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International symposium on computational intelligence and Informatics

作者： Katalin Ferencz József Domokos Levente Kovács Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Faculty of Technical and Human Sciences Târgu Mure. Romania Sapientia Hungarian University of Transylvania Faculty of Technical and Human Sciences Târgu Mure Sapientia Hungarian University of Transylvania Târgu Mure. Romania John Von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (纸本)9798350398830

The integration of sensors in our everyday lives and in industry presents a serious challenge to data analysis professionals. Since the use of smart devices has exponentially increased the amount of data collected in all areas, we must not only store these data, but also extract valuable information from those using some data analysis method. In many cases, the increased amount of data also causes problems for data analysis algorithms, so we need to be continuously updated and specialized for fundamental purposes. In the article, we will present some data analysis techniques, starting from the simplest statistical methods to more complex techniques using machine learning and presenting one of their possible applications. In our study, we will use the KMeans clustering algorithm and examine its effectiveness in time series sensor data analysis.

关键词： Support vector machines Analytical models Data analysis Machine learning algorithms computational modeling Time series analysis Clustering algorithms

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Hierarchical Reinforcement Learning With Multi Discount Factors In A Differential Game

Hierarchical Reinforcement Learning With Multi Discount Fact...

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ieee symposium Series on computational intelligence (SSCI)

作者： Amirhossein Asgharnia Howard M. Schwartz Mohamed Atia Departmant of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781665487696

Reinforcement learning (RL) is an approach to solving differential games, especially pursuit-evasion games. Re-inforcement learning, treats the model and the environment as black boxes. It is shown that using multi-agent reinforcement learning is a suitable tool to address pursuit-evasion games. This paper proposes a new approach based on hierarchical reinforcement learning to find an instantaneous reward function for the game of guarding a territory, which is a more complex version of pursuit-evasion games. To have greater control over the generated path, a novel modification to the fuzzy actor-critic learning algorithm is proposed, so the augmented algorithm is able to handle several reward functions with different time horizons. The proposed approach has shown to have a suitable control over the combination of a terminal reward function and an instantaneous reward.

关键词： Reinforcement learning Games Differential games computational intelligence

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Monitoring system of plant growth environment temperature based on LabVIEW and BP neural network

Monitoring system of plant growth environment temperature ba...

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ieee Information Technology, Networking, Electronic and automation control Conference

作者： Xudong Cui Liguo Tian Meng Li Han Han Shidong Hou Chunhu Shang College of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin China

In the current agricultural intelligence, the monitoring of plant growth environment parameters needs to use a variety of sensors which also leads to the existing intelligent control system being generally large. The electrical signal is another important signal in plants which contains abundant information besides hydraulic and chemical signals. Plant electrical signals can not only regulate plant growth but also reflect the environment to a certain extent. The collection of Aloe Vera electric signals at different temperatures (15, 19, 23, 27, 31, and 35 ℃) is accomplished by using LabVIEW software and hardware. The signal is denoised by using the module in the signal processing toolkit. The BP network is trained repeatedly by extracting the eigenvalues from the signal and the model with the smallest mean variance is selected as the optimal model. Deploy the model to the computer and use mixed programming with MATLAB to invoke the model. Use the powerful graphical programming and interface development capabilities of LabVIEW to ultimately build a monitoring system for plant growth environment temperature. Making the future agricultural intelligent control system lightweight and portable.

关键词： Temperature sensors computational modeling Plants (biology) Neural networks Programming Mathematical models Eigenvalues and eigenfunctions

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Inertial sensor-based movement classification with dimension reduction based on feature aggregation

Inertial sensor-based movement classification with dimension...

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International symposium on computational intelligence and Informatics

作者： Peter Sarcevic József Sárosi Dominik Csík Ákos Odry Department of Mechatronics and Automation University of Szeged Szeged Hungary

ISBN: (纸本)9798350398830

In this paper, a novel feature aggregation method is proposed for dimension reduction in inertial sensor-based pattern recognition applications. The method is utilized in a movement classification algorithm, which applies measurements of wearable sensor units attached to the wrists of the subjects. The feature aggregation is realized separately for each feature type by using a linear combination of the feature values extracted for each sensor axis. The weights are computed using genetic algorithm-based optimization, which uses the classification efficiency provided by the k-nearest neighbor (kNN) method to compute the fitness value. After the weights are determined for all feature types and the aggregated inputs are computed for both the training and validation data, a multi-layer perceptron (MLP) classifier is trained, which can be used during real-time classification. The algorithm was tested with multiple datasets based on features extracted using different processing window sizes and used sensor combinations. The achieved results show that the proposed weighted aggregation provides significantly, mostly 5-6%, higher recognition rates on validation data than when equal coefficients are utilized during aggregation. The method in the case of some datasets with the same configuration gives even higher classification efficiencies than when using the features extracted for the sensor axes separately.

关键词： Dimensionality reduction Training Wrist Feature extraction Robot sensing systems Pattern recognition Classification algorithms

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Bidirectional Attention Network for Monocular Depth Estimation

Bidirectional Attention Network for Monocular Depth Estimati...

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ieee International Conference on Robotics and automation (ICRA)

作者： Aich, Shubhra Vianney, Jean Marie Uwabeza Islam, Md Amirul Kaur, Mannat Liu, Bingbing Huawei Technol Noahs Ark Lab Markham ON L3R 5Y1 Canada

ISBN: (纸本)9781728190778

In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks. The structure of this mechanism derives from a strong conceptual foundation of neural machine translation, and presents a light-weight mechanism for adaptive control of computation similar to the dynamic nature of recurrent neural networks. We introduce bidirectional attention modules that utilize the feed-forward feature maps and incorporate the global context to filter out ambiguity. Extensive experiments reveal the high degree of capability of this bidirectional attention model over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets - KITTI and DIODE. We show that our proposed approach either outperforms or performs at least on a par with the state-of-the-art monocular depth estimation methods with less memory and computational complexity.

关键词： Recurrent neural networks Conferences computational modeling Memory management Estimation Machine translation Convolutional neural networks

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Adaptation of XAI to Auto-tuning for Numerical Libraries

Adaptation of XAI to Auto-tuning for Numerical Libraries

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ieee International symposium on Embedded Multicore Socs (MCSoC)

作者： Shota Aoki Takahiro Katagiri Satoshi Ohshima Masatoshi Kawai Toru Nagai Tetsuya Hoshino Graduate School of Informatics Nagoya University Japan Information Technology Center Nagoya University Japan Research Institute for Information Technology Kyushu University Fukuoka Japan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

The unregulated utilization of Artificial intelligence (AI) outputs, potentially leading to various societal issues, has received considerable attention. While humans routinely validate information, manually inspecting the vast volumes of AI-generated results is impractical. Therefore, automation and visualization are imperative. In this context, Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. Simultaneously, software Auto-Tuning (AT) technology has emerged for reducing the man-hours required for performance tuning in numerical calculations. AT is a potent tool for cost reduction during parameter optimization and high-performance programming for numerical computing. The synergy between AT mechanisms and AI technology is noteworthy, with AI finding extensive applications in AT. However, applying AI to AT mechanisms introduces challenges in AI model explainability. This study focuses on XAI for AI models when integrated into two different processes for practical numerical computations: performance parameter tuning of accuracy-guaranteed numerical calculations and sparse iterative algorithm.

关键词： Adaptation models Visualization Explainable AI computational modeling Libraries Numerical models Iterative methods Sparse matrices Tuning Optimization

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Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning

Safe Stabilization with Model Uncertainties: A Universal For...

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International Conference on control and automation (ICCA)

作者： Ming Li Zhiyong Sun Department of Electrical Engineering Eindhoven University of Technology and also with the Eindhoven Artificial Intelligence Systems Institute Eindhoven The Netherlands

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

A combination of control Lyapunov functions (CLFs) and control barrier functions (CBFs) forms an efficient framework for addressing control challenges in safe stabilization. Developing an analytical control strategy, known as the universal formula approach, which integrates CLF and CBF conditions, is recognized as a computationally efficient method for achieving safe stabilization. However, successful implementation of this universal formula relies on an accurate model, as any mismatch between the model and the actual system can compromise stability and safety. In this paper, we propose a new universal formula that leverages Gaussian processes (GPs) learning to address the safe stabilization problem in the presence of model uncertainty. By utilizing the results related to bounded learning errors, we achieve a high probability of stability and safety guarantees with the proposed universal formula. Additionally, we introduce a probabilistic compatibility condition to evaluate conflicts between the modified CLF and CBF conditions with GP learning results. In cases where compatibility assumptions fail, we propose a modified universal formula that relaxes stability constraints. We illustrate the effectiveness of our approach through a simulation of adaptive cruise control (ACC), highlighting its potential for practical applications in real-world scenarios.

关键词： Adaptation models Uncertainty computational modeling Gaussian processes Probabilistic logic Stability analysis Safety

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L₁ Adaptive control-Based Formation Tracking of Multiple Quadrotors Without Linear Velocity Feedback Under Unknown Disturbances

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ieee Transactions on automation Science and Engineering 2024年 22卷 5804-5815页

作者： Yang Hu Zhiqiang Miao Yaonan Wang Haoming Tang Xiangke Wang Wei He College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Intelligence Science and Technology and the Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

This paper addresses the problem of formation control for a quadrotor swarm (QS) system with directed graph topology under external environmental disturbances and unreliable internal state acquisition. The proposed distributed robust control framework, based on a gemetric controller, incorporates ${\mathcal {L}}_control$ adaptive controllers and differentiator systems. First, the geometric formation controller is designed to implement the formation control of the nominal system. Then, ${\mathcal {L}}_control$ adaptive controllers are designed separately for each quadrotor’s position loop and attitude loop subsystems to address the effects of uncertainties such as external time-varying disturbances (matched and unmatched disturbances) and different mass variations of quadrotors. Furthermore, the differentiator system is devised to accurately estimate the higher-order derivatives of the non-directly-measurable velocity information and the virtual translation control signal, which enhances system accuracy while reducing computational complexity. The Lyapunov stability theory is employed to analyze the stability of the closed-loop system. Finally, the effectiveness and exceptional performance of this approach in QS formation control were validated through numerical simulation and experimental results. Note to Practitioners—The inspiration for this article comes from the issue of formation control in a cluster of quadrotor drones, which is also applicable to formation control in other types of drones. In this paper, a formation control algorithm based on ${\mathcal {L}}_control$ adaptive control strategy and arbitrary-order differentiation is designed. This algorithm can address not only the issue of time-varying wind disturbances frequently encountered during quadrotor drone flights but also the effects of unpredictable velocities and inconsistent masses of quadrotor drones. The disturbance rejection capability of this scheme enables quadrotor drones to be applied more safely and r

关键词： Quadrotors Formation control Adaptive control Topology Drones Autonomous aerial vehicles Uncertainty

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Gait Planning for Biped Robot Based on Variable Center-of-Mass Height Hybrid Strategy

Gait Planning for Biped Robot Based on Variable Center-of-Ma...

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ieee International Conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Jianjun Yu Ruihua Zhu Daoxiong Gong Miaoqiang Zhou Guoyu Zuo Beijing Key Laboratory of Computational Intelligence and Intelligent Systems Beijing University of Technology Beijing China

In order to make the gait of a bipedal robot more human-like and to further improve the efficiency of energy utilisation, this paper combines preview control strategy and a knee extension strategy to design a bipedal robot gait planning method based on a variable centre-of-mass height hybrid strategy. The core idea is to achieve gait stability and coordination by controlling the robot's centre-of-mass height. Firstly, the horizontal centre-of-mass trajectory of the biped robot is generated based on Preview control strategy with a constant centre-of-mass height; secondly, the vertical centre-of-mass trajectory is generated based on the knee joint extension strategy by trajectory planning of the robot's knee joint; finally, the horizontal and vertical centre-of-mass trajectories and foot point positions are combined by the inverse kinematics of the bipedal robot to plan the offline gait of the bipedal robot with variable centre-of-mass height. The experimental results show that the bipedal robot is able to walk smoothly with the planned gait, with better human-like characteristics, and requires less knee torque than bent knee walking mode with a constant centre of mass height, which improves energy efficiency.

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Design of control system for automatic steel sheet wrapping machine 3

Design of control system for automatic steel sheet wrapping ...

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3rd International symposium on Robotics and Intelligent Manufacturing Technology, ISRIMT 2021

作者： Mao, Yuxiang Xu, Song Dou, Fuping Wang, Lei Fu, Shuangning Machinery Technology Development Co. Ltd Beijing100044 China

ISBN: (纸本)9781665437189

According to the actual process requirements of steel sheet packaging in steel coil packaging process, this paper designs the control system of automatic steel sheet wrapping machine. And this control system includes pneumatic control system design, hydraulic control system design and electronic control system design. The control system takes PLC as the core to control the unwinding, conveying, cutting, glue spraying and wrapping of the steel sheet wrapping machine through the motor, solenoid valve, proximity switch and photoelectric switch. The field results show that the control system can better control the wrapping machine instead of manual, and realize the automation and intelligence of steel sheet wrapping, and greatly improve the packaging efficiency and quality. © 2021 ieee.

关键词： Quality control

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