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Direct data-driven LPV control of nonlinear systems: An experimental result

IFAC-PapersOnLine 2023年第2期56卷 2263-2268页

作者： Chris Verhoek Hossam S. Abbas Roland Tóth Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology 5600MB Eindhoven The Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck 23558 Lübeck Germany Systems and Control Lab Institute for Computer Science and Control 1111 Budapest Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points.

关键词： Data-Driven Control Linear Parameter-Varying systems

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Model for Anomaly Detection in Reducing Water Losses in the Distribution Network of Peru using Machine Learning Algorithms

Model for Anomaly Detection in Reducing Water Losses in the ...

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Software engineering and computer Science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies

Towards Designing a Medication Assistive Intelligent System ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

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High-Impedance Non-Linear Fault Detection via Eigenvalue Analysis with low PMU Sampling Rates

arXiv

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arXiv 2023年

作者： Paramo, Gian Bretas, Arturo Meyn, Sean Electrical and Computer Engineering University of Florida GainesvilleFL United States Distributed Systems Group Pacifc Northwest National Laboratory RichlandWA United States

This work presents a hybrid data-driven and physics-based framework for high-impedance fault detection in power systems. An innovative method based on eigenvalue analysis is expanded and validated. Phasor Measurement Unit data is used to estimate eigenvalues corresponding to the powerlines being monitored. Eigenvalue statistics are then tracked and evaluated. Faults are detected as they drive eigenvalues outside of their normal zones. This technique holds several advantages over contemporary techniques: It utilizes technology that is already deployed in the field, it offers a significant degree of generality, and so far it has displayed a very high-level of sensitivity without sacrificing accuracy. Validation is performed in the form of simulations based in the IEEE 13 Node System and non-linear fault models. Test results are encouraging, indicating potential for real-life applications. © 2023, CC BY.

关键词： Phasor measurement units

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WASTEWATER TREATMENT IN UAE USING GEOTHERMAL ENERGY 50

WASTEWATER TREATMENT IN UAE USING GEOTHERMAL ENERGY

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50th International Conference on computers and Industrial engineering: Sustainable Digital Transformation, CIE 2023

作者： El Haj Assad, Mamdouh Almallahi, Maryam Aryanfar, Yashar Rejeb, Oussama Elgendi, Mahmoud Department of Sustainable and Renewable Energy Engineering University of Sharjah Sharjah United Arab Emirates Department of Mechanical Engineering and Aerospace United Arab Emirates University Alain United Arab Emirates Department of Electric Engineering and Computer Sciences Universidad Autonoma de Ciudad Juarez Ciudad Juarez Mexico Sustainable Energy Development Research Group Sustainable Energy and Power Systems Research Center University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781713886952

Geothermal energy is the best natural, renewable resource through which wastewater can be treated and reused, serving many lives. This study discusses a proposed plan using geothermal energy to treat wastewater in the ORC system. Primary geothermal resources are waterrelated, with a temperature above 120 °C. In UAE, examined the areas through the magnetic survey in Al Ain and Ras AlKhaimah with 120 °C temperature in water samples collected from 3km depth from which 1000 megawatts of electricity can be produced to give off the supply to many residential houses. The temperature rises to 150 °C as we move deeper than 3 km underground. The same sources can be extracted through the planning system and utilized for wastewater treatment. Production well distributed the geo-fluid into a fraction that generates desiccant flow for dryer and remaining feed a small-scale ORC system. The installation of an auxiliary boiler is managed in the absence of a geothermal system. However, electricity is produced from the ORC system, used to supply wastewater to a treatment plant, and pumps the geo-fluid possible with a higher temperature than 150 °C. A thermal dryer and drying processes are used, and a global energy efficiency test is done. Inlet and outlet temperatures were managed, with 5 and 10 °C differences. The efficiency and economic status of the thermal and electric systems were observed and compared briefly in this study. The moisture removal of the sludge is above 90% in the design, as the outlet water content of the sludge is less than 10%. It reduces the mass of sludge by about 73.2%. In the Organic Rankine cycle, 150kW of electricity can be produced that will be used to pump geofluid in the drying section of the sludge and wastewater treatment plant. This can be an excellent alternative for energy efficiency and greenhouse gas reduction from wastewater treatment plants in the UAE. © 2023 computers and Industrial engineering. All rights reserved.

关键词： Energy efficiency

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Three-stage data generation algorithm for multiclass network intrusion detection with highly imbalanced dataset

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International Journal of Intelligent Networks 2023年第1期4卷 202-210页

作者： Chui, Kwok Tai Gupta, Brij B. Chaurasia, Priyanka Arya, Varsha Almomani, Ammar Alhalabi, Wadee Department of Electronic Engineering and Computer Science School of Science and Technology Hong Kong Metropolitan University Hong Kong Taichung 413 Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Computing Engineering and Intelligent Systems Ulster University United Kingdom Department of Business Administration Asia University Taiwan School of Information Technology Skyline University College SharjahP.O. Box 1797 United Arab Emirates Al- Balqa Applied University Jordan Immersive Virtual Reality Research Group King Abdulaziz University Jeddah21589 Saudi Arabia Symbiosis International University Pune India Lebanese American University Beirut1102 Lebanon Birkbeck University of London United Kingdom Chandigarh University Chandigarh India

The Internet plays a crucial role in our daily routines. Ensuring cybersecurity to Internet users will provide a safe online environment. Automatic network intrusion detection (NID) using machine learning algorithms has recently received increased attention recently. The NID model is prone to bias towards the classes with more training samples due to highly imbalanced datasets across different types of attacks. The challenge in generating additional training data for minority classes is the generation of insufficient data. The study's purpose is to address this challenge, which extends the data generation ability by proposing a three-stage data generation algorithm using the synthetic minority over-sampling technique, a generative adversarial network (GAN), and a variational autoencoder. A convolutional neural network is employed to extract the representative features from the data, which were fed into a support vector machine with a customised kernel function. An ablation study evaluated the effectiveness of the three-stage data generation, feature extraction, and customised kernel. This was followed by a performance comparison between our study and existing studies. The findings revealed that the proposed NID model achieved an accuracy of 91.9%–96.2% in the four benchmark datasets. In addition, it outperformed existing methods such as GAN-based deep neural networks, conditional Wasserstein GAN-based stacked autoencoder, synthesised minority oversampling technique-based random forest, and variational autoencoder-based deep neural network, by 1.51%–28.4%. © 2023 The Authors

关键词： Support vector machines

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On the Design of the MITLL Trimodal Dataset for Identity Verification

On the Design of the MITLL Trimodal Dataset for Identity Ver...

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International Workshop on Biometrics and Forensics (IWBF)

作者： Elliot Singer Bengt J. Borgström Kenneth Alperin Trang Nguyen Cagri Dagli Melissa Dale Arun Ross MIT Lincoln Laboratory Artificial Intelligence Technology and Systems Group Lexington MA Computer Science and Engineering Department Michigan State University East Lansing MI

The recent advances in deep learning have led to an increased interest in the development of techniques for multimodal identity verification applications, particularly in the area of biometric fusion. Associated with these efforts is a corresponding need for large scale multimodal datasets to provide the bases for establishing performance baselines for proposed approaches. After examining the characteristics of existing multimodal datasets, this paper will describe the development of the MITLL Trimodal dataset, a new triple-modality collection of data comprising parallel samples of audio, image, and text for 553 subjects. The dataset is formed from YouTube videos and Twitter tweets. Baseline single modality results using a common processing pipeline are presented, along with the results of applying a conventional fusion algorithm to the individual stream scores.

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State Derivative Normalization for Continuous-Time Deep Neural Networks

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IFAC-PapersOnLine 2024年第15期58卷 253-258页

作者： Jonas Weigand Gerben I. Beintema Jonas Ulmen Daniel Görges Roland Tóth Maarten Schoukens Martin Ruskowski Chair of Machine Tools and Control Systems RPTU Kaiserslautern and the German Research Center for Artificial Intelligence Kaiserslautern Germany Control Systems (CS) Group at the Department of Electrical Engineering Eindhoven University of Technology Netherlands. R. Tóth is also affiliated to the Systems and Control Laboratory at the Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.

关键词： System Identification Continuous-Time Neural Ordinary Differential Equations Nonlinear State-Space

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Discovering Structural Errors From Business Process Event Logs (Extended Abstract)

Discovering Structural Errors From Business Process Event Lo...

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International Conference on Data engineering

作者： Wei Song Zhen Chang Hans-Arno Jacobsen Pengcheng Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Middleware Systems Research Group University of Toronto Toronto Canada College of Computer and Information Hohai University Nanjing China

While process mining has gained much attention in the past decade, surprisingly, discovering structural errors (i.e., deadlock and lack of synchronization) from event logs has seldom been studied. Since event logs may involve erroneous event occurrences caused by unsynchronized activities, discovering deadlocks and lack of synchronization errors may influence each other. To this end, we first extract from the original event log two independent event logs which are employed to discover deadlocks and lack of synchronization errors, respectively. We then discard the erroneous event occurrences in the two event logs, from which our event relation based mining rules can discover the corresponding structural errors. We have implemented our approach, and the experimental results corroborate that our approach can effectively and efficiently discover process structural errors from the event logs involving sufficient event sequences.

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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