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A Lightweight Steel Strip Defect Detection Method Based on YOLO 43

A Lightweight Steel Strip Defect Detection Method Based on Y...

引用

43rd Chinese control conference, CCC 2024

作者： Qian, Hao Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

As an essential component of modern industry, steel strips play an indispensable role in various fields and serve as a crucial raw material in industrial production. However, due to various factors such as production technology, the surface of steel strips often exhibits unavoidable defects like scratches and crazings. Therefore, implementing online surface quality inspection for steel strips is one effective way to enhance product quality and production efficiency. This paper proposes a detection method for surface defects in steel strips based on YOLO. By optimizing the network structure of YOLOv5s, the performance on the NEU-DET dataset has improved by nearly 4 % compared to the original network's mAP value. Experimental results indicate that the detection performance of this method is significantly superior to the current mainstream lightweight detection networks. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Strip metal

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'Less Knowledge is Less' - An Empirical Study of Intelligent Traffic Signal network Efficiency Under Partial Information

'Less Knowledge is Less' - An Empirical Study of Intelligent...

引用

2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Shen, Yanming Wang, Zepu Sun, Peng Hu, Yulin Boukerche, Azzedine Duke Kunshan University China Wuhan University China University of Ottawa Canada

ISBN: (纸本)9798350351255

This article investigates the influence of limited information on the efficacy of traffic signal control algorithms for supporting Intelligent Transportation systems. Our project has collected several classic and trending intelligent traffic signal control schemes and evaluated algorithmic performance under constrained data conditions. In these simulating scenarios, access to certain traffic data is restricted. The findings reveal that although information scarcity generally degrades algorithm performance, algorithms that perform well with comprehensive data maintain their superiority even in data-limited environments. This underscores the necessity of designing resilient algorithms capable of adapting to varying levels of information availability, offering valuable insights for developing robust traffic control systems. © 2024 IEEE.

关键词： Algorithmic resilience Information scarcity Intelligent Transportation System Reinforcement learning Traffic management efficiency Traffic signal control

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Casting Discontinuity Detection Method Based on Deep Learning Algorithms 25

Casting Discontinuity Detection Method Based on Deep Learnin...

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25th International Carpathian control conference (ICCC)

作者： Przepiora, Jan Ksiazek, Piotr AGH Univ Krakow Fac Mech Engn & Robot Krakow Poland

ISBN: (纸本)9798350350708;9798350350715

This paper concerns the detection of material discontinuity and inclusions in self-excited acoustic aluminium systems through the application of artificial intelligence methods. Classical machine learning techniques and convolutional neural networks were employed to tackle the binary classification problem associated with inclusion detection. The process shown involves the partitioning of base signals into samples for machine learning, followed by spectral analysis. Various feature extraction methods were compared, including Mel-Frequency Cepstral Coefficients (MFCC), short-time Fourier transform, Power Spectral Density (PSD) as well as wavelet transform. The study evaluates the efficacy of each method in capturing essential information for inclusion detection within the acoustic signals. Additionally, Principal Component Analysis (PCA) was employed to reduce dimensionality, and its impact on classifier performance was thoroughly analysed. The results of this research provide insights into the effectiveness of different neural network architectures and feature extraction techniques for the task of inclusion detection in self-excited acoustic cast aluminium systems. The findings contribute to the advancement of techniques for enhancing the quality control for the production of such systems through robust and efficient detection mechanisms.

关键词： Self-excited system Machine learning Quality control Spectral analysis

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Research on network Security Intrusion Detection Based on Devnet 24

Research on Network Security Intrusion Detection Based on De...

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3rd International conference on Signal Processing, Computer networks and Communications, SPCNC 2024

作者： Zheng, Hao Sun, Dongxu Han, Xiao Zhang, Xiaohua Zhao, Yan Co. Ltd. Hebei Shijiazhuang China Guodian Hefeng Wind Power Development Co. Ltd. Liaoning Shenyang China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

ISBN: (纸本)9798400710834

With the promotion of industrial 4.0 and intelligent manufacturing, the network security of industrial control system becomes particularly important. In the face of new network attacks, the traditional intrusion detection system (IDS) has been difficult to meet the protection requirements, so it is particularly urgent to research and develop efficient network security detection technology. In this study, an intrusion detection method of industrial control system based on DevNet network is proposed, which uses deep convolution neural network architecture to detect events and locate evidence by analyzing the characteristics of key frames, which significantly improves the performance of multimedia event detection. This method optimizes the anomaly score through end-to-end learning, makes effective use of the limited marked abnormal data, and enhances the accuracy and real-time performance of detection. The research first examines the challenges of network security in industrial control systems, and then describes the DevNet framework and its core algorithms, including exception score learner, normal a priori and Z-Score deviation loss function. Through the experimental evaluation of multiple data sets, DevNet performs well in key evaluation indicators such as AUC-ROC and AUC-PR, especially in dealing with large-scale network traffic and defending against zero-day attacks. The interpretability of DevNet provides strong support for security analysis and enhances the credibility of the model prediction. This research not only enhances the protection of network security, but also promotes technological innovation, and provides new ideas for the field of network security. With the increase of iioT equipment, DevNet is expected to become the key technology to protect the safety of industrial control system. The results of this research are of great significance in theory, and show broad prospects in practical application, and contribute to the progress of network security. ©

关键词： network intrusion

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Identifying Electrical Issues by Cascade Bayesian-Regularization Neural network (CBRNN) 12

Identifying Electrical Issues by Cascade Bayesian-Regulariza...

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12th IEEE conference on systems, Process and control, ICSPC 2024

作者： Qassab, Karam Sameer Qasim Al-Khalaf, Imad Idan Abed Al-Nima, Raid Rafi Omar Technical Institute Northern Technical University Department of Electrical Techniques Mosul Iraq Northern Technical University Technical College of Engineering for Computer and Artificial Intelligence / Mosul Mosul Iraq

ISBN: (纸本)9798350391398

Proper detection of electrical power grid faults is a crucial and challenging responsibility for maintaining system reliability, performance, and component longevity. This is because power demand increases and distribution systems expand. Nowadays, Artificial Neural networks (ANNs) are increasingly recognized as a valuable technique for recognition due to their ability to identify intricate patterns and generate accurate forecasts. This paper proposes the Cascade Bayesian-Regularization Neural network (CBRNN) to accurately and successfully identify electrical issues such as faults. A model's normal state and faults are collected from a database of Electrical Fault Detection and Classification (EFDC), which is downloaded from Kaggle. Ground, phases, currents and voltages are employed as inputs for the CBRNN. Such interesting performance is obtained. That is, the Mean Square Error (MSE) is achieved 0.0219. The CBRNN can successfully detect and diagnose electrical infrastructure faults, preventing interruptions, enhancing safeguarding, and decreasing financial losses. © 2024 IEEE.

关键词： Power distribution faults

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Event-Triggered Iterative Learning control for MIMO Linear systems Over Fading Channels 10

Event-Triggered Iterative Learning Control for MIMO Linear S...

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10th International conference on systems and Informatics, ICSAI 2024

作者： Gao, Wenjie Li, Junmin School of Mathematics and Statistics Xidian University Shaanxi Xi'an710126 China

ISBN: (纸本)9798331510138

This research delves into the application of an event-driven iterative learning control (ET-ILC) strategy for MIMO linear systems operating in fading channel conditions. The P-type iterative learning controller determines whether to update by the event-triggered condition. The new ET-ILC strategy proposed in this paper is closely related to the two dimensions of time and number of iterations. This strategy not only ensures that the system can achieve accurate tracking performance, but also effectively saves valuable network communication resources. Finally, it is theoretically proved that the control output tracks the desired trajectory in the sense of mathematical expectation, and the effectiveness of the proposed algorithm and the saving of communication resources are verified by simulation experiments. © 2024 IEEE.

关键词： Fading channels

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20th Chinese Intelligent systems conference, CISC 2024

20th Chinese Intelligent Systems Conference, CISC 2024

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20th Chinese Intelligent systems conference, CISC 2024

ISBN: (纸本)9789819786534

The proceedings contain 202 papers. The special focus in this conference is on Chinese Intelligent systems. The topics include: Modeling of Interval Type-2 Fuzzy Logic System by Semi-tensor Product;Robust Adaptive control of Missiles Based on Fuzzy RBF Neural network;deep Reinforcement Learning of Physically Simulated Character control;the Game of Degree of Target-Attacker-Defender Game With Non-zero Capture Radius;Visual Inertial 3D Reconstruction System Based on KLT Optical Flow and Voxel Hash;analysis of Students’ Class Status Based on Deep Learning;fault-Tolerant Attitude control of Hypersonic Flight Vehicles Based on Extended Adaptive Iterative Learning;predefined-Time control of Robot systems with Prescribed performance Guarantees;dynamic Coverage of Unicycle Robots with Anisotropic Sensing for Time-Priority;method for Evaluating the Multi-factor Decision-Making Efficiency Based on Dynamic Bayesian network;submarine Cable Target Search Based on Improved Biological Neural network;end-to-End mmWave-Based Human Pose Estimation from Raw Signal;urban Sewage Treatment Plant Simulation System Based on Industrial Internet;Load Matching Design and Multi-objective Optimization of Helicopter EHA;Testability Design and Verification of Typical Functional Links of CNI System;adaptive Sliding Mode Tracking control for Multiple High-Speed Trains with Actuator Saturation and Parameter Uncertainties;Abnormal Data Detection Based on Dual-Factor Weighted SVDD for Multimode Batch Processes;dual-Branch Attention Dense Residual network Based on Negative Image for Image Denoising;Research on Testability Work Planning Technology for Aviation Equipment Based on DoDAF;aero Engine Instability Prediction and Detection Method Based on Gated Recurrent Neural networks;Seven-Degrees-of-Freedom Robotic Arm Path Planning Based on Improved RRT;dynamic Event-Triggered Based Coherent Formation control for Multi-robot systems;adaptive Prescribed Time Prescribed performance control for High-Order No

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20th Chinese Intelligent systems conference, CISC 2024

20th Chinese Intelligent Systems Conference, CISC 2024

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20th Chinese Intelligent systems conference, CISC 2024

ISBN: (纸本)9789819786497

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Learning-based Nonlinear Model Predictive control Using Deterministic Actor-Critic with Gradient Q-learning Critic 18

Learning-based Nonlinear Model Predictive Control Using Dete...

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18th International conference on control Automation Robotics and Vision

作者： Salaje, Amine Chevet, Thomas Langlois, Nicolas Univ Rouen Normandie IRSEEM ESIGELEC F-76000 Rouen France

ISBN: (纸本)9798331518509;9798331518493

In this paper, we present an off-policy reinforcement learning (RL) method used to tune the optimal weights of a nonlinear model predictive control (NMPC) scheme. The objective is to find the optimal policy minimizing the closed-loop performance of point stabilization with obstacle avoidance control task. The parameterized NMPC scheme serves to approximate the optimal policy and update the parameters via compatible off-policy deterministic actor-critic with gradient Q-learning critic (COPDAC-GQ). While efficient, this algorithm requires a heavy computational complexity when combined with NMPC, as two optimal control problems have to be solved at each time instant. We therefore propose two different methods to reduce the real-time computational cost of the algorithm. First, a neural network is used to learn the subsequent stateaction features of the advantage function. Then, we propose to use the information delivered by the NMPC scheme to approximate the subsequent state-action features in the critic. Whichever method is used removes the need of a secondary NMPC, significantly improving the training speed. The results show that there is no difference between the original method and the proposed methods in terms of the learned policy and the control performance, whereas the real-time computational burden is almost halved with the proposed methods.

关键词： Predictive control systems

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An Adaptive Dropout and Parallel Computing Approaches for Accelerating RNN controller 15

An Adaptive Dropout and Parallel Computing Approaches for Ac...

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15th Ubiquitous Computing Electronics and Mobile Communication conference

作者： Surendranath, Kushal Kalyan Devalampeta Sam, Maxwell Qingge, Letu North Carolina A&T State Univ Dept Comp Sci Greensboro NC 27411 USA

ISBN: (纸本)9798331540913;9798331540906

The efficiency and performance of neural network (NN) controllers present a significant challenge in the rapidly evolving landscape of real-time closed-loop control systems, such as those used in solar inverters. This paper introduces a novel approach that enhances training efficiency by combining adaptive dropout with parallel computing techniques, utilizing the Levenberg-Marquardt (LM) algorithm and Forward Accumulation Through Time (FATT). Unlike traditional dropout methods that apply a fixed dropout rate uniformly across all neurons, Adaptive Dropout dynamically adjusts the dropout rate based on each neuron's calculated importance and its stage in the training process. This allows for the protection of more critical neurons while regularizing less significant ones, thereby improving convergence speed and enhancing generalization in the neural network controller. To further accelerate the training process, the Adaptive Dropout method is seamlessly integrated into a parallel computing architecture. This architecture employs multiple cores to compute Dynamic Programming (DP) costs and Jacobian matrices for various trajectories simultaneously. This approach not only harnesses the computational power of modern multi-core systems but also ensures efficient processing across all trajectories. The experimental results demonstrate that adaptive dropout with parallel computing provides improvements in training efficiency and overall performance than both no dropout and weight dropout control schemes.

关键词： Neural network controller Solar Inverter Adaptive Dropout Parallel Computing Levenberg-Marquardt (LM) Algorithm Forward Accumulation Through Time (FATT)

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