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4th International Conference on Intelligent control, measurement and Signal Processing, ICMSP 2022

作者： Zhou, Jing Zhang, Lu He, Wenhao Zheng, Libo National Engineering Research Center for Oli & Gas Drilling Downhole Measurement and Control Research Department of Xi'an ShiYou University Xi'an China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

There are drill collar mode waves propagating directly along the drill collar during logging while drilling, drill collar waves will seriously interfere with the received strata wave information, At present, the grooveting method is widely used in drilling instruments, and the effect on drilling collar wave suppression is obvious. In this paper, a new type of sound insulator with gradual U-shaped groove structure is designed to suppress drill collar waves, This paper uses theoretical analysis and simulation methods to study the model mechanics and acoustics two parts of the study. The results show that the uniform groove width basically not affect the mechanical properties of drill collar, The combined groove width parameters of groove depth and gradient groove will affect the mechanical properties. Pulsed sound source depth detection sound insulation device with gradient U-shaped groove structure, on the basis of ensuring the mechanical strength of drill collar, the sound wave attenuation of the drilling collar wave is more than that of the uniform groove structure sound insulation body, that is, the gradual U -shaped groove structure sound insulation body has better acoustic characteristics. © 2022 IEEE.

关键词： Infill drilling

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Information Presentation Strategies to Promote Pedestrian Behavior Change in Mixed Spaces with Automated Vehicle

Information Presentation Strategies to Promote Pedestrian Be...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Nishiura, Yuki Yamada, Hiroyuki Kurazume, Ryo Graduate School of Information Science and Electrical Engineering Kyushu University Japan Center for Sustainability - Connective Automation Hitachi Ltd. Research and Development Group Autonomous Control Research Department Ibaraki312-0034 Japan Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka819-0395 Japan

ISBN: (纸本)9798350312072

This paper introduces an information presentation strategy for pedestrians, aiming to enhance traffic efficiency in a mixed pedestrian-automated vehicle environment, such as a public road. While automated driving technology has made remarkable progress, interactions with pedestrians on regular roads have mostly been studied in virtual environments using virtual reality goggles. According to these studies, potential traffic efficiency and safety issues arise from pedestrians' limited understanding of automated vehicle behavior. To address this, we propose a human-machine interface employing a head-mounted display (HMD) to mitigate traffic efficiency degradation caused by pedestrians. The proposed system draws upon behavioral economics principles to encourage pedestrians to modify their behavior and develop better interactions with automated vehicles. Simulations were conducted to identify an information presentation strategy that strongly supports learning, and its effectiveness was further validated through experiments involving a real vehicle. Notably, the experimental results confirmed that the information presentation strategies proven effective in simulations also facilitated pedestrian learning during real-world interactions. © 2024 IEEE.

关键词： Virtual reality

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research on workflow recognition for liver rupture repair surgery

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Mathematical Biosciences and engineering 2024年第2期21卷 1844-1856页

作者： Men, Yutao Zhao, Zixian Chen, Wei Wu, Hang Zhang, Guang Luo, Feng Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Medical Support Technology Research Department Systems Engineering Institute Academy of Military Sciences People’s Liberation Army Tianjin300161 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons.

关键词： Image classification

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A LESO Based Backstepping controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Dubins Vehicle Intercept of a Brownian Target on a Sphere 63

Dubins Vehicle Intercept of a Brownian Target on a Sphere

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Milutinovic, Dejan Casbeer, David W. University of California Department of Electrical and Computer Engineering Santa CruzCA United States Air Force Research Laboratory Control Science Center Wright-Patterson AFBOH United States

ISBN: (纸本)9798350316339

This paper focuses on a navigation of a Dubins vehicle (DV) to intercept a moving target on a sphere. The uncertainty in the target motion is described by a Brownian motion model. An Itô-type stochastic differential equation for the relative motion between DV and the Brownian moving target is derived. This stochastic differential equation is used to formulate an intercept controller as a minimum time optimal controller. The properties of the controller are incorporated into the numerical computations of the controller, which by its construction can intercept a Brownian target. Our numerical simulation results show that the computed controller can intercept both stationary and moving targets on the sphere. © 2024 IEEE.

关键词： Stochastic systems

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Policy for Optimal Avoidance of Multiple Engagement Zones

Policy for Optimal Avoidance of Multiple Engagement Zones

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Milutinović, Dejan Von Moll, Alexander Weintraub, Isaac E. Casbeer, David Department of Electrical and Computer Engineering University of California Santa Cruz Santa CruzCA95064 United States Control Science Center Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This work formulates the feedback control strategies for vehicles to reach a goal point amongst a field of dynamic risk regions. Whereas previous work has considered deterministic versions of this problem, we consider the scenario in the context of uncertainty. This uncertainty could be due to unknown wind or other external disturbances. The risk regions’ dynamics are tightly coupled to the state of the vehicle and as a result the task of navigating through a field dynamically changes as the vehicle traverses through it. Rather than using a nonlinear program solver, the approach taken here is one of formulating the problem as a stochastic optimal control problem wherein a feedback control law is derived. This feedback control law allows a single or multiple vehicles to reach the desired goal location efficiently and with minimal risk incursion. When inside a risk region, there is a probability rate of the vehicle being neutralized. The stochastic dynamical system is modeled as a hybrid system, i.e., regime switching diffusion. The hybrid system is comprised of continuous spatial dynamics and discrete operational states corresponding to "normal" and "neutralized". The optimal control problem is discretized into a field of vehicle states and control. Using Value Iteration, the control signal at each discrete location is optimized, providing a feedback control strategy over the field. Following a discussion of the methods and approaches, this problem is formulated, simulations are performed, and conclusions and remarks are made © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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Deep learning framework for wind speed prediction in Saudi Arabia

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Neural Computing and Applications 2025年第5期37卷 3685-3701页

作者： Alabdulhadi, Arwa Ahmed Rehman, Shafiqur Ali, Amjad Shafiullah, Md Physics Department College of Science and Humanities Imam Abdulrahman Bin Faisal University Jubail3065 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Increased utilization of conventional energy sources severely impacts the environment by increasing the global temperature and contributing to global warming. Sustainable energy sources could contribute to handling the increment while also providing cheap, clean, and abundant energy. Wind energy is considered one of the most reliable sources of sustainable energy due to its abundance and availability during the day and night compared to other renewable resources. However, effective forecasting of such intermittent resources is considered a key challenge for power system operators. This paper develops a novel deep learning framework for forecasting the wind speed in Dhahran City, Saudi Arabia, using two years of data acquired at different heights from the light detection and ranging device, an active remote sensing wind monitoring system. It also segments the data and removes wrong measurements through data pre-processing. The article identifies the best configuration for deep learning models, such as long short-term memory, through a systematic approach. The presented results confirm the efficacy of the developed models against the selected statistical performance measures. The developed model performs better with large data volumes than with lower volumes. Finally, the comparative analysis with the literature-reported results provides confidence in the competency of the proposed model in predicting wind speed for various periods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Clean energy

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Modeling Axial Compressor Systems Using Deep Learning Methods

Modeling Axial Compressor Systems Using Deep Learning Method...

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Intermountain engineering, Technology and Computing (IETC)

作者： Kellie Wilson Marco P. Schoen Department of Mechanical Engineering Measurement and Control Engineering Research Center Idaho State University Pocatello

Studying the characteristics of complex nonlinear systems and designing control strategies necessitates the development of computationally efficient methods. In particular, compressor systems will benefit from accurate models that capture the nonlinear dynamics. In this paper, an overview of neural networks is given with a focus on Long-Short Term Memory (LSTM) networks for dynamic systems. A simple test system is presented for verifying the LSTM approach for single input single output (SISO) systems. An axial compressor system is used to simulate its nonlinear dynamics using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). The proposed LSTM approach is demonstrated for the multiple input multiple output (MIMO) axial compressor model. The proposed approach indicates that LSTM based modeling for highly nonlinear MIMO systems can yield accurate model characterizations.

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Q-Learning Based control for Swing-Up and Balancing of Inverted Pendulum

Q-Learning Based Control for Swing-Up and Balancing of Inver...

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Intermountain engineering, Technology and Computing (IETC)

作者： Antora Dev Kanan Roy Chowdhury Marco P. Schoen Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Measurement & Control Engineering Research Center (MCERC) Idaho State University Pocatello ID USA

ISBN: (数字)9798350373073

ISBN: (纸本)9798350373080

This study addresses the classic control problem of stabilizing an inverted pendulum on a moving cart, a challenge in control theory and robotics due to its inherent instability and highly nonlinear dynamics. We explore the application of Q-learning, a model-free reinforcement learning algorithm, and its efficacy in deriving an optimal control policy for the system without precise system models. Our approach utilizes Q-learning's capacity for stabilizing a pendulum in an upright position on the top of the horizontally moving cart within a certain boundary. Our strategy adapts to a dynamic environment while showcasing its robustness in developing control policies for complex systems. This research bridges classical control theory with reinforcement learning techniques, contributing to the domain by demonstrating the versatility and potential of machine learning in control tasks.

关键词： Adaptation models Q-learning Heuristic algorithms Optimal control Robustness control theory Nonlinear dynamical systems

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A Monitoring and control System Based on Image Recognition for Iron Filings Cleaning of CNC Machine Tools

A Monitoring and Control System Based on Image Recognition f...

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2024 International Automatic control Conference, CACS 2024

作者： Yang, Tsung-Yen Chiang, Fu-Yu Chen, Yan-Hong Lee, Guan-Jie Lin, Yu-Sheng Lin, Shang-Chih Su, Shun-Feng Huang, Yennun Institute of Automatic Control Engineering Feng Chia University Taichung407 Taiwan Feng Chia University Department Engineering Taichung407 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei115 Taiwan

ISBN: (纸本)9798350354904

This study addresses a key challenge in the CNC tool machining industry: automated in-machine chip removal. Initially, we designed and manufactured an innovative nozzle, incorporating elastic plastic hoses to replace sections of traditional hard coil pipes. A 3D-printed auxiliary mechanism was developed to enable the servo motor to direct the nozzle to any specified coordinates on a two-dimensional plane. Simultaneously, captured images are divided into multiple subblocks for detailed analysis. Image recognition algorithms process each block to determine the location of iron filings. The ratio of detected iron filings is then computed to establish cleaning priorities, which the embedded system uses to direct the servo motor for effective cleaning. Additionally, the system features a human-machine interface that monitors the cleaning process, providing on-site personnel with real-time insights into the machine's internal conditions. Experimental results from a custom simulation environment demonstrate that the system efficiently reduces the accumulation of iron filings on the platform to levels below the set threshold within a short time frame. This research aims to minimize machine tool idle time, lower personnel costs, and enhance overall production capacity. © 2024 IEEE.

关键词： Nozzles

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