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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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A Student engineering Competence Level Prediction Method Based on RNN-GRU and Dynamic Bayesian Network

A Student Engineering Competence Level Prediction Method Bas...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Xu, Xiang Li, Guanxiao Xiao, Shaozhang Zhang, Zheyi Huaiyin Institute of Technology School of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

To achieve effective feedback and proficiency tracking of students in contemporary educational settings, we propose a methodology that integrates the RNN-GRU model and dynamic Bayesian networks for predicting students' engineering competence level based on their online learning behaviors. The approach employed utilizes the RNN-GRU model to analyze students' progression in algorithm development skills and algorithm implementation ability, resulting in the generation of a historical record of their learning features. These features are then employed to train a dynamic Bayesian network model, facilitating the prediction of students' engineering competence level. After the amount of data reaches a certain amount, it can compare the previous models such as RNN and LSTM to obtain higher accuracy. © 2023 IEEE.

关键词： Students

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A Ternary-attention Capsule Feature Pyramid Network for Building Footprint Extraction from Remote Sensing Imagery

A Ternary-attention Capsule Feature Pyramid Network for Buil...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Haiyan Li, Yazhou Wang, Meifeng Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

The up-to-date and accurate building footprint database plays a significant role in a large variety of applications. Recently, remote sensing images have provided an important data source for building footprint extraction tasks. However, due to topology variations, color diversities, and complicated rooftop and environmental scenarios, it is still a challenging task to realize fully automated and highly accurate extraction of building footprints from remote sensing images. In this paper, we propose a novel ternary-attention capsule feature pyramid network (TA-CapsFPN), which is formulated with a capsule feature pyramid network architecture and integrated with context-augmentation and feature attention modules, aiming at improving the building footprint extraction accuracy by combining the superior properties of capsule representations and the powerful capability of attention mechanisms. Quantitative evaluations and comparative analyses show that the TA-CapsFPN provides a promising and competitive performance in processing buildings of varying conditions. © 2023 IEEE.

关键词： Extraction

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Research on Chemical Accident News Classification Based on CNN and BiLSTM-Self-Attention

Research on Chemical Accident News Classification Based on C...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zong, Hui Zhao, Yun Zhou, Heng Zhang, Le Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Chemical accident news data encompasses essential information such as news headlines, news content, and news sources, with the context of news content playing a crucial role. To enhance the accuracy of text feature extraction and improve the efficiency of chemical accident news classification, this paper introduces a feature fusion-based classification approach. The proposed model employs a multi-layer convolutional neural network (CNN) to extract local features from the text of chemical accident news. Additionally, a Bidirectional Long Short-Term Memory (BiLSTM) network is utilized to capture global features, supplemented by the integration of a Self-Attention mechanism behind the BiLSTM network to assign weights to the features and reduce noise. The local and global features are then fused to enrich the semantic information. Furthermore, the feature fusion information undergoes maximum pooling and average pooling to reduce dimensionality and enhance the training speed. Finally, the information is fed into a Softmax layer for classification. Experimental results demonstrate that the proposed neural network model, namely ABLSACNN (Add-CNN-BiLSTM-Self-Attention), outperforms the CNN-Self-Attention model. The ABLSACNN model exhibits an improvement of1.59% in accuracy, 2.46% in recall rate, and 1.93% in F1 score on the chemical accident news dataset, thereby showcasing its superiority. © 2023 IEEE.

关键词： Convolutional neural networks

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Air Quality Index Prediction Based on CNN-LSTM-Attention Hybrid Modeling

Air Quality Index Prediction Based on CNN-LSTM-Attention Hyb...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Li, Yinyin Jiang, Tao Gu, Hanzhu Lu, Weibing Wu, Qingguo Yu, Yongtao Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Air Quality Index (AQI) is an important indicator for determining good or bad air quality. The accurate and efficient prediction of AQI plays a positive role in promoting the management of air pollution. However, current algorithms for predicting AQI usually do not comprehensively consider the effects of pollutant factors and meteorological factors on the prediction performance. Therefore, taking pollutant factors and meteorological factors as the basis of the model study, a CNNLSTM-Attention hybrid model is proposed. The CNN-LSTM module is used to obtain the air quality-related features, and the attention mechanism is introduced to weigh and sum the output of the LSTM in order to obtain the final attention-weighted features. The results show that the model has better performance than the single model for the prediction of the air quality index. © 2023 IEEE.

关键词： Air quality

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A Bearing Fault Diagnosis Method Based on Improved Deep Residual Contraction Network

A Bearing Fault Diagnosis Method Based on Improved Deep Resi...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Guo, Shuxin Chen, Xiaobing Zhang, Chuangchuang Wang, Rongrong Lu, Heng Lu, Chengfa Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

In this paper, a method for bearing fault diagnosis based on an improved deep residual contraction network is proposed. The method utilizes the residual contraction module in the deep residual contraction network, which is improved in combination with the Inception network, in order to enhance the diagnostic accuracy and efficiency of bearing faults. The method divides the bearing fault diagnosis problem into several sub-problems and designs the corresponding residual contraction module and Inception network structure for each sub-problem. Through experimental validation using an actual bearing fault dataset, the results demonstrate that the method achieves high accuracy and stability in bearing fault diagnosis, providing a new idea and method for research in the field of bearing fault diagnosis. © 2023 IEEE.

关键词： Failure analysis

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A Semi-Anchoring Guided High-Resolution Capsule Network for Vehicle Logo Recognition

A Semi-Anchoring Guided High-Resolution Capsule Network for ...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Wang, Lanfang Yu, Zhenyang Yu, Yongtao Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

As a unique identity reflecting the manufacturer of the vehicle, the vehicle logo information plays an important role in many transportation-related applications. However, due to the challenges of size variations, shape and form diversities, deformations, occlusions, and complex scenarios, it is still not an easy task to realize highly accurate vehicle logo recognition from images. This paper proposes a novel semi-anchoring guided high-resolution capsule network (SAGHR-CapsNet) for vehicle logo recognition. First, constructed with a multibranch high-resolution capsule network architecture functioned with repeated multiresolution feature fusion for feature extraction, the SAGHR-CapsNet can extract semantically strong and spatially accurate feature representations at each scale. Second, designed with a capsule-based efficient self-attention mechanism for feature semantic promotion, the SAGHRCapsNet functions excellently to attend to channel-wise informative features and target-oriented spatial features. Finally, adopted with a semi-anchoring guided strategy for vehicle logo recognition, the SAGHR-CapsNet performs promisingly to simultaneously improve the processing efficiency and guarantee the recognition accuracy. Intensive quantitative evaluations and comparative analyses on two large-scale data sets demonstrated the applicability and superiority of the SAGHR-CapsNet in vehicle logo recognition tasks. © 2023 IEEE.

关键词： Vehicles

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Safety Helmet Detection Algorithm for Complex Scenarios Based on PConv-YOLOv8

Safety Helmet Detection Algorithm for Complex Scenarios Base...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Wang, Yuanyuan Jiang, Feilong Li, Yazhou Zhang, Haiyan Wang, Meifeng Yan, Shaofeng Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huaian China

ISBN: (纸本)9798350306293

Detecting safety helmets in complex environments is challenging due to issues like occlusion and lighting variations. Addressing the issues of slow detection speed and low object detection accuracy in complex environments with the YOLOv8 model, this paper introduces a lightweight safety helmet detection model, called PConv-YOLOv8, that is suitable for real-time applications in complex environments. Our method incorporates the PConv (Partial Convolution) module into the YOLOv8 model, reducing the complexity of the feature extraction network while enhancing feature representation accuracy. It also incorporates SimAM attention to extract and enhance the most relevant features by evaluating their similarity. Additionally, it considers category imbalance and positional regression in the target detection task, enhancing the model's performance in target category identification and positional localization. Moreover, we propose the Wise-Distribution Focal Loss function to improve bounding box selection accuracy and enhance the model's robustness. This paper introduces the Wise-Distribution Focal Loss method, which enhances the performance of target category recognition and location localization by improving the accuracy of bounding box selection and increasing the robustness of the overall model. The experimental results demonstrate that the method proposed in this paper achieves a 125% improvement in detection speed and a 1.8% increase in mAP0.5 compared to the YOLOv8 model. © 2023 IEEE.

关键词： Convolution

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A Surface Defect Detection Method Based on Attention Mechanism for Workpieces

A Surface Defect Detection Method Based on Attention Mechani...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Qiu, Junlin Shen, Yongshan Feng, Wanli Zhang, Haodong Sun, Keke Qin, Yuxin Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

With the rapid development of image processing technology in recent years, faced with the issues of low detection accuracy and missed detections in the process of surface defect detection on workpieces, we propose a workpiece surface defect detection method based on attention mechanism. The model uses EfficientDet-d0 as the baseline network, mixes the Fused-MBConv structure and MBConv structure in EfficientNetv2 network as part of the feature extraction network, and uses the convolutional attention module CBAM to focus on the information of space and channel direction at the same time, and the Hardmish activation function is used in the structure. Introducing a fast spatial pyramid pooling module (SPPF) at the top of the feature extraction network increases the network's depth and enhances its expressive power. The extracted features are fed into the Improved-BiFPN (Bidirectional Feature Pyramid Network) for feature enhancement, improving the model's ability to detect defects of different sizes. Experimental results demonstrate that our proposed surface defect detection method for workpieces outperforms other advanced detectors. © 2023 IEEE.

关键词： Feature extraction

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A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

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computers, Materials & Continua 2024年第8期80卷 1831-1850页

作者： Hina Gul Sana Ullah Ki-Il Kim Farman Ali Department of Computer and Software Technology University of SwatMingora19130Pakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Applied AI Sungkyunkwan UniversitySeoul03063Republic of Korea

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart *** and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of *** paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such *** proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual *** protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base *** performance of the proposed protocol is evaluated through the network simulator *** different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network *** 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

关键词： Internet of Things wireless sensor networks traffic load clustering routing energy efficiency

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