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International Conference on Development and Application systems

作者： Alin-Mihai Căilean Sebastian-Andrei Avătămăniței Cătălin Beguni Eduard Zadobrischi Mihai Dimian Department of Computers Electronics and Automation Stefan cel Mare University of Suceava Suceava Romania Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control Stefan cel Mare University of Suceava Suceava Romania East European Border Scientific and Technological Park Siret Romania

ISBN: (数字)9798350349290

ISBN: (纸本)9798350349306

In the context in which human society is struggling to achieve carbon neutrality and to reduce energy waste, this work proposes a novel Visible Light Communications (VLC) prototype designed for emergency message transmission enhanced with energy management consumption capability. Thus, in order to be able to accommodate different lighting requirements, the VLC transmitter is able to work with variable duty cycles, while maintaining the data link active. Moreover, different from existing works, the VLC receiver integrates a light monitoring function and an infrared data transmission function, which can be further expanded into a Light Fidelity (Li-Fi) system. This way, based on the workspace’s illumination level, the VLC transmitter can maximize energy efficiency through duty cycle adjustments. The proposed prototype has been implemented in a real working space and experimentally evaluated. The preliminary experimental results have confirmed the advantages of this concept, as the prototype demonstrated its ability to constantly adjust its illumination level while maintaining the active communication link. Additionally, the prototype is able of achieving a BER lower than 10 -7 for duty cycles between 10% and 90%.

关键词： Wireless communication Transmitters Prototypes Lighting Light fidelity Energy efficiency Reliability

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Field-Free Current-Induced Switching of L10-FePt Using Interlayer Exchange Coupling for Neuromorphic Computing

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Physical Review Applied 2023年第2期19卷 024034-024034页

作者： Kaifeng Dong Zhe Guo YiYi Jiao Ruofan Li Chao Sun Ying Tao Shuai Zhang Jeongmin Hong Long You School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China

L10-phase FePt is well known for its unusually robust perpendicular magnetic anisotropy (PMA) properties arising from strong conduction-electron spin-orbit coupling (SOC) with the Fe orbital moment. The strong PMA enables stable magnetic storage and memory devices with ultrahigh capacity. Meanwhile, SOC is also the premise of the recently discovered spin-orbit-torque (SOT) effect, which opens avenues for possible electrical manipulation of magnetization for L10-FePt. The bulk SOT of the L10-FePt single layer was discovered recently; this leads to the magnetization of L10-FePt reversibly switching on itself. However, deterministic SOT switching of bulk perpendicularly magnetized FePt magnets relies on an external magnetic field to break the symmetry. Here, the symmetry-breaking issue is resolved by interlayer exchange coupling, where the FePt layer is coupled with an in-plane magnetized NiFe layer through a TiN spacer layer. Furthermore, our device also presents memristive or gradual switching behaviors, even without an external field, offering the potential for constructing spin synapses and spin neurons for neuromorphic computing. An artificial neural network with high accuracy (∼91.17%) is realized based on the constructed synapses and neurons. Our work paves the way for field-free SOT switching of single bulk PMA magnets and their potential applications in neuromorphic computing.

关键词： Exchange interaction Magnetic anisotropy Magnetization switching Spin-orbit torque Spintronics

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A Reinforcement Learning Based Dynamic Multi-Objective Constrained Evolutionary Algorithm for Open-Pit Mine Truck Scheduling

A Reinforcement Learning Based Dynamic Multi-Objective Const...

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Chinese automation Congress (CAC)

作者： Junxiang Qiu Changhe Li Shengxiang Yang Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Computer Science and Informatics De Montfort University Leicester UK

Aiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods.

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Speech Emotion Recognition Based on Deep Learning Network with Non-local Networks

Speech Emotion Recognition Based on Deep Learning Network wi...

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2021 China automation Congress, CAC 2021

作者： Han, M.T. Liu, Z.T. 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

ISBN: (纸本)9781665426473

Convolutional Neural Networks (CNN) based Speech Emotion Recognition (SER) has the problem of learning bias towards stronger emotions while giving less weight to relatively faint emotions. This lack of subtleness causes the misclassification of emotions that have similar arousal such as anger and joy. To solve this problem, a novel speaker-independent SER methods consist of CNN with non-local networks and attention-based Bi-directional Long Short-Term Memory (BiLSTM) is proposed. The non-local networks can capture the dependence between any two points globally to enhance the correlation of emotional features at the utterance level. The extracted tri-channel Log-Mel spectrograms features include the raw of Log-Mel spectrums, the Log-Mel spectrums' deltas and delta-deltas respectively. Finally long-term contextual dependencies, local information and non-local information which is obtained by non-local networks is contained in the extracted feature. It was tested on the Interactive Emotional Dyadic Motion Capture Database (IEMOCAP) dataset. The experimental results show that the WAR of SER is 66.85% and the UAR of SER is 61.01%. Compared with several newly proposed SER methods, the effectiveness of the proposed model is demonstrated. © 2021 IEEE

关键词： Convolutional neural networks

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Finite-Time State Estimation for Discrete-Time Nonlinear Singularly Perturbed Complex Networks under New Event-Triggered Mechanism† 40

Finite-Time State Estimation for Discrete-Time Nonlinear Sin...

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40th Chinese control Conference, CCC 2021

作者： Yang, Chao Wan, Xiongbo China University of Geosciences School of Automation 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

ISBN: (纸本)9789881563804

This paper investigates the finite-time state estimation problem for a class of discrete-time nonlinear singularly perturbed complex networks under a new dynamic event-triggered mechanism (DETM). This new DETM is devised to adjust the date packet transmissions flexibly with hope to save network resources. By constructing a new Lyapunov function dependent on the information of the singular perturbation parameter (SPP) and DETM, a sufficient condition is derived which ensures that the error dynamics of state estimation is finite-time stable. The parameters of the state estimator are given by means of the solutions to several matrix inequalities and the upper bound of the SPP can be evaluated simultaneously. The effectiveness of the designed state estimator is demonstrated by a numerical example. © 2021 Technical Committee on control Theory, Chinese Association of automation.

关键词： Complex networks

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Context-Aware control for Quality Improvement in Liquid Steel Manufacturing

Context-Aware Control for Quality Improvement in Liquid Stee...

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control Technology and Applications (CCTA),

作者： Isabela R. Birs Cristina I. Muresan Mircea Susca Vlad Mihai Mihaly Mihaela Ghita Maria Ghita Robin De Keyser Clara Ionescu Research Group of Dynamical Systems and Control Faculty of Engineering and Architecture Ghent University EEDT Decision & Control Flanders Make Consortium Ghent Belgium Department of Automation Technical University of Cluj Napoca Cluj-Napoca Romania

This paper presents an original theoretical and implementation framework of fractional order event-based control with the purpose of quality improvement at the end production line of a continuous casting process. A real-life steel manufacturing plant is taken into consideration in the study. A prior fractional order model is considered to tune a fractional order Proportional Integral controller that ensures an ideal flow velocity of liquid steel. A novel event-based fractional order methodology is proposed that uses internal and external process events in order to improve quality . The implementation strategy takes into consideration available software and hardware components at the plant site, offering a realistic and implementable solution, highly suitable for the current steel industry setting. Numeric simulations successfully validate the proposed methodology.

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A Multi-modal Panoramic Speaker Localization Method

A Multi-modal Panoramic Speaker Localization Method

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2021 China automation Congress, CAC 2021

作者： Long, Y.Z. Jian, Y.W. Liu, Z.T. 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

ISBN: (纸本)9781665426473

As a supporting technology in the field of human-computer interaction, speaker localization method has been a research hotspot in recent years. However, the existing single-mode speaker location methods cannot meet the requirements of stability and rapidity. For improve the accuracy and effective positioning range of speaker localization system, a multi-modal panoramic speaker localization method is proposed in this paper. Firstly, the coarse position of the speaker was preliminarily obtained by sound source localization. Then, according to the initial positioning of the speaker, whether the image used for face recognition need to be stitched is decided. What's more, the position of speaker is calculated by face detection and calibration. Finally, the results of voice and image localization are fused by coordinate transformation. The proposal is tested on self-built experimental system, and the experimental results prove the effectiveness of the proposed method. © 2021 IEEE

关键词： Face recognition

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Accelerometer optimization based on real-time measurement of Hole Deviation INS with While-Drilling

Accelerometer optimization based on real-time measurement of...

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2021 China automation Congress, CAC 2021

作者： Liu, Yuan-Qiu Jin, Fang Song, Jun-Lei Dong, Kai-Feng Mo, Wen-Qin Hui, Ya-Juan School of Automation China University of Geosciences Wuhan420074 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

ISBN: (纸本)9781665426473

Well deviation Measurement of While-Drilling(MWD) refers to the real-time measurement of well deviation and azimuth While-Drilling is the key for navigation in automatic drilling, real-time detection of drilling direction and automatic drilling control. In this paper, we solve the problem that the accelerometer could not accurately measure gravity because of the high-speed rotation caused by the eccentric installation of INS (Inertial Navigation System) and the acceleration of vibration line in the real-time measurement of borehole deviation and azimuth angle while drilling. Firstly, the dynamic physical model of accelerometer and gyroscope under eccentric installation is built, the non-gravity error of accelerometer measurement under eccentric motion is analyzed, and the algorithm optimization is made. Secondly, a calibration scheme for the compensation coefficients of centrifugal and tangential forces is given, which enables the carrier to make eccentric pure rotational motion on the rotating platform to determine the compensation calibration coefficient matrix. Finally, the first-order complementary filter algorithm is used to fuse the gyroscope angular velocity with the corrected acceleration for attitude calculation, and the algorithm is experimented. In this study, MPU6050 is selected as the chip of INS, and the attitude algorithm is realized by STM32. The experimental results show that the high dynamic error of the accelerometer measurement caused by eccentric motion is effectively reduced by the algorithm compensation. © 2021 IEEE

关键词： Accelerometers

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A fault diagnosis method based on multi-feature fusion and broad learning system

A fault diagnosis method based on multi-feature fusion and b...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Wang, Yan Hu, Wenkai Cao, Weihua Wu, Min China University of Geosciences School of Automation 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

ISBN: (纸本)9781665401159

Reliable fault diagnosis is of practical significance for process safety in modern industrial facilities. This article proposes a data-driven fault diagnosis method based on multi-feature fusion and broad learning. The contributions are twofold: First, a systematic multi-feature extraction approach is presented to extract long-term trend features, short-term trend features, and binary-valued alarm signals;second, a broad learning system based fault diagnosis method is proposed to diagnose faults based on multi-feature fusion and to update the diagnosis model in an incremental manner. The proposed method has a strong nonlinear modeling capability and can also update the diagnosis model incrementally when unseen fault types are detected. The efficacy of the proposed method is demonstrated by simulations with the benchmark Tennessee Eastman (TE) process. © 2021 IEEE.

关键词： Learning systems

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NeRF in Robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in Robotics and The Advance of NeRF in Robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： Robotics

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