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Monolithically Integrated Temperature Sensor Based on p-GaN/AlGaN/GaN Heterostructure

Monolithically Integrated Temperature Sensor Based on p-GaN/...

China International Forum o Solid State Lighting: International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS)

作者： Qingxin Liu Kailin Ren Luqiao Yin Jianhua Zhang School of Microelectronics Shanghai University Shanghai China Shanghai Key Laboratory of Chips and Systems for Intelligent Connected Vehicle Shanghai University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Sensing Chip Technology Shanghai University Shanghai China Key Laboratory of Advanced Display and System Applications (Ministry of Education) Shanghai University Shanghai China

ISBN: (数字)9798331541149

ISBN: (纸本)9798331541156

A monolithically integrated temperature sensor circuit based on a p-GaN/AlGaN/GaN heterojunction is proposed. The sensor comprises two parts: the first part is a temperature-sensitive voltage source formed by a two-dimensional electron gas (2DEG) resistor and a schottky barrier diode (SBD) in series, and the second part is a resistor-transistor logic (RTL) inverter formed by a 2DEG resistor and a p-GaN high electron mobility transistor (HEMT) in series. The output voltage of the temperature-sensitive voltage source is connected to the gate of the p-GaN HEMT in the second part, providing the gate voltage. According to data obtained from TCAD simulation results, the output voltage of the temperature-sensitive voltage source exhibits positive temperature dependence. The output voltage of the RTL inverter exhibits a negative temperature dependence. When the temperature sensor is properly biased, the voltage division between the SBD and the 2DEG resistor in the temperature-sensitive voltage source reallocates as the temperature rises, causing the gate voltage of the connected RTL inverter to increase. This increasement enhances the temperature-induced variation in the RTL inverter's output voltage, increasing the sensitivity of the temperature sensing circuit from 4 mV/K with a fixed gate voltage to 14 mV/K. This simulation demonstrates a compact monolithic integration sensor for monitoring the on-chip temperature of GaN power integrated circuits for protection and control.

关键词： Temperature sensors Resistors Temperature dependence Sensitivity HEMTs Logic gates Thermal management Inverters System-on-chip MODFETs

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An Informer Based Alarm Early Prediction Method Over Consecutive Alarm Monitoring Periods

An Informer Based Alarm Early Prediction Method Over Consecu...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Wenbin Jiang Wenkai Hu Zhe Liu Fei Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China SINOPEC-SK (WUHAN) Petrochemical Company Limited Wuhan China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Alarm systems serve as the first layer of protection for modern process industries to monitor industrial processes and ensure operational safety. However, the presence of alarm floods is common in alarm systems and may distract operators from critical alarms. To provide decision supports to operators during alarm flood situations, alarm prediction becomes a potential effective solution. The difficulty of alarm prediction lies in how to achieve high prediction accuracy over long-term consecutive alarm monitoring periods, including situations with either high or low alarm rates. Consequently, this paper presents an industrial alarm prediction method for both alarm flood periods and non-alarm flood periods. Specifically, a strategy of combining fixed-length and fixed-time sliding windows is designed. Further, an alarm prediction model based on Informer is proposed for alarm prediction under different alarm rates. A case study is presented to prove the validity of the proposed alarm prediction method.

关键词： Industries Adaptation models Analytical models Accuracy Predictive models Alarm systems Floods Protection Informatics Monitoring

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Fault Diagnosis of Single-Phase Five-Level Packed U-cell Inverter under Different Load and Variable Working Conditions

Fault Diagnosis of Single-Phase Five-Level Packed U-cell Inv...

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International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM)

作者： Ibtissem Abari Mahmoud Hamouda Mohammad Sleiman Jaleleddine Ben Hadj Slama Hadi Y. Kanaan Kamal Al-Haddad LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d’Ingénieurs de Sousse Sousse Tunisie Ecole de Technologie Supérieure GREPCI Montreal Canada Faculty of Engineering – ESIB Saint-Joseph University of Beirut Beirut Lebanon

ISBN: (纸本)9781665451697

Open-circuit fault under different load and variable working conditions is the most severe issue that affects the robustness of fault diagnosis algorithms. Considering this, this research work proposes a high-frequency fault diagnosis approach that is capable to detect and isolate a single open-switch fault in a single-phase five-level Packed U-Cell (PUC5) inverter, not only under stable conditions but also under different load and variable working conditions. Firstly, a high frequency model of the PUC5 inverter is proposed. Then, the conducted emissions, measured across the electromagnetic interferences filter resistor, are employed as a fault signature. Finally, simulation results are considered to verify the effectiveness of the proposed fault diagnosis method.

关键词： Fault diagnosis Employee welfare Resistors Fault detection Simulation Numerical simulation Inverters

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Semivortex solitons and their excited states in spin-orbit-coupled binary bosonic condensates

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Physical Review E 2024年第6期109卷 064201-064201页

作者： Haiming Deng Jinqing Li Zhaopin Chen Yaohui Liu Dong Liu Chunzhi Jiang Chao Kong Boris A. Malomed School of Physics and Electronic-Electrical Engineering Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Physics Department and Solid-State Institute Department of Physical Electronics School of Electrical Engineering Faculty of Engineering Instituto de Alta Investigación

It is known that two-dimensional two-component fundamental solitons of the semivortex (SV) type, with vorticities (s+,s−)=(0,1) in their components, are stable ground states (GSs) in the spin-orbit-coupled (SOC) binary Bose-Einstein condensate with the contact self-attraction acting in both components, in spite of the possibility of the critical collapse in the system. However, excited states (ESs) of the SV solitons, with the vorticity set (s+,s−)=(S+,S++1) and S+=1,2,3,..., are unstable in the same system. We construct ESs of SV solitons in the SOC system with opposite signs of the self-interaction in the two components. The main finding is stability of the ES-SV solitons, with the extra vorticity (at least) up to S+=6. The threshold value of the norm for the onset of the critical collapse, Nthr, in these excited states is higher than the commonly known critical value, Nc≈5.85, associated with the single-component Townes solitons, Nthr increasing with the growth of S+. A velocity interval for stable motion of the GS-SV solitons is found too. The results suggest a solution for the challenging problem of the creation of stable vortex solitons with high topological charges.

关键词： Solitons

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A Multi-feature Extraction-based Image Identification Method for Rock Debris in The Drilling Process

A Multi-feature Extraction-based Image Identification Method...

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2022 Chinese Automation Congress, CAC 2022

作者： Xu, Pengju Gan, Chao Wang, Luzhao Cao, Weihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China No.3 Exploration Institute of Geology and Mineral Resources of Shandong Province Yantai264000 China

ISBN: (纸本)9781665465335

In this paper, a multi-feature extraction-based image identification method for rock debris in the drilling process is proposed, involving three main parts (trainable feature extractor, strong feature extraction, and classification). In trainable feature extractor, abstract features are obtained by extracting the full connection layer of Convolutional Neural Network (CNN). In strong feature extraction, the method uses Gray-Level Co-occurrence Matrix (GLCM) and Color Coherence Vector (CCV) to get the strong feature. In classification, the extracted abstract features and strong features are concatenated and fed into the Support Vector Machine (SVM). Comparison results with two well-known methods indicated the effectiveness of the proposed method. © 2022 IEEE.

关键词： Feature extraction

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AN Optimized Genetic Algorithm (GA)-Based MPPT for Off-Grid Solar Photovoltaic System

AN Optimized Genetic Algorithm (GA)-Based MPPT for Off-Grid ...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Mariem Mallat Olfa Bel Hadj Brahim Kechiche Kawther Ezzeddine Mahmoud Hamouda École Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie ESSTHSousse Laboratory of Energy and Materials (LR11ES34) Université de Sousse Sousse Tunisie École Nationale Supérieure d'Ingénieurs de Tunis (ENSIT) Université de Sousse Université de Tunis Tunis Tunisie

Maximum power point tracking (MPPT) algorithms play a key role in a photovoltaic (PV) energy conversion system, which should inherently operate at its maximum available power. Genetic algorithms (GA) have shown their capability to solve different optimization problems and also their superiority to many other methods. In this paper, a GA-based MPPT algorithm is implemented to extract the maximum power from an off-grid PV system. To convert the generated DC power from the PV arrays into adequate AC power to supply different types of loads, a double-stage power conversion topology is adopted. A DC-DC boost converter is firstly, connected to the terminals of the PV modules. The GAbased MPPT algorithm is, thereafter, implemented to adjust the duty cycle of the boost converter in a way to achieve MPP operation of the PV modules. Then, a stand-alone PV inverter is connected in cascade with the boost converter to feed the AC loads. A numerical model of the whole PV conversion platform is developed using Matlab/Simulink software. Thereafter, different simulation tests are performed considering a real case of an off-grid PV installation located in the city of Mzaougha, Monastir (Tunisia) in an isolated area from distribution grid of the national company of electricity. An adequate PV module, which satisfies the energetic needs of the site is also selected and its real model is used for simulations. The obtained results confirm the feasibility of the proposed technique and its effectiveness in controlling an off-grid PV system.

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Three-Dimensional Force Sensor Based on Deep Learning 1st

Three-Dimensional Force Sensor Based on Deep Learning

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1st International Conference on Cognitive Computation and systems, ICCCS 2022

作者： Duan, Qingling Zhang, Qi Luo, Dong Yang, Ruofan Zhu, Chi Liu, Zhiyuan Ou, Yongsheng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen China

ISBN: (纸本)9789819927883

Human skin can accurately sense subtle changes of both normal and shear forces. However, tactile sensors applied to robots are challenging in decoupling 3D forces due to the inability to develop adaptive models for complex soft materials. Therefore, a new soft tactile sensor has been designed in this paper to detect shear and normal forces, including a soft probe and image acquisition device. First, to capture the deformation of the sensor, colored silicone squares were embedded in the soft probe. Capcamera movement of the colored squares under external forces. The image dataset collected at different 3D forces is then input into a deep learning model. Finally, a custom miniature image device is acquired and embedded in the soft probe to miniaturize the sensor. Computing results obtained from experimental datasets show that the proposed method can accurately decouple the 3D forces. Robots can grap vulnerable objects with sensors prepared at the robot’s tip. The tactile sensors studied in this paper are expected to be applied in robotics fields such as adaptive grasping, dexterous manipulation and human-computer interaction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Probes

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A Novel Cascading Artificial Neural Networks for Enhanced Distribution Network State Estimation

A Novel Cascading Artificial Neural Networks for Enhanced Di...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Mohamad El Iaali Reza Razi Antoine Bruyere Bruno Francois Joao Soares Centrale Lille Junia ULR 2697 - L2EP Univ. Lille Arts et Metiers Institute of Technology Lille France GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

The growing integration of renewable energy sources and the widespread adoption of electric vehicles add complexity to power flows in distribution systems. Understanding the system dynamics and implementing control actions are imperative for effective grid management. While measurements in specific nodes are facilitated by smart meters, accurate sensors, communication networks, and electric vehicles, it is costly and, in some cases, impossible to obtain measurements at all nodes. In response to these challenges, this paper introduces a machine learning-based state estimation method, utilizing available measurements and estimating others. By harnessing data-driven techniques, the proposed method aims to provide accurate estimations of voltages and currents in distribution networks, offering valuable insights into flexibilities management and control processes. Specific case study is considered, confirming the efficiency of the suggested state estimation approach and providing insights into its flexibility across a range of scenarios. In addition to the mentioned benefits, the results validate the proposed method, demonstrating a small margin of error. This approach holds significant promises in improving situational awareness for power system operators and enhancing their understanding of the evolving grid dynamics.

关键词： Accuracy System dynamics Current measurement Power system dynamics Distribution networks Electric vehicles Smart meters

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Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-LSTM-Attention Network from Surface Electromyography

Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhuo Wang Hui Chen Fangliang Yang Xiangyang Wang Xinyu Wu Chunjie Chen Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In various fields such as robotics, rehabilitation, biomechanics, human-machine interfaces, and clinical research, human motion prediction has extensive applications. A hybrid networks model based on Convolutional Neural Network-Long Short-Term Memory-Attention (CNN-LSTM-Attention) was employed in this paper to extract features from the high-dimensional space and time series of surface electromyography (sEMG) data for continuous prediction of knee angles. Five able-bodied subjects participated in the study, walking at a self-selected pace while six surface electromyography (sEMG) signals from muscles involved in knee flexion and extension, along with knee angles, were synchronously recorded. The performance was assessed using the root mean square error (RMSE), Pearson Correlation Coefficient $(r)$ , and coefficient of determination $(R^{2})$ between estimated and measured knee joint angles. Compared to the CNN, LSTM, and CNN-LSTM algorithms, the adopted method estimated knee angles more accurately, achieving an average RMSE, $\rho$ , and $R^{2}$ of $3.09\pm 0.90,\ 0.9911 \pm 0.0052$ , and $0.9817\pm 0.0108$ , respectively. As the advance time increased, the accuracy of knee joint angle estimation decreased, particularly when the advance time reached 100 ms, at which point the RMSE is 4.83 degress. In addition, the computing time of the prediction model was less than 1 ms, which was shorter than the advance time 100 ms it could predict, thereby facilitating real-time computation in practical environments. Compared to previous studies, the proposed method excelled at extracting features directly from raw signals, avoiding the need for handcrafted feature extraction. Consequently, the model effectively achieved continuous and accurate predictions of joint motion angles and demonstrated potential to reduce latency in the human-machine interface.

关键词： Knee Accuracy Computational modeling Predictive models Feature extraction Electromyography Convolutional neural networks Wearable devices Long short term memory Robots

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Fixed-Time Sliding Mode Tracking Control for Two-Dimensional Directional Drilling System

Fixed-Time Sliding Mode Tracking Control for Two-Dimensional...

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Asian Control Conference

作者： Jinyu Li Hongchao Wei Chengda Lu Wangnian Li Quanxin Li Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, the effect of the initial state of the drilling system on trajectory tracking is taken into account in the directional drilling trajectory tracking control. Firstly, the trajectory model describing the two-dimensional drilling trajectory of the rotary steering system (RSS) is introduced. To improve the accuracy of trajectory tracking control and reduce the trajectory deviation, a fixed-time sliding mode control (FTSMC) method is introduced. Then, the stability of the closed-loop system is analyzed, and the range of controller parameters satisfying the stability condition is obtained. Finally, the effectiveness of the designed control strategy is verified by simulation experiments.

关键词： Drilling Upper bound Trajectory tracking Steering systems Stability analysis Trajectory Closed loop systems

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