "leti" Depart. of Computer Engineering St. Petersburg Russia St. Petersburg Electrotechnical University "leti" Depart. of Automation and Control Processes St. Petersburg Russia St. Petersburg Electrotechnical University "leti" Depart. of Information Systems St. Petersburg Russia

ISBN: (纸本)9798350303964

Handwriting recognition is one of the most important tasks in computer vision. Despite the variety of methods and algorithms used for text recognition in various languages, the recognition of handwritten Russian words is still a completely unsolved problem, which keeps the relevance of this topic. This paper proposes the application of a convolutional neural network (CNN) to the task of recognizing the 100 most frequently used Russian words, trained on a new unique dataset. A unique algorithm for extracting Russian handwritten words from forms was applied to obtain a new dataset. The paper also presents the results of a comparison of such powerful models as VGG-16, VGG-19, ResNet-50, and ResNet-101, which were trained using transfer learning technology. The best results were shown by VGG-16 with 98.7% accuracy, and the worst results were shown by ResNet-50 with 80% accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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An Adaptive-Gain Recurrent Neural Network for Solving the Joint-Angle Drift Issues of Redundant Manipulators

An Adaptive-Gain Recurrent Neural Network for Solving the Jo...

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2023 International Annual Conference on Complex systems and Intelligent Science, CSIS-IAC 2023

作者： Zheng, Boyu Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. School of Information Engineering Nanchang University Nanchang330031 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Engineering Peking University Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

ISBN: (纸本)9798350309003

This paper proposes a new adaptive-gain recurrent neural network (AG-RNN) to effectively cope with the joint-angle drift issues in redundant manipulators. Specifically, a joint-angle drift-free with the feedback control of the velocity layer motion equation (JADF-FC) is proposed via an optimization criterion for synchronously optimizing linear terms and quadratic. Then, the JADF-FC is reasonably formulated into a standard quadratic programming (QP) issue. Different from the previous recurrent neural networks (RNNs), the AG-RNN proposed in this paper constructs an error-based differential equation with a new adaptive-gain. It should be noted that the proposed adaptive-gain does not gradually approach infinity as time increases, which is more in line with actual hardware implementation requirements than the existing time-variant-gain. The adaptive-gain can reduce the joint-angle drift errors of the redundant manipulator. Therefore, the proposed AG-RNN can solve the QP problem of the manipulator more effectively and quickly. To validate the performance of the proposed AG-RNN, it is compared with representative RNNs. The experimental results indicate that smaller joint-angle drift errors can be get by the proposed AG-RNN solving JADF-FC scheme than the other solutions when solving the joint-angle drift issues. © 2023 IEEE.

关键词： Quadratic programming

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Frequency Domain Identification of the MIT Interferometer Testbed in Generalized Orthogonal Basis

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IFAC Proceedings Volumes 1997年第11期30卷 1631-1635页

作者： J. Bokor M. Athans Systems and Control Laboratory Computer and Automation Institute Hungarian Academy of Sciences Budapest XI. Kende u. 13-17. H-1111 Hungary Laboratory for Information and Decision systems Massachusetts Institute of Technology Cambridge MA 02139 USA

This paper presents a new approach for approximate identification under the H 2 criterion where the model is parametrized in a basis generated by apriori knowledge on the location of system poles. A frequency domain i... 详细信息

关键词： Generalized Fourier series Generalized othogonal basis Lightly damped structures H 2 -identification

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