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Complex Event Processing on Linked Stream Data

Datenbank-Spektrum 2015年第2期15卷 119-129页

作者： Saleh, Omran Hagedorn, Stefan Sattler, Kai-Uwe Department of Computer Science and Automation Database and Information Systems Group Technische Universität Ilmenau Ilmenau Germany

Social networks and Sensor Web technologies typically generate a massive amount of data published as streams. In order to give these streams a meaningful sense and enrich them with semantic descriptions, the concept of Linked Stream Data (LSD) has emerged. However, to support a wide range of LSD scenarios and queries comprehensive solutions providing not only classic data stream operators such as windows, but also for processing of complex events, linking of (static) datasets, and scalable processing are required. In this paper, we present our approach for processing LSD and addressing these requirements. In contrast to existing LSD engines relying on streaming extensions to SPARQL, our PipeFlow system is a (relational) dataflow language and engine providing support for complex event processing (CEP) and a few dedicated operators for RDF data. We describe this language and particularly the CEP model as well as the system architecture for parallel CEP and LSD processing by exploiting partitioning techniques for cluster environments. Finally, we report results from experiments evaluating our system in comparison to existing LSD engines. © 2015, Springer-Verlag Berlin Heidelberg.

关键词： Engines

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Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification

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IEEE Transactions on Neural Networks and Learning systems 2025年第5期36卷 8166-8177页

作者： Zhang, Zhijun He, Yu Mai, Weijian Luo, Yamei Li, Xiaoli Cheng, Yuanxiong Huang, Xiaoming Lin, Run South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Guangdong Second Provincial General Hospital Department of Pulmonary and Critical Care Medicine Guangzhou518025 China Sun Yat-sen Memorial Hospital Otolaryngology-Head and Neck Surgery Guangzhou510120 China Sun Yat-sen University The First Affiliated Hospital Department of Radiology Guangdong Guangzhou510080 China

The brain signal classification is the basis for the implementation of brain–computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manual intervention, such as channel selection and dimensionality reduction, and often struggle to achieve satisfactory classification accuracy. To achieve high classification accuracy and as little manual intervention as possible, a convolutional dynamically convergent differential neural network (ConvDCDNN) is proposed for solving the electroencephalography (EEG) signal classification problem. First, a single-layer convolutional neural network is used to replace the preprocessing steps in previous work. Then, focal loss is used to overcome the imbalance in the dataset. After that, a novel automatic dynamic convergence learning (ADCL) algorithm is proposed and proved for training neural networks. Experimental results on the BCI Competition 2003, BCI Competition III A, and BCI Competition III B datasets demonstrate that the proposed ConvDCDNN framework achieved state-of-the-art performance with accuracies of 100%, 99%, and 98%, respectively. In addition, the proposed algorithm exhibits a higher information transfer rate (ITR) compared with current algorithms. © 2012 IEEE.

关键词： Convolutional neural networks

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Strategic Robust Mixed Model Assembly Line Balancing Based on Scenario Planning

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Tsinghua Science and Technology 2011年第3期16卷 308-314页

作者：徐炜达肖田元 National Laboratory for Information Science and Technology National Computer Integrated Manufacturing Systems Engineering Research CenterDepartment of AutomationTsinghua University

Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.

关键词： mixed model assembly line balancing robust scenario planning genetic algorithm

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Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria

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Scientific Reports 2025年第1期15卷 1-16页

作者： Ladjal, Boumediene Nadour, Mohamed Bechouat, Mohcene Hadroug, Nadji Sedraoui, Moussa Rabehi, Abdelaziz Guermoui, Mawloud Agajie, Takele Ferede Department of Automation and Electromechanics Faculty of Science and Technology University of Ghardaïa Ghardaïa Algeria Applied Automation and Industrial Diagnostics Laboratory (LAADI) Faculty of Sciences and Technology University of Djelfa Djelfa 17000 Algeria Laboratory of Inverse Problems Modeling Information and Systems (PI:MIS) University 8 Mai 1945 Guelma Algeria Telecommunications and Smart Systems Laboratory University of Djelfa PO Box 3117 Djelfa 17000 Algeria Centre de Développement des Energies Renouvelables Unité de Recherche Appliquée en Energies Renouvelables URAER CDER Ghardaïa 47133 Algeria Department of Electrical and Computer Engineering Faculty of Technology Debre Markos University Debre Markos 269 Ethiopia

This paper provides an in-depth analysis and performance evaluation of four Solar Radiance (SR) prediction models. The prediction is ensured for a period ranging from a few hours to several days of the year. These models are derived from four machine learning methods, namely the Feed-forward Back Propagation (FFBP) method, Convolutional Feed-forward Back Propagation (CFBP) method, Support Vector Regression (SVR), and the hybrid deep learning (DL) method, which combines Convolutional Neural Networks and Long Short-Term Memory networks. This combination results in the CNN-LSTM model. Additionally, statistical indicators use Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Normalized Root Mean Squared Error (nRMSE). Each indicator compares the predicted output by each model above and the actual output, pre-recorded in the experimental trial. The experimental results consistently show the power of the CNN-LSTM model compared to the remaining models in terms of accuracy and reliability. This is due to its lower error rate and higher detection coefficient (R² = 0.99925). © The Author(s) 2025.

关键词： Artificial neural networks Convolutional feed-forward back propagation Convolutional neural network Deep learning Feed-forward back propagation Long short-term memory Solar radiance forecasting

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Data-driven modeling of hysteresis in ReRAM devices: A multifactorial approach for enhanced memory performance 35

Data-driven modeling of hysteresis in ReRAM devices: A multi...

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35th GI-Workshop on Foundations of Database systems, GvDB 2024

作者： Zamankhani, Saba TU Ilmenau Department of Computer Science and Automation Databases and Information Systems Group Germany

Many physical systems in the real world exhibit complex behavior, making it difficult to identify their dynamics. To overcome this problem, prototypes are created to provide a better insight into the behavior of such systems. However, building these prototypes using traditional methods is often slow due to the computational intensity required to accurately capture the detailed complexities within these systems and the large parameter space that needs to be explored. Data-driven approaches, such as machine learning and deep learning frameworks, have the potential to significantly speed up this process by generalizing the model. In this study, we explore the use of such an approach to understand the complex and non-linear behavior of dynamical systems, using resistive random access memory (ReRAM) devices as a case study. It is important to emphasize that modeling ReRAM is only one specific example in our wider investigation, which aims to design predictive models for a range of non-linear dynamical systems. Our work attempts to overcome the limitations of traditional research and development by using neural networks to reproduce the complex behavior of ReRAM cells accurately. We introduce a hybrid dual-input neural network architecture (HDiNN), equipped with a custom loss function, to capture both spatial and temporal patterns, improving the predictability of cell behavior under different conditions. This involves integrating important factors such as material properties, device geometry, and electrical interactions into our model to explain the complexities of ReRAM technology. However, our ambitions extend far beyond ReRAM to develop methods to help create innovative, durable solutions in various fields. This study highlights the impact of predictive modeling in advancing materials science and demonstrates the transformative potential of neural networks in improving the design and optimization of future technologies. © 2024 Copyright for this paper by its authors.

关键词： Neural networks

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Expectation maximization for average reward decentralized POMDPs

Expectation maximization for average reward decentralized PO...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013

作者： Pajarinen, Joni Peltonen, Jaakko Department of Automation and Systems Technology Aalto University Finland Department of Information and Computer Science Aalto University Finland

ISBN: (纸本)9783642409875

Planning for multiple agents under uncertainty is often based on decentralized partially observable Markov decision processes (Dec-POMDPs), but current methods must de-emphasize long-term effects of actions by a discount factor. In tasks like wireless networking, agents are evaluated by average performance over time, both short and long-term effects of actions are crucial, and discounting based solutions can perform poorly. We show that under a common set of conditions expectation maximization (EM) for average reward Dec-POMDPs is stuck in a local optimum. We introduce a new average reward EM method;it outperforms a state of the art discounted-reward Dec-POMDP method in experiments. © 2013 Springer-Verlag.

关键词： Multi agent systems

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Algorithmic realization of system for short-term weather forecasting 07

Algorithmic realization of system for short-term weather for...

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International Conference on computer systems and Technologies and Workshop for PhD Students in Computing, CompSysTech '07

作者： Simeonov, Ivan Kilifarev, Hristo Ilarionov, Rajcho Department Computer Systems and Technologies Technical University of Gabrovo Bulgaria Department of Automation Information and Control Systems Technical University of Gabrovo Bulgaria

ISBN: (纸本)9789549641509

In this paper is presented algorithmic realization of system for short-term weather forecasting, which makes acquisition, processing and visualization of information, related to the parameters temperature, atmospheric pressure, humidity, wind speed and direction. The proposed algorithm is defined from the juxtaposition of the collected statistical data with the currently measured data, on the chosen methods for weather forecasting and the determined correlation between the processed meteorological quantities. The choice of the methods for weather prediction is made according the needed input data and the resources for their realization. © 2007 ACM.

关键词： Weather forecasting

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DCFNet:Discriminant Correlation Filters Network for Visual Tracking

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Journal of computer Science & Technology 2024年第3期39卷 691-714页

作者：胡卫明王强高晋李兵 Stephen Maybank State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences Beijing 100190China Department of Computer Science and Information Systems Birkbeck CollegeLondon WC1E 7HXU.K.

CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter based trackers can update the model parameters online in real *** this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter *** correlation filter can be efficiently updated *** previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position *** combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for *** adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual *** back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the *** evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.

关键词： correlation filter convolutional neural network(CNN) visual tracking

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Improvement and optimization of an embedded system for short-time weather forecasting 08

Improvement and optimization of an embedded system for short...

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9th International Conference on computer systems and Technologies and Workshop for PhD Students in Computing, CompSysTech'08

作者： Kilifarev, Hristo Simeonov, Ivan Ilarionov, Raycho Department of Automation Information and Control Systems Technical University of Gabrovo Bulgaria Department Computer Systems and Technologies Technical University of Gabrovo Bulgaria Department of Computer Systems and Technologies Technical University of Gabrovo Bulgaria

ISBN: (纸本)9789549641523

In this paper is presented the improvement of the hardware and the optimization of the algorithm of a system for short-time weather forecasting, developed by the authors. With a purpose of reading of more parameters there are proposed some solutions, related with giving of more accurate forecasts. The features of the system are extended and by means of Wireless communication and access to Internet for connection with other similar systems and/or Internet servers for meteorological information. The algorithm of work of the system is optimized as for base methods for prediction are accepted the Trends and Persistent methods, and the read of the surface effects are reduced to the influence of the wind, the humidity and the clouds cover. This leads to reducing of the requirements for processing resources of the used microcontroller and the volume of the processed statistical data. Under account are taken some modern methods for atmosphere sounding, and by them to increase the accuracy and to extend the range of the given forecasts.

关键词： Weather forecasting

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Modeling and Measurement of 3D Solenoid Inductor Based on Through-Silicon Vias

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Chinese Journal of Electronics 2023年第2期32卷 365-374页

作者： YIN Xiangkun WANG Fengjuan ZHU Zhangming Vasilis F.Pavlidis LIU Xiaoxian LU Qijun LIU Yang YANG Yintang Shaanxi Key Lab.of Integrated Circuits and Systems School of Microelectronics Xidian University School of Automation and Information Engineering Xi'an University of Technique Department of Computer Science University of Manchester

Through-silicon via(TSV) provides vertical interconnectivity among the stacked dies in three-dimensional integrated circuits(3D ICs) and is a promising option to minimize 3D solenoid inductors for on-chip radio-frequency applications. In this paper, a rigorous analytical inductance model of 3D solenoid inductor is proposed based on the concept of loop and partial inductance. And a series of 3D samples are fabricated on 12-in high-resistivity silicon wafer using low-cost standard CMOS-compatible process. The results of the proposed model match very well with those obtained by simulation and measurement. With this model, the inductance can be estimated accurately and efficiently over a wide range of inductor windings, TSV height, space, and pitch.

关键词： Semiconductor device modeling Inductance Analytical models Solid modeling Three-dimensional displays Integrated circuit modeling Inductors

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