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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Gábor Knyihár Kristóf Csorba Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

In vitro dissolution prediction is a field of phar-maceutical research that aims to predict a tablet's dissolution profile using non-destructive methods based on the tablet's measured manufacturing parameters or composition. One of the most popular non-destructive measurements is Raman chemical imaging, which outputs a Raman map. However, because of the current development of the Raman imaging technique, obtaining large training datasets is very expensive and time-consuming. Still, training popular complex deep learning architectures that may achieve better accuracy is only possible with a large dataset. Therefore this article focuses on utilising pre-trained networks and the transfer learning technique to attempt to solve the problem of the small size of training samples. The results showed that deep neural networks with data augmentation and transfer learning could achieve better accuracy than simple convolutional neural networks. The presented strategies showed the need for an extensive dataset could be handled, and the accuracy could be enhanced, although there is room for further refinement.

关键词： Training Accuracy Uncertainty Current measurement Transfer learning Imaging Manufacturing

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Improving Traffic Signal control With Joint-Action Reinforcement Learning

Improving Traffic Signal Control With Joint-Action Reinforce...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Labres, Joao V. B. Bazzan, Ana L. C. Abdoos, Monireh Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Shahid Beheshti Univ Fac Comp Sci & Engn Tehran Iran

ISBN: (纸本)9781728190488

An increasing demand for mobility in urban areas leads to several issues such as traffic congestion. While many approaches exist to tackle this problem, a popular one is the adaptive control of traffic signals. This paper focuses on reinforcement learning based approaches for adaptive control. These use learning agents that act individually to adapt the signal timings to the current demand of vehicles crossing an intersection. However, in a network of intersections (thus constituting a multiagent reinforcement learning problem), as the actions of each agent have impact on the states, actions, and rewards of others, it is clear that there is a relevant non-local issue that is rarely dealt with. Thus, this paper proposes a joint learning approach in which agents learn in groups, besides also learning an individual policy. This means that we have two parallel learning processes. The group policy is implemented from time to time, thus helping agents to implicitly consider other agents' policies. A distinguishing feature of our approach is that it is not based on fixed membership in such groups;rather, groups are formed and dissolved on demand, based on their performance. Results of applying this approach are compared to a baseline and to when agents learn individually only, showing that the joint learning leads to lesser waiting time.

关键词： reinforcement learning traffic signal control

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Experiments with a One Parameter Computed Torque Position control Method Using a PLC and a Simple Linear Actuator

Experiments with a One Parameter Computed Torque Position Co...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Árpád Varga János Fricz Zoltán Braun Obuda University Budapest Hungary Doctoral School of Applied Informatics and Applied Mathematics

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

In engineering practice, a very common problem to solve is to guide a tool, a moving part of a machine, or the vehicle itself along a predefined path or trajectory with the smallest possible deviation. If the desired nominal trajectory is known and can be derived at least twice with respect to time, and a kinematic model of the system is available, then the force or torque required to achieve the trajectory can be calculated from the nominal acceleration (this method is often referred as CTC: Computed Torque control). However, since the exact kinematic model is not available in most cases, the system parameters may vary during operation, and external disturbing forces and torques may be present, constant error feedback is required to calculate the acceleration required to maintain the desired trajectory. In practice, the implementation of CTC can be greatly simplified by tuning only one feedback parameter value. This paper presents a demonstration and experimental application of such a single-parameter CTC method for a single axis linear positioning.

关键词： Torque Trajectory tracking Force Torque control Kinematics Displacement measurement Trajectory

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Koopman NMPC: Koopman-based Learning and Nonlinear Model Predictive control of control-affine Systems

Koopman NMPC: Koopman-based Learning and Nonlinear Model Pre...

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ieee International Conference on Robotics and automation (ICRA)

作者： Folkestad, Carl Burdick, Joel W. CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA

ISBN: (纸本)9781728190778

Koopman-based learning methods can potentially be practical and powerful tools for dynamical robotic systems. However, common methods to construct Koopman representations seek to learn lifted linear models that cannot capture nonlinear actuation effects inherent in many robotic systems. This paper presents a learning and control methodology that is a first step towards overcoming this limitation. Using the Koopman canonical transform, control-affine dynamics can be expressed by a lifted bilinear model. The learned model is used for nonlinear model predictive control (NMPC) design where the bilinear structure can be exploited to improve computational efficiency. The benefits for control-affine dynamics compared to existing Koopman-based methods are highlighted through an example of a simulated planar quadrotor. Prediction error is greatly reduced and closed loop performance similar to NMPC with full model knowledge is achieved.

关键词： Learning systems Dictionaries System dynamics computational modeling Transforms Predictive models Tools

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Type-2 Fuzzy LSTM for Nonlinear System Modeling

Type-2 Fuzzy LSTM for Nonlinear System Modeling

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ieee symposium Series on computational intelligence (SSCI)

作者： Francisco Vega Xiaoou Li Brisbane Ovilla-Martínez Wen Yu Departamento de Control Automático CINVESTAV-IPN Mexico City Mexico Departamento de Computación CINVESTAV-IPN Mexico City Mexico

Type-2 fuzzy systems have a great adoption in different branches of engineering, due to the fact that this type of fuzzy systems are very well suited to tasks related to nonlinear systems. Data driven models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we show the advantages of type-2 fuzzy systems over type-1 fuzzy systems in modeling nonlinear systems. We combine Type-2 Takagi-Sugeno fuzzy model with the popular deep learning model, LSTM (long-short term memory), to overcome the disadvantages fuzzy model and neural network model. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in order to show the advantages of the type-2 fuzzy LSTM neural networks.

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Dynamics Analysis and control of Memristor-Based Hopfield Neural Networks under Electromagnetic Radiation

IEEE Transactions on Artificial Intelligence

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ieee Transactions on Artificial intelligence 2025年

作者： Wang, Mingxin Zhu, Song Luo, Weiwei Zhang, Zhen Mu, Chaoxu China University of Mining and Technology School of Mathematics Xuzhou221116 China Tianjin University School of Electrical and Automation Engineering Tianjin300072 China

As is well known, the widespread use of electrical equipment, the solar storm or malicious attacks may result in a large or even strong electromagnetic radiation (ER) in the actual environment. Hopfield neural network (HNN) is a computational model that has been widely applied in multiple fields, but its dynamic characteristics after exposure to ER have not been systematically considered. With that in mind, this paper investigates the dynamics analysis and control for a class of memristor-based HNNs (MHNNs) under the influence of ER. Firstly, a new type of MHNNs model that is affected by ER is proposed, in which a resulting current is introduced to simulate the effects of ER. On this basis, some sufficient criteria for the boundedness of system states and stability strategies are given based on the Lyapunov function method. Subsequently, the synchronization control problem of the driving-response MHNNs under the influence of ER is studied by using Lyapunov stability theory and some inequality techniques. The stabilization and synchronization controller frameworks proposed in this paper can respectively achieve exponential, finite-time, and fixed-time stability and synchronization by adjusting parameter conditions. Three simulation experiments have verified the effectiveness of the above criteria and control strategies. Finally, based on the fixed-time synchronization control scheme, the theoretical results of this paper are applied to the image encryption. © 2020 ieee.

关键词： Heat radiation

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Quantized Adaptive Fault-like Model-based SMC Design for Vehicular Platoon with Actuator Deadzone and Distance Restrictions 5

Quantized Adaptive Fault-like Model-based SMC Design for Veh...

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5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022

作者： Lu, An Yang, Wan-Yi Gao, Zhi-Yi Guo, Xiang-Gui School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

This paper aims to design an adaptive fault-like model-based sliding-mode control (SMC) law for a class of vehicle platoon systems, where the follower vehicles have quantized input, unknown actuator deadzone, and distance restrictions. Using adaptive terminal sliding mode control method and barrier Lyapunov function (BLF), an adaptive controller is designed to ensure the collision avoidance and communication connection keeping. In addition, the problem of the unknown actuator deadzone and input quantization is first transformed to a fault-like problem, which will simplify the control design procedure. Finally, the effectiveness of the proposed control strategy is verified by a numerical example. © 2022 ieee.

关键词： Sliding mode control

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V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection with BEV Global Non-Maximum Suppression

V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection ...

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ieee symposium on Intelligent Vehicle

作者： Caiji Zhang Bin Tian Shi Meng Shuangying Qi Yang Sun Yunfeng Ai Long Chen School of Artificial Intelligence University of Chinese Academy of Sciences and With the National Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Bin Tian is With the National Key Laboratory for Multi-Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and With the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China National Key Laboratory for Multi-Modal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and With the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chongqing Iron and Steel Group Mining Co. Ltd. Chongqing China School of Mechanical and Equipment Engineering Hebei University of Engineering Handan China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Waytous Inc. Qingdao China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Waytous Ltd. Qingdao China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In recent years, research on Vehicle-to-Everything (V2X) cooperative perception algorithms mainly focuses on the fusion of intermediate features from LiDAR point clouds. Since the emergence of excellent single-vehicle visual perception models like BEVFormer, collaborative perception schemes based on camera and late-fusion have become feasible approaches. This paper proposes a V2X-collaborative 3D object detection structure in Bird's Eye View (BEV) space, based on global non-maximum suppression and late-fusion (V2X-BGN), and conducts experiments on the V2X-Set dataset. Focusing on complex road conditions with extreme occlusion, the paper compares the camera-based algorithm with the LiDAR-based algorithm, validating the effectiveness of pure visual solutions in the collaborative 3D object detection task. Additionally, this paper highlights the complementary potential of camera-based and LiDAR-based approaches and the importance of object-to-ego vehicle distance in the collaborative 3D object detection task.

关键词： Point cloud compression Visualization Three-dimensional displays Laser radar Roads Collaboration Object detection

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Nonlinear control of Crane Systems Based on intelligence Computing of Disturbance Observer Under Mismatched Disturbance

Nonlinear Control of Crane Systems Based on Intelligence Com...

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International symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Tianlei Wang Yiyan Wang Meng Xu Jiajie Tian Jingling Zhang Zhiyong Hong School of Electronic and Information Engineering Wuyi University Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China School of Mechanical and Automation Engineering Wuyi University Jiangmen China Guangdong Kejie Technology Co. Ltd Jiangmen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

A control method based on disturbance observer is proposed to address the positioning drift issue of the gantry crane under unmatched disturbances, aiming at precise positioning of the trolley and effective payload swing angle attenuation in a variable-length two-dimensional lifting system. The control scheme comprises a combination of a proportional-derivative controller and a disturbance observer. Firstly, the design of the coupling function in this paper is based on the observation of the natural characteristics of the dynamical model. It cleverly designs the coupling function, which not only helps to effectively reduce the swing angle but also prevents the problem of positioning drift under non-matching disturbances, subsequently, design a controller by integrating intelligent computing with control theory. Secondly, the designed disturbance observer observes and compensates for the system output to achieve disturbance suppression. Thirdly, a thorough stability analysis has been conducted to demonstrate that the proposed controller satisfies the desired conditions. Finally, simulation results illustrate that the pro-posed method success-fully addresses the limitations of existing methods and exhibits superior performance and robustness.

关键词： Couplings Cranes computational modeling Simulation Attenuation Disturbance observers Stability analysis Robustness Mathematical models Payloads

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An Ensemble Learning to Detect Decision-Based Adversarial Attacks in Industrial control Systems

An Ensemble Learning to Detect Decision-Based Adversarial At...

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ieee symposium Series on computational intelligence (SSCI)

作者： Narges Babadi Hadis Karimipour Anik Islam Department of Electrical & Software Engineering Schulich School of Engineering University of Calgary Calgary Canada

An increasing number of Intrusion Detection Systems (IDSs) rely on Artificial intelligence (AI), specifically Ma-chine Learning (ML) algorithms, to distinguish between benign and malicious data and detect cyber attacks. However, using ML algorithms exposes IDSs to Adversarial Machine Learning (AML) attacks during the training and test phase. These AML attacks aim to deceive ML algorithms by misclassifying data, posing significant disruptions to the system and its users. Two critical categories of AML attacks are White-box and Black-box attacks, with Black-box attacks being more practical and representative of real-world scenarios. This paper investigates the impact of adversarial examples on supervised ML models in IDSs and proposes an ensemble learning-based detection approach. The study uses a power system dataset and employs Random Forest, AdaBoost, and Decision Tree classifiers to achieve this. During the test phase, adversarial examples are generated using the decision boundary and HopSkipJump attacks, two types of Black-box decision-based attacks. The research applies a deep neural network to the dataset containing the generated adversarial examples to detect these AML attacks, achieving an accuracy of 98 % to 99 %.

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