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End-to-End Autonomous Driving Algorithm Based on PPO and Its Implementation

End-to-End Autonomous Driving Algorithm Based on PPO and Its...

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data driven control and learning systems (ddcls)

作者： Jianfeng Zhao Yang Zhao Weixia Li Chaoqun Zeng School of Automative and Transportation Engineering Shenzhen Politechnic University Shenzhen P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the commercialization of vehicles equipped with partial autonomous driving capabilities, achieving fully autonomous driving remains a hot topic among researchers. In contrast to modular autonomous vehicle control, end-to-end autonomous driving aims to replicate human driving by directly translating sensor inputs into vehicle control outputs, making it the ultimate goal in autonomous driving. In this study, we propose an end-to-end autonomous driving algorithm based on the Proximal Policy Optimization (PPO) reinforcement learning algorithm and verify its feasibility. Firstly, we construct an autonomous driving algorithm based on the PPO algorithm, where pre-encoded images and vehicle state information are input to a policy based on deep neural networks, directly generating speed and steering controls for the vehicle, thus achieving true end-to-end autonomous driving. Furthermore, using the CARLA simulation environment for autonomous vehicles, we define a reward mechanism for driving behavior and establish a reinforcement learning environment to train and validate the proposed algorithm. Finally, we analyze the algorithm's feasibility based on rewards, cumulative average rewards, average episode length, and average driving distance. While the algorithm still has a long way to go before practical application, it lays the foundation for further implementation of end-to-end autonomous driving algorithms.

关键词： Training Time series analysis Reinforcement learning Prediction algorithms Cameras data models Trajectory

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Improved YOLOv8 Based Small Object Detection for Intelligent Robotic Arm in Complex Environments

Improved YOLOv8 Based Small Object Detection for Intelligent...

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data driven control and learning systems (ddcls)

作者： Zhiqing Li Dinglinag Ren College of Information Science and Technology Beijing University of Chemical Technology Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Small objects in complex environments may be very close to the background, which brings great challenges to computer vision based intelligent robotic arms. To address these problems, an improved YOLOv8 based small object detection method is proposed in this paper. Firstly, the YOLOv8 prediction heads are reconstructed to improve the prediction performance of small objects. An adaptive multi-scale convolutional attention module (MSCA) is introduced in the backbone which leverages multi-scale features to apply spatial attention with element multiplication. Part of convolutional modules is replaced with GhostNet blocks. these GhostNet blocks generate numerous ghost feature maps with cost-effective linear transformations based on intrinsic feature maps. Finally, the coordinate regression loss function is also optimized with Wasserstein divergence. Experimental results show that the improved YOLOv8 achieves a 0.093 improvement in mean average precision (mAP) compared to vanilla YOLOv8. For practical deployment applications, a sophisticated spatially-aware intelligent robotic arm is proposed by combining the improved YOLOv8 and laser ranging. the practical application results demonstrate that the proposed method has high feasibility and stability.

关键词： Program processors Embedded systems Robot kinematics Computational modeling Object detection Manipulators Real-time systems

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Sequential POI Recommendation Based on Graph Neural Networks

Sequential POI Recommendation Based on Graph Neural Networks

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data driven control and learning systems (ddcls)

作者： Bo Shao Zhichun Jia Hongda Wang Yiwen Wang Xiyu Zhang Xing Xing College of Information Science and Technology Bohai University

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the rapid rise of location-based social networks, POI (point-of-interest) recommendation has become a hotspot in the field of recommendation research; however, existing methods ignore user interactions and POI transfer choices, and the user's preference keeps changing over time, leading to a decrease in the accuracy of recommendation. In order to solve the problem of dynamic changes in user preferences over time, we propose a model based on graph neural networks. the model firstly utilizes the gating mechanism to learn the features of users containing interpersonal connections, and then performs feature extraction; secondly, after demonstrating the intricate relationship between visitors and arrivals using the graph attention layer. We then apply the multi-head attention mechanism based on this so that potential feature vectors (embeddings) focus on multifaceted information, thus making their associations more accurate. the user's preferences are then captured and finally the next POI is recommended. Experiment using two real datasets, Gowalla and Foursquare, show that our proposed approach improves recall and mrr by 5% and 3%, respectively, as compared to other approaches, thus suggesting that this improvement has better recommendation performance.

关键词： learning systems Adaptation models Technological innovation Accuracy Social networking (online) Feature extraction Graph neural networks

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A data-driven Based Blind Path Recognizing and Tracking control Strategy for a Guided Quadruped Robot

A Data-Driven Based Blind Path Recognizing and Tracking Cont...

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data driven control and learning systems (ddcls)

作者： Shida Liu Yunlong Sun Honghai Ji Li Wang School of Electrical and Control Engineering North China University of Technology Beijing People's Republic of China Key Laboratory of Marine Intelligent Unmanned Swarm Technology and System (Shanghai University) Ministry of Education

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this study addresses the motion control issues of a guiding quadruped robot and proposes a novel data-driven approach for blind path recognition and tracking. the proposed approach consists of two parts, including blind path recognition and robot tracking control. In terms of visual principles, the guiding quadruped robot identifies blind paths based on color using the Otsu threshold segmentation algorithm, combined with Canny edge detection and Hough transform for precise positioning of the blind path. Next, this study presents a model-free adaptive control method utilizing a dynamic linearization technique to follow the identified blind path. the controller is configured with a pseudo partial derivative (PPD) parameter. the proposed method enables accurate recognition and tracking of blind paths by the robot, thereby achieving precise following of blind paths by the guiding quadruped robot. the control aspect of the proposed method relies on a model-free strategy, eliminating the requirement to establish an exact mathematical model of the guiding robot during the controller design process. Instead, the controller can be achieved solely using input and output data of the robot. the proposed method was applied to a real guiding quadruped robot experimental platform, and the experimental results demonstrated a 96.24% accuracy in blind path recognition and an average deviation angle of 0.17 degree in tracking control. the effectiveness of this approach has been validated.

关键词： Visualization Accuracy Tracking Robot control Process control Transforms Mathematical models

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Full Wavelength Selection and Deep Spectra Feature Extraction Network for Near-Infrared Spectroscopy Quantitative Analysis

Full Wavelength Selection and Deep Spectra Feature Extractio...

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data driven control and learning systems (ddcls)

作者： Jinlong Liu Xiaoli Luan Fei Liu Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Jiangnan University Wuxi Jiangsu P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Near-infrared spectroscopy (NIRS) has been widely used in the chemical industry and has attracted much attention because of its fast and non-destructive characteristics. However, spectra are susceptible to interference from external environmental factors, leading to spectral overlap and noise problems. To solve these problems, spectral wavelength selection can be performed. Traditional selection methods require manually choosing the appropriate method based on spectral data and combining it with appropriate regression modeling, which is complicated and time-consuming. In contrast, this study proposes a novel spectral wavelength selection method that intelligently filters the entire wavelength based on the correlation between spectra and output variables. Simultaneously, it utilizes a deep spectra feature extraction module to obtain a more abstract spectral representation applied in regression prediction. Evaluation results on the training and test sets demonstrate the outstanding performance of this model: RMSE of 0.007796 and 0.01036 for the training and test sets, respectively, R2 of 0.9918 and 0.9842, with an impressive RPD of 7.9703. these metrics indicate that this model meets the rigorous requirements for high-performance models in the chemical engineering domain.

关键词： Training Analytical models Statistical analysis Noise Predictive models Feature extraction Prediction algorithms

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the Instance Segmentation and Grasping Sequence Method for Stacked Workpieces

The Instance Segmentation and Grasping Sequence Method for S...

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data driven control and learning systems (ddcls)

作者： Chunfang Liu Chen Chen Faculty of Information and Technology Beijing University of Technology Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

To obtain the grasping sequence of the robotic arm for grasping target objects in a stacked workpiece industrial scenario, this paper proposes an instance segmentation and grasping sequence method for stacked workpieces. Firstly, a Mask R-CNN instance segmentation network based on boundary refinement is designed to obtain more accurate instance mask information. Additionally, a Euclidean clustering method based on smoothness parameters is integrated to correct extreme cases of mis-segmentation by the improved Mask R-CNN instance segmentation network from the perspective of three-dimensional space (where adjacent closely stacked workpieces are mis-segmented as the same workpiece). Secondly, a stacking workpiece grasping sequence filtering mechanism is proposed. this mechanism determines the occlusion status of the target workpiece to be grasped by judging the relationship between visible masks and complete masks obtained through instance segmentation. Furthermore, based on the grasping judgment scores, the workpieces in unoccluded states are arranged in sequence, filtering out target workpieces that satisfy the principle of grasping unoccluded state workpieces first, in a top-to-bottom manner. Experimental results demonstrate that the proposed method can effectively infer the grasping sequence in stacked workpiece environments, thereby driving the robotic arm to perform grasping tasks.

关键词： Instance segmentation learning systems Accuracy Filtering Service robots Stacking Grasping

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AMING:Adaptive-learning Mechanism Based Missing-Value Imputation Networks Using GAN in Industrial Processes

AMING:Adaptive-Learning Mechanism Based Missing-Value Imputa...

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data driven control and learning systems (ddcls)

作者： Jiale Zhang Xiaofeng Yuan Kai Wang Yalin Wang Chunhua Yang Weihua Gui School of Automation Central South University Changsha

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In industrial production, uncontrollable factors can lead to the inability to collect or transmit data, resulting in the widespread presence of missing values in industrial datasets. these missing data may contain crucial information for production and serve as a vital source for diagnosing equipment malfunctions or issues. Incomplete datasets directly impact subsequent modeling, prediction, and optimization tasks. However, traditional imputation methods struggle to capture complex data distributions and structures, making them unsuitable for production tasks that require high imputation accuracy. Due to its proficiency in simulating data distributions, generative networks have emerged as effective tools for handling missing or corrupted data. the classic GAIN model introduces a prompting mechanism, providing fixed prompts to the discriminator, but this may lead to issues such as model degradation, pattern collapse, and convergence difficulties. In this paper, we propose a novel unsupervised adaptive imputation framework based on GANs, referred to as AMING. It aims to impute missing data in industrial production processes in an unsupervised adaptive manner. Furthermore, we introduce an adaptive learning mechanism framework that dynamically provides learning information to the discriminator during the training process. Finally, we conduct experimental validation on a hydrocracking dataset, demonstrating the superior performance of the AMING method.

关键词： Training Adaptation models Adaptive learning Time series analysis Production Predictive models Generative adversarial networks

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Remaining Useful Life Prediction of Rolling Bearings based on CAGRU-HF

Remaining Useful Life Prediction of Rolling Bearings based o...

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data driven control and learning systems (ddcls)

作者： Chao Li Weimin Zhai Zhangpeng Yan School of Information Science and Technology University of Science and Technology of China Hefei P. R. China Institute of Advanced Technology University of Science and Technology of China Hefei P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Remaining useful life (RUL) prediction is crucial to avoid sudden failures and improve equipment reliability. Deep learning (DL) techniques have shown effectiveness in RUL prediction. However, many existing works lack attention to important features extracted from data and ignore the enhancing effect of prior knowledge on the prediction network. To address these issues, a novel convolutional attention gated recurrent unit network integrating hand-crafted features (CAGRU-HF) is proposed for bearings RUL prediction. Firstly, in the automatic learning branch, one-dimensional convolution neural network (1DCNN) is used to automatically learn the bearing degradation information, which are then weighted by a novel average-maximum-standard deviation channel attention module (AMSCA), thereby enhancing the 1DCNN feature extraction capability. then, gated recurrent unit (GRU) is used to learn temporal dependencies among features. In addition, Pearson correlation coefficient (PCC) is applied to select hand-crafted features that are highly correlated to the bearing degradation from some indicators. In this way, mixed degradation features are obtained by concatenating the automatically learned features and hand-crafted features. Finally, fully connected layers are applied to predict the RUL. Taking the ieee PHM2012 challenge bearing dataset as an example, the effectiveness of the proposed method is demonstrated.

关键词： Degradation Vibrations learning systems Neural networks Rolling bearings Logic gates Predictive models

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Denoising Diffusion Generative Adversarial Network Integrating Multi-Scale CNN

Denoising Diffusion Generative Adversarial Network Integrati...

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data driven control and learning systems (ddcls)

作者： Yanmin Cao Fenglian Dong Zhiyi Xin Zhiwei Wei Longkun Han Key Laboratory of Oil & Gas Business Chain Optimization CNPC PetroChina Planning and Engineering Institute Beijing China PetroChina Daqing Petrochemical Company Heilongjiang China College of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the progress of networking technology, network attack become increasingly diverse and lead to more severe consequences from emerging attacks. the unbalanced data has a great impact on modeling accuracy. In the vast amount of network traffic, the number of different network attack samples varies greatly. therefore, balancing the network traffic dataset and detecting different network attacks is an urgent problem to be addressed. To tackle this challenge, this paper proposes a denoising diffusion generative adversarial network (DDGAN) integrating multi-scale CNN (MCNN) (DDGAN-MCNN). the DDGAN augments the minority class samples in the traffic dataset and generate synthetic samples to achieve data balancing and enhance the detection rate of minority class intrusions in detection process. Furthermore, a MCNN model is employed as the classifier to identify different intrusions, presenting a comprehensive DDGAN-MCNN intrusion detection method. Experimental results on the UNSW-NB15 and CIC-IDS2017 network traffic datasets demonstrate our method remain higher accuracy compared to other data generation and detection models, that validate the superiority and feasibility of the proposed approach.

关键词： Training learning systems Accuracy Convolution Noise reduction Network intrusion detection Telecommunication traffic

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Marine Navigation Radar Multi-Target Tracking Using Adaptive Innovation Sequence-Based Joint Probability data Association

Marine Navigation Radar Multi-Target Tracking Using Adaptive...

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data driven control and learning systems (ddcls)

作者： Qiang Wang Xiao Huang Hao Tao Zhiwei Xia Chun Liu Xiaoqiang Ren School of Mechatronic Engineering and Automation Shanghai University Shanghai China China Ship Development and Design Center Wuhan China Institute of Artificial Intelligence Shanghai University Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

To address the challenges associated with marine navigation radar tracking in scenarios characterized by dense clutter and high-speed targets, this paper presents a multi-target tracking approach featuring an adaptive innovation sequence-based non-linear joint probability data association (AIS-JPDA) algorithm. the introduction of a straightforward calculation method for association probability aims to mitigate the issue of incorrect associations in target tracking within dense clutter environments. this method incorporates common measurements and their membership degrees to rectify the association probability, thereby enhancing the accuracy and real-time efficiency of multi-target track associations. the adaptive innovation policy is integrated to allow for the dynamic adjustment of the covariance matrix, accommodating process and measurement noise in non-linear maneuvering models. Furthermore, a lifecycle-based target track maintenance method is introduced to tackle the challenge of track disconnection resulting from high target relative velocity. Finally, simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： Technological innovation Target tracking Heuristic algorithms Probability Radar tracking Sonar navigation Real-time systems

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