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3rd International Conference on Recent Advances in Mechanical engineering Research and Development, ICRAMERD 2022

作者： Choudhury, Subhashree Das, A.S. Misra, Sarita Hossain, Ismail Dash, Taraprasanna Swain, Kaliprasanna Bhubaneswar India J Systems and Control Cuttack India Department of Electrical Engineering GITA Autonomous College Bhubaneswar India Department of Nuclear Power Plants and Renewable Energy Ural Federal University Yekaterinburg Russia Bhubaneswar India Department of Electronics and Communication Engineering GITA Autonomous College Bhubaneswar India

ISBN: (纸本)9789811994920

Network on Chip (NoC) is a communication subsystem between various IPs interconnected through an on-chip router inside a single chip. The on-chip interconnection infrastructure connects the different intellectual property (IP) with the help of various industry-standard high-performance (HPC) interconnection topologies. Also, the on-chip interconnection network facilitates the connection of more Processing Elements (PE) to enable deadlock-free scheduling and parallel processing. The present work addresses one important aspect of NoC which is "Optimal Mapping" which allows the participation of cores in an optimal way for achieving low bandwidth requirement, low latency hence greater throughput. To achieve the optical mapping, a Genetic Algorithm is proposed here for the optimization of NoC performance by employing 2D Mesh topology. The genetic algorithm produces consistent and comparable results at par with standard algorithms. A deterministic seeding and successive seeding would yield better results and consume less CPU time. The proposed result is marginally better than NMAP and PSMAP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network-on-chip

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On Mikhailov Stability Conditions for a Class of Integer-and Commensurate Fractional-Order Discrete-Time Systems 21st

On Mikhailov Stability Conditions for a Class of Integer-and...

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21st Polish control Conference, PCC 2023

作者： Stanislawski, Rafal Rydel, Marek Department of Electrical Control and Computer Engineering Opole University of Technology ul. Pŕoszkowska 76 Opole45-758 Poland

ISBN: (纸本)9783031351693

The Mikhailov stability condition is a classical stability test for continuous-time systems, similar to the well-known Nyquist method. However, in contrast to the Nyquist criterion, the Mikhailov stability tests do not reach considerable research attention. This paper introduces an extension of Mikhailov stability condition for two classes of a discretetime system in terms of linear time-invariant system, and fractional-order one based on ‘forward-shifted’ Gr¨unwald-Letnikov difference. Simulation experiments confirm the usefulness of the Mikhailov methods for stability analysis of discrete-time systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Continuous time systems

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Research on the Flux-weakening Capability of the Reverse-salient Permanent Magnet Synchronous Motor 26

Research on the Flux-weakening Capability of the Reverse-sal...

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26th International Conference on electrical Machines and Systems, ICEMS 2023

作者： Zhao, Xiaokun Zhang, Cheng Wang, Weinan Kou, Baoquan Zhu, Liangkuan Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Department of Electrical and Automation Harbin China

ISBN: (纸本)9798350317589

Permanent magnet synchronous motors (PMSMs) offer the benefits of high torque density and a superior power factor. Nevertheless, the challenge lies in the inherent difficulty of adjusting the permanent magnet flux. To address this issue, this paper analyzes the influence of electromagnetic parameters on the constant-power speed range (CPSR). First, the analysis focuses on the influence of d-axis inductance and saliency ratio, aiming to establish a design method for achieving a wide CPSP in PMSMs. Second, based on the electromagnetic parameter requirements, a reverse-salient PMSM is proposed and its working principle is explained. Finally, the electromagnetic characteristics, including torque and the CPSP, are determined through the application of the finite element method (FEM). The obtained results confirm that the reverse-salient PMSM achieves a CPSP of 7:1, thus validating the accuracy of the theoretical analysis. © 2023 IEEE.

关键词： Finite element method

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Fault-Tolerant Quantum Computation Using Large Spin-Cat Codes

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PRX Quantum 2024年第2期5卷 020355-020355页

作者： Sivaprasad Omanakuttan Vikas Buchemmavari Jonathan A. Gross Ivan H. Deutsch Milad Marvian Center for Quantum Information and Control Department of Physics and Astronomy Venice California 90291 USA Department of Electrical and Computer Engineering

We construct a fault-tolerant quantum error-correcting protocol based on a qubit encoded in a large spin qudit using a spin-cat code, analogous to the continuous-variable cat encoding. With this, we can correct the dominant error sources, namely processes that can be expressed as error operators that are linear or quadratic in the components of angular momentum. Such codes tailored to dominant error sources can exhibit superior thresholds and lower resource overheads when compared to those designed for unstructured noise models. A key component is the cnot gate that preserves the rank of spherical tensor operators. Categorizing the dominant errors as phase and amplitude errors, we demonstrate how phase errors, analogous to phase-flip errors for qubits, can be effectively corrected. Furthermore, we propose a measurement-free error-correction scheme to address amplitude errors without relying on syndrome measurements. Through an in-depth analysis of logical cnot gate errors, we establish that the fault-tolerant threshold for error correction in the spin-cat encoding surpasses that of standard qubit-based encodings. We consider a specific implementation based on neutral-atom quantum computing, with qudits encoded in the nuclear spin of 87Sr, and show how to generate the universal gate set, including the rank-preserving cnot gate, using quantum control and the Rydberg blockade. These findings pave the way for encoding a qubit in a large spin with the potential to achieve fault tolerance, high threshold, and reduced resource overhead in quantum information processing.

关键词： Quantum computation Quantum control Quantum error correction Quantum information theory

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Model Predictive control of a wastewater treatment process using neural networks 28

Model Predictive Control of a wastewater treatment process u...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Vasiliev, Iulian Luca, Irina Condrachi, Larisa Luca, Laurentiu Barbu, Marian Vilanova, Ramon Caraman, Sergiu Dunǎrea de Jos University of Galati Department of Automation and Electrical Engineering Galati Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain

ISBN: (纸本)9798350339918

This paper deals with the increasing of the operation efficiency of a wastewater treatment plant by reducing the level of pollutant concentration in the effluent. In essence, the main goal is the reduction of the concentration of organic substrate, Model Predictive control algorithm being used for this, considering as a model of the wastewater treatment process a neural network model based on a simplified mathematical model of the 4th order. For the implementation of the control algorithm, two neural networks were trained. In the first version of the neural network, it was considered that all 4 state variables are measurable and in the second version, only the organic substrate was considered measurable. Regarding the training of the two neural networks, good results were obtained in the case of both versions. Instead, the control algorithm gave better results when the second neural network was used (lower values of the substrate, lower variations of the aeration rate, which means a lower cost of operation). © 2023 IEEE.

关键词： Neural networks

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An analysis towards integrated control of wastewater collecting and treatment systems 27

An analysis towards integrated control of wastewater collect...

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Vasiliev, Iulian Luca, Laurentiu Barbu, Marian Vilanova, Ramon Caraman, Sergiu Department of Automation and Electrical Engineering Dunărea de Jos University of Galati Galati Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain

ISBN: (纸本)9798350337983

This paper deals with an analysis regarding the effect of Sewer Network operation on the performances of the integrated wastewater collecting and treatment system. The analysis is done by considering various control actions for the output storage tank of the sewer network, comparing a series of performance indicators defined for both subsystems with their "no control" value. Two cases have been considered regarding the control actions for the output storage tank of the sewer network: 1. keeping the output valve at a constant opening and 2. keeping the output valve at the maximum opening possible such as the outflow is limited to a constant value. While the second case did not prove advantageous, the first one showed that lowering the sewer network output valve opening would improve the integrated system performances. © 2023 IEEE.

关键词： Wastewater treatment

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A Deep Learning Approach for Sustainable and Secure Operations of Cloud Data Centres for Optimising the Energy Efficiency 4

A Deep Learning Approach for Sustainable and Secure Operatio...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Sathish, K. Arya, Hemlata Kaliappan, S. Sivakumar, S. Arigela, Arun Kumar Venkatesh, R. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh Madanapalle India Department of Computer Science & Engineering Madhya Pradesh Gwalior India Kumaraguru College of Technology Department of Electrical and Electronics Engineering Saravanampatti Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electrical and Electronics Engineering Tamilnadu Avadi Chennai India A UGC Autonomous Institute Department of CSIT Telangana Hyderabad India Department of Physics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350385793

This study focuses on enhancing the sustainability and efficiency of hospital data centers through the deployment of machine learning algorithms. Support Vector Machines (SVM), Decision Trees (DT), Artificial Neural Networks (ANN), and Recurrent Neural Networks (RNN) are applied to forecast server workloads and dynamically optimize resource allocation. SVM emerges as the most accurate model at 96.5%, leading a suite of models that effectively avoid server overloads and allow efficient load sharing. The research extends its influence to the optimization of important hardware parameters, including temperature, humidity, power consumption, and energy efficiency. Each approach contributes differently to enhance the performance of the server infrastructure reliability. The study continues with a thorough examination of energy efficiency gains over an 8-hour period, offering vital insights into the models' dynamic influence. Ultimately, this study develops a complete framework for sustainable and adaptable healthcare data management, revealing the potential of machine learning to modernize hospital infrastructure and meet increasing operational difficulties. © 2024 IEEE.

关键词： Data Center Optimization Energy Efficiency Healthcare Machine Learning

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Hamiltonian learning using machine-learning models trained with continuous measurements

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Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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Correction To: Photovoltaic system fault detection techniques: a review

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Neural Computing and Applications 2024年第29期36卷 18547-18548页

作者： El-Banby, Ghada M. Moawad, Nada M. Abouzalm, Belal A. Abouzaid, Wessam F. Ramadan, E. A. Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Electrical Engineering of Computer and Control Systems Faculty of Engineering Kafrelsheikh University Kafrelsheikh Egypt

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