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Rotation Speed Estimation for Motor Fault Diagnosis Using Bidirectional Long Short Term Memory Networks

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Rotation Speed Estimation for Motor Fault Diagnosis Using Bi...

Chinese Control and Decision Conference, CCDC

作者： Xiaoxian Wang Siliang Lu Shiwu Zhang Department of Precision Machinery and Precision Instrumentation CAS Key Laboratory of Mechanical Behavior and Design of Materials University of Science and Technology of China Hefei China College of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781665478977

Rotation speed estimation is a key step for motor fault diagnosis under variable-speed working conditions. This study investigates a method for speed estimation by fusing the information from vibration and current signals using a bidirectional long short term memory (BiLSTM) deep neural networks. Firstly, the instantaneous frequency (IF) curves are extracted from the signals using an improved time-frequency analysis method. A BiLSTM regression model is trained for IFs fusion and rotation speed estimation. Finally, the rotation angle is computed from the estimated rotation speed curve, and the vibration signal is resampled and processed for motor fault diagnosis. The performance of the proposed method is validated by the experimental data acquired from a switched reluctance motor test rig.

关键词： Vibrations Fault diagnosis Deep learning Time-frequency analysis Fuses Neural networks Estimation

Incentive Mechanism design for Emergency Frequency Control in Multi-Infeed Hybrid AC-DC System

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arXiv 2022年

作者： Liu, Ye Shen, Chen Wang, Zhaojian Liu, Feng The State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

In multi-infeed hybrid AC-DC (MIDC) systems, the emergency frequency control (EFC) with LCC-HVDC systems participating is of vital importance for system frequency stability. Nevertheless, when regional power systems are operated by different decision-makers, the LCC-HVDC systems and their connected AC systems might be unwilling to participate in the EFC due to the costs and losses. In this paper, to incentivize the LCC-HVDC systems and their connected adjacent AC systems to participate in the droop-based EFC, a novel control-parameter-based incentive mechanism is proposed, which can deal with various possible emergency frequency faults. Then, a non-cooperative-based incentive game model is formulated to implement the incentive mechanism in the MIDC system. An algorithm for seeking the Nash equilibrium is designed, and the uniqueness of Nash equilibrium is proven. Moreover, the individual rationality, incentive compatibility and social optimality of the proposed mechanism are analyzed and proven. The effectiveness of the proposed incentive mechanism is verified through a case study. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

Distributed Emergency Frequency Control Considering Transient Stability Constraints in Multi-Infeed Hybrid AC-DC System

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arXiv 2022年

作者： Liu, Ye Shen, Chen Wang, Zhaojian The State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

Due to possible emergency faults and frequency regulation reserve shortage in the multi-infeed hybrid AC-DC (MIDC) system, the emergency frequency control (EFC) with LCC-HVDC systems participating is important for system frequency stability. Nevertheless, the existing decentralized EFC strategies cannot guarantee the transient stability constraints of lines and cannot meet the frequency restoration requirement. To fill this gap, this paper proposes a complementary distributed EFC strategy. By selecting the semi-distributed or fully-distributed control law according to the state of AC system control center, the proposed distributed EFC can improve the control performance with a normal control center and enhance the control reliability in case of control center failure. Then, to derive the semi-distributed and fully-distributed control laws, a general design method is proposed by formulating an optimal EFC problem. Both of these two control laws can guarantee transient stability constraints, restore system frequency and achieve the defined optimal control objective. Moreover, the optimality of the closed-loop equilibrium is proven, and a Lyapunov analysis shows the asymptotic stability of the closed-loop system with the discontinuous control dynamics. A case study verifies the effectiveness of the proposed distributed EFC strategy. Copyright © 2022, The Authors. All rights reserved.

关键词： Asymptotic stability

***: Arduino-Based Automated Soil Macronutrients and pH Level Analyzer Using Visible-Near Infrared Spectrometer

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***: Arduino-Based Automated Soil Macronutrients and pH Leve...

Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Maria Victoria C. Padilla Mary Anne Joyce D. Larido Mae Nard L. Gener Christian V. Dela Cruz Irene Viktoria C. De Vergara Jay Mark A. Arevalo Nilo M. Arago August C. Thio-Ac Edmon O. Fernandez Romeo L. Jorda Gilfred Allen M. Madrigal Lean Karlo S. Tolentino Jessica S. Velasco Department of Electronics Engineering Technological University of the Philippines Manila Philippines Center for Engineering Design Fabrication and Innovation College of Engineering Technological University of the Philippines Manila Philippines Intelligent Systems Applications and Circuits Laboratory (ISAAC) Integrated Research and Training Center Technological University of the Philippines Manila Philippines Center for Artificial Intelligence and Nanoelectronics (CAIN) Integrated Research and Training Center Technological University of the Philippines Manila Philippines Department of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan

This study aimed to improve efficiency and reduce costs associated with soil nutrient analysis for farmers. A device called ***, utilizing an Arduino Mega 2560 microcontroller and TOSHIBA TCD1304AP CCD Linear Sensor, was developed to measure soil macronutrients (Nitrogen, Phosphorus, and Potassium) and pH levels. Spectroscopy and the Beer-Lambert law were employed to classify these parameters accurately in Philippine farmlands. The results were presented in a printed form, including nutrient and pH level calculations, along with fertilizer recommendations. This project particularly benefits farmers in remote areas, providing rapid results within one to two hours, eliminating the need to visit regional soil test centers and endure lengthy waiting periods. The study focused on identifying soil macronutrients and pH levels for rice crops, achieving a 95% accuracy rate.

关键词：

Development of an IoT-Based Soil Macronutrient Analysis System Utilizing Electrochemical Sensors and Machine Learning Algorithms

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Development of an IoT-Based Soil Macronutrient Analysis Syst...

Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Timothy M. Amado Adrian Erwin D. Alvarez Allyssa Ocampo Venise Aziel F. Paz Airah Jan N. Punongbayan Lemuel M Yumena Maria Victoria C. Padilla Gilfred Allen M. Madrigal Lean Karlo S. Tolentino Edmund G. Monilar Electronics Engineering Department Technological University of the Philippines Manila Philippines Center for Engineering Design Fabrication and Innovation College of Engineering Technological University of the Philippines Manila Philippines Intelligent Systems Applications and Circuits Laboratory (ISAAC) Integrated Research and Training Center Technological University of the Philippines Manila Philippines Center for Artificial Intelligence and Nanoelectronics (CAIN) Integrated Research and Training Center Technological University of the Philippines Manila Philippines Department of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan

Efficient administration of principal soil nutrients is essential for achieving rational agriculture and minimizing economic losses. To improve on-site observing techniques and soil management, the utilization of advanced sensors capable of quantifying soil properties is crucial. In this study, three sensors were employed: an Ion Sensitive Field Effect Transistor (ISFET) pH sensor, a soil moisture sensor, and an RGB color sensor with an IR filter and white LED (TCS34725). The ISFET pH sensor, functioning as a Metal Oxide Semiconductor Field Effect Transistor (MOSFET), provided pH measurements ranging from 1 to 14 and exhibited temperature resistance between 0 to 100 degrees Celsius. The soil moisture sensor was designed to determine soil moisture content within a range of 0 to 1023. The RGB color sensor, by reducing the infrared spectral component of incoming light, enabled precise color measurements. These sensors were integrated into the Arduino MEGA 2560 microcontroller board to facilitate simultaneous operation of complex schemes. The acquired sensor data were correlated to the concentrations of soil macronutrients, specifically Nitrogen (N), Phosphorus (P), and Potassium (K). Through the implementation of machine learning algorithms, the levels of macronutrients can be determined, and for the training set of these algorithms, data collection was conducted using over 300 soil samples.

关键词：

Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model

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arXiv 2025年

作者： Liu, Chenyang Chen, Keyan Zhao, Rui Zou, Zhengxia Shi, Zhenwei The Department of Aerospace Intelligent Science and Technology School of Astronautics The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Recently, generative foundation models have significantly advanced large-scale text-driven natural image generation and have become a prominent research trend across various vertical domains. However, in the remote sensing field, there is still a lack of research on large-scale text-to-image (text2image) generation technology. Existing remote sensing image-text datasets are small in scale and confined to specific geographic areas and scene types. Besides, existing text2image methods have struggled to achieve global-scale, multi-resolution controllable, and unbounded image generation. To address these challenges, this paper presents two key contributions: the Git-10M dataset and the Text2Earth foundation model. Git-10M is a global-scale image-text dataset comprising 10.5 million image-text pairs, 5 times larger than the previous largest one. The dataset contains essential resolution information and covers a wide range of geographic scenes and contains essential geospatial metadata, significantly surpassing existing datasets in both size and diversity. Building on Git-10M, we propose Text2Earth, a 1.3 billion parameter generative foundation model based on the diffusion framework to model global-scale remote sensing scenes. Text2Earth integrates a resolution guidance mechanism, enabling users to specify image resolutions. A dynamic condition adaptation strategy is proposed for training and inference to improve image generation quality. Text2Earth not only excels in zero-shot text2image generation but also demonstrates robust generalization and flexibility across multiple tasks, including unbounded scene construction, image editing, and cross-modal image generation. This robust capability surpasses previous models restricted to the basic fixed size and limited scene types. On the previous text2image benchmark dataset, Text2Earth outperfoms previous models with a significant improvement of +26.23 FID and +20.95% Zero-shot Cls-OA metric. Our project page is https://chen-y

关键词： Optical remote sensing

Zero-Forcing Based Downlink Virtual MIMO-NOMA Communications in IoT Networks

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arXiv 2022年

作者： Shi, Zheng Wang, Hong Fu, Yaru Yang, Guanghua Ma, Shaodan Hou, Fen Tsiftsis, Theodoros A. Institute of Physical Internet The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Information Systems Technology and Design Singapore University of Technology and Design Singapore The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China

To support massive connectivity and boost spectral efficiency for internet of things (IoT), a downlink scheme combining virtual multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to de-multiplex the input data streams. Only statistical channel state information (CSI) is available at base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed-form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with higher transmission rate under benign channel conditions. © 2022, CC BY.

关键词： Internet of things

Chance-Constrained Generic Energy Storage Operations under Decision-Dependent Uncertainty

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arXiv 2022年

作者： Qi, Ning Pinson, Pierre Almassalkhi, Mads R. Cheng, Lin Zhuang, Yingrui State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China The Dyson School of Design Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT05405 United States

Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units. © 2022, CC BY.

关键词： Energy storage

Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet

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arXiv 2022年

作者： Xu, Minrui Niyato, Dusit Yang, Zhaohui Xiong, Zehui Kang, Jiawen Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China Zhejiang Laboratory Hangzhou31121 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore 487372 Singapore The School of Automation Guangdong University of Technology China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality issues due to eavesdropping risks of exchanging cryptographic keys through traditional encryption schemes. Therefore, in this paper, we propose a hierarchical architecture for quantum-secured FEL systems with ideal security based on the quantum key distribution (QKD) to facilitate public key and model encryption against eavesdropping attacks. Specifically, we propose a stochastic resource allocation model for efficient QKD to encrypt FEL keys and models. In FEL systems, remote FEL workers are connected to cluster heads via quantum-secured channels to train an aggregated global model collaboratively. However, due to the unpredictable number of workers at each location, the demand for secret-key rates to support secure model transmission to the server is unpredictable. The proposed systems need to efficiently allocate limited QKD resources (i.e., wavelengths) such that the total cost is minimized in the presence of stochastic demand by formulating the optimization problem for the proposed architecture as a stochastic programming model. To this end, we propose a federated reinforcement learning-based resource allocation scheme to solve the proposed model without complete state information. The proposed scheme enables QKD managers and controllers to train a global QKD resource allocation policy while keeping their private experiences local. Numerical results demonstrate that the proposed schemes can successfully achieve the cost-minimizing objective under uncertain demand while improving the training efficiency by about 50% compared to state-of-the-art schemes. © 2022, CC BY.

关键词： Stochastic systems