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Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit

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arXiv 2022年

作者： Chen, Weibin Sharifrazi, Danial Liang, Guoxi Band, Shahab S. Chau, Kwok Wing Mosavi, Amir College of Computer Science and Artificial Intelligence Wenzhou University Zhejiang Wenzhou325035 China Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Artificial Intelligence Wenzhou Polytechnic Wenzhou325035 China Future Technology Research Center College of Future National Yunlin University of Science and Technology Yunlin County Taiwan Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong John von Neumann Faculty of Informatics Obuda University Budapest Hungary Institute of Information Engineering Automation and Mathematics Slovak University of Technology Bratislava Slovakia

Streamlined weirs, as a nature-inspired type of weirs, has gained tremendous attention among hydraulic engineers mainly due to their well-known performance with high discharge coefficient. Computational fluid dynamic (CDF) is considered as a robust tool to predict discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modelling techniques, as an alternative to CFD simulation, to predict discharge coefficient based on an experimental dataset. To this end, after splitting the dataset by k-fold cross-validation technique, the performance assessment of classical and hybrid machine-deep learning (ML-DL) algorithms is undertaken. Amongst ML techniques, linear regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and decision tree (DT) algorithms are studied . In the context of DL, long short-term memory (LSTM), convolutional neural network (CNN), gated recurrent unit (GRU) and their hybrid forms such as LSTM-GRU, CNN-LSTM and CNN-GRU techniques are compared by different error metrics. It is found that the proposed three-layer hierarchical DL algorithm consisting of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized by LR method (i.e., LR-CGRU), leads to lower error metrics. This paper paves the way for data-driven modelling of streamlined *** Codes 68T05 © 2022, CC BY.

关键词： Deep neural networks

Integration of ANN for Accurate Estimation and Control in Wastewater Treatment

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Integration of ANN for Accurate Estimation and Control in Wa...

International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Andreea Elena Țîru Iulian Vasiliev Larisa Diaconu Ramon Vilanova Daniel Voipan Harsha Ratnaweera Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galaţi Galaţi Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain Department of Computer Science and Information Technology "Dunărea de Jos" University of Galaţi Galaţi Romania Faculty of Science and Technology (Realtek) Norwegian University of Life Sciences Ås Norway

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

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Rotation Speed and Wind Speed Indirect Measurement Methods for the Control of Windmills with Fixed Blades Turbine

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Rotation Speed and Wind Speed Indirect Measurement Methods f...

2008 IEEE Instrumentation and Measurement Technology Conference (I2MTC 2008), vol.3

作者： N. Budisan V. Groza O. Prostean I. Filip M. Biriescu I. Szeidert M. Stern Faculty of Automation and Computer Science and Engineering Polytechnic University of Timisoara Timisoara Romania School of Information Technology and Engineering (SITE) University of Ottawa Ottawa ONT Canada Cleanfield Energy Corporation Ancaster ONT Canada

The implementation complexity of classical algorithms for wind turbines control limits the utilization of low-end microcontrollers for such applications. We conceived an original method that aims to optimize the efficiency of windmills with fixed blades with the variation of the wind speed. In the paper we present an original method to determine generator's efficiency values using measurements that were performed at the prototype's homologation, along with original simplified models of wind turbine and of wind turbine - generator assembly. Measured values of the frequency, voltage and current of the generator are applied to determine the turbine rotation speed and the wind speed, reducing thus the number of needed sensors. A couple of families of regression functions were derived and tuned accordingly to the factory experimental characteristics to allow for a facile calculation of the generator's efficiency database table. To further speedup the assessment of the optimum/safety value of the windmill rotation speed, the generator's model is considered to provide the rotation as a function of the wind speed in a tabular format that is further used at run-time as a look-table for rapid interpolations in the control loop. The main advantages of the proposed approach are the simplicity and robustness of the implementation, being easily portable to low-end microcontroller platforms.

关键词： indirect measurement methods renewable energy fixed blades turbine windmills control

Optimal Pricing Strategies Based on Time Delay in Multi-Service Networks

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IFAC Proceedings Volumes 2001年第3期34卷 105-110页

作者： Fude Jin Yuanwei Jing Zhianhua Zhou G.M. Dimirovske K. Sohraby Institute of Control Science School of Information Science & Engineering Northeastern University Shenyang Liaoning 110006 P.R. of CHINA +86-24/2389-3138 Inst. of Automation & Systems Engg. Faculty of EE SSs. Cyril & Methodius University Karpos 2 B.B. P.D.Box 574:MK-1000 Skopje Rep.of Macedonia Fax# +90-216/327-9631 Dept. of Computer Science & Telecommunications University of Missouri-Kansas Kansas City M064110 USA

The problem of the pricing equilibrium in multi-service priority-based networks isstudied by using Stackelberg game theory. Some concepts of the game theory were revisited first. Then, the existing results on two-user, twolevel Nash problem was reviewed briefly. Following this background, a new one-leader, two-level, two-user incentive Stackelberg strategy was derived and proved by employing the time delay involved.

关键词： Communication networks game theory incentives optimal systems priority Stackelberg game

On a vectorized basic linear algebra package for prototyping codes in MATLAB

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arXiv 2024年

作者： Moskovka, Alexej Rahman, Talal Valdman, Jan Vatne, Jon Eivind Department of Mathematics Faculty of Applied Sciences University of West Bohemia Technická 8 Pilsen30100 Czech Republic Faculty of Engineering and Science Western Norway University of Applied Sciences Inndalsveien 28 Bergen5063 Norway Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 Č. Budějovice37005 Czech Republic Department of Economics BI Norwegian Business School Kong Christian Frederiks plass 5 Bergen5006 Norway The Czech Academy of Sciences Institute of Information Theory and Automation Pod Vodárenskou věží 4 Prague18208 Czech Republic

When writing a high-performance code for numerical computation in a scripting language like MATLAB, it is crucial to have the operations in a large for-loop vectorized. If not, the code becomes too slow to be of any use, even for a moderately large problem. However, in the process of vectorizing, it often happens that the code loses its original structure and becomes less readable. This is particularly true in the case of a finite element implementation, even though finite element methods are inherently structured. A basic remedy to this is the separation of the vectorization part from the mathematics part in the code, which is easily achieved through building the code on top of the basic linear algebra subprograms that are already vectorized codes, an idea which has been used in a series of papers over the last fifteen years, developing codes that are fast and still structured and readable. We discuss the vectorized basic linear algebra package, and introduce a formalism using multi-linear algebra to explain and define formally the functions in the package, as well as MATLAB’s pagetime functions. We provide examples from computations of varying complexity, including the computation of normal vectors, volumes, and finite element methods. Benchmarking shows that we also get fast computations. Using the library, we can write codes that closely follow our mathematical thinking, making it easier to write, follow, reuse, and extend the code. © 2024, CC BY.

关键词： MATLAB

Proceedings of the 2016 International Conference on Image Pr...

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Foreword

Proceedings of the 2016 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

Large margin multi-modal multi-task feature extraction for image classification

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arXiv 2019年

作者： Luo, Yong Wen, Yonggang Tao, Dacheng Gui, Jie Xu, Chao School of Electronics Engineering and Computer Science Peking University Beijing China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Centre for Quantum Computation & Intelligent Systems Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We therefore propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on two challenging real-world image datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

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arXiv 2023年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Liu, Zhiwei Wu, Jia Yu, Zhengtao Yu, Philip S. The Institute of Information Engineering Chinese Academy of Sciences the School of Cyber Security University of Chinese Academy of Sciences Beijing China The School of Cyber Science and Technology Beihang University Beijing1000191 China Salesforce AI Research CA94301 United States The School of Computing Macquarie University Sydney Australia The Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China The Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCLSED, and its variant - UCL-ECSED, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012_100, Events2018_100, and CrisisLexT_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

Effects of carbon dioxide on different PSK modulation formats of Optical Wireless Communications using USRP

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Effects of carbon dioxide on different PSK modulation format...

International Conference on Transparent Optical Networks

作者： Jan Vitasek Radek Martinek Ales Vanderka Lukas Hajek Jan Latal Zhenli Lu Bin Li Department of Telecommunications VSB-Technical University of Ostrava Ostrava Czech Republic Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava Czech Republic Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 15 708 33 Czech Republic State key Laboratoryof Robotics Chinese Academy of Sciences Shenyang China School of Electrical Engineering and Automation Changshu Instituteof Technology Changshu China

ISBN: (纸本)9781509014682

This article explores the influence of the concentrated carbon dioxide (CO 2 ) gas on the different Phase Shift Keying (PSK) modulations used for Optical Wireless Communication (OWC) links. In this work, the OWC consisted of a laser diode, a photodetector, and a transmission medium. The Universal Software Radio Peripheral (USRP) unit created and evaluated the PSK modulations. This unit modulated the laser diode on the transmitter side and demodulated the data which were received by the photodetector on the receiver side. The CO 2 gas was emitted as a consequence of sublimation of the solid carbon dioxide in hot water. The measurements were done in a closed box in laboratory. The CO 2 gas is a one element of smog which pollutes cities now and then. Our experimental results with CO 2 gas could help to choose a suitable modulation scheme for OWC which are installed in cities with smog-polluted atmospheric conditions.

关键词： Phase shift keying Carbon dioxide Optical receivers Diode lasers Integrated optics Optical transmitters