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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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Efficient Online Edge Learning for UAV Object Detection via Adaptive Batch Size Fitting 9

Efficient Online Edge Learning for UAV Object Detection via ...

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9th international conference on Big data computing and Communications, BigCom 2023

作者： Wu, Feiyu Qu, Yuben Wang, Weijun Dong, Chao Jing, Yuqian Zhang, Lei Wu, Qihui Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology China Nanjing University China

ISBN: (纸本)9798350331240

Benefiting from flexibility and mobility, Unmanned Aerial Vehicle (UAV) plays an important role in object detection. However, it is challenging to maintain high accurate inference results in an inherent dynamic scene. Because data drift caused by changeable weather, motion blur, etc leads to inference deviation from the pre-training model. Online learning is an effective method for data drift by supplementing actual environmental data. However, recent researches about online learning are unsuitable for resource-constrained and computationally complex UAV object detection, because of excessive training time for online learning. To improve recognition accuracy and shorten training time, this paper designs an efficient online edge learning for UAV object detection via adaptive batch size fitting. It comprehensively considers online samples and the model's real-time performance to fit the batch size adaptively. Our method efficiently improves the convergence speed of model training and can be easily implemented on the embedded devices of UAVs. Experimental results show that compared with the existing batch size fitting methods, our method reduces the total training time by 30% and has strong robustness in different scenes. © 2023 ieee.

关键词： Object detection

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A Structural Expansion Method of Fuzzy cognitive Map under cognitive Limitations

A Structural Expansion Method of Fuzzy Cognitive Map under C...

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2022 international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2022

作者： Han, Huijian Li, Zongwei Li, Lei Zhang, Rui School of Computer Science and Technology Shandong University of Finance and Economics Jinan China

ISBN: (数字)9781665498586

ISBN: (纸本)9781665498586

With the diversification of social development, the difficulty and complexity of prediction problems have increased significantly. However, most prediction models are not interpretative, can only deal with limited and relatively simple problems, and lack solutions to complex problems. As a data-driven discrete nonlinear system, Fuzzy cognitive Map combines the advantages of neural network and fuzzy logic, has strong knowledge representation and logical reasoning ability. However, the nodes selected in complex systems cannot be comprehensive and practical due to the limitations of human cognition, and the lack of essential nodes will affect the accuracy of the prediction model. This paper will focus on the structure of FCM and analyze the FCM prediction model under cognitive limitations: The results show that cognitive limitations reduce the prediction accuracy;We propose a complete cognitive structure fuzzy cognitive map method (CC-FCM) to improve the prediction accuracy and solve the modeling and prediction problems of complex systems. © 2022 ieee.

关键词： Fuzzy cognitive Maps

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Online Multi-Object Tracking Based On Global Feature Enhancement Network And Historical Feature Complement Iteration

Online Multi-Object Tracking Based On Global Feature Enhance...

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cognitive computing and complex data (ICCD), international conference on the

作者： Zunwang Ke Puping An Gang Wang Yugui Zhang School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Multi-object tracking is an important part of computer vision and plays a crucial role in many real-world application scenarios. The existing integrated tracking framework loses the key features of the target when fusing features and iteratively updating the trajectory apparent features, resulting in tracking instability. In order to solve the above problems, this paper proposes an online multi-object tracking method based on global feature enhancement network and historical feature complement iteration. In this method, a dual global feature fusion unit is used to replace the global feature enhancement network of summation or splicing operation, which is used to extract more discriminative key features and enhance the recognition ability of objects in multiple scenes. In addition, the historical features are used to supplement the iterative mechanism, which introduces random features and average features from the historical appearance features stored in the trajectory as supplementary features to supplement the lack of object features in the current frame. Comprehensive experiments are carried out on MOT15, MOT16, MOT17, and MOT20 datasets, in which the MOTA score of 73.2% is achieved on MOT16 dataset, indicating that the proposed method has good tracking performance. Source code: http://***/Anbping/gfen-hfci

关键词： Computer vision Target tracking Splicing Source coding Computational modeling Feature extraction Trajectory Iterative methods Object tracking cognitive systems

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LSTM deep learning model for Alzheimer's disease prediction based on cost-effective time series cognitive scores 5

LSTM deep learning model for Alzheimer's disease prediction ...

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5th international ieee Novel Intelligent and Leading Emerging Sciences conference, NILES 2023

作者： Saleh, Hager El-Rashidy, Nora Abuhmed, Tamer El-Sappagh, Shaker South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt Kafrelsheiksh University Faculty of Artificial Intelligence Machine Learning and Information Retrieval Department Egypt Sungkyunkwan University College of Computing and Informatics Korea Republic of Galala University Faculty of Computer Science and Engineering Suez435611 Egypt

ISBN: (纸本)9798350381030

Alzheimer's disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer's disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance. © 2023 ieee.

关键词： data fusion

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Deep Learning based Parts Classification in a cognitive Robotic Cell System 26

Deep Learning based Parts Classification in a Cognitive Robo...

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26th international conference on System Theory, Control and computing (ICSTCC)

作者： Rosioru, Sabin Stamatescu, Grigore Stamatescu, Julia Fagarasan, Ioana Popescu, Dan Asti Automat Bucharest Romania Univ Politehn Bucuresti Automat & Ind Informat Bucharest Romania

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

Advanced manufacturing systems increasingly rely on intelligent algorithms to discriminate, model and predict system behaviours that lead to increased productivity. Edge intelligence allows the industrial systems to collect, compute and act based on process data while reducing the latency and cost associated to an hierarchical control system in which complex decisions are generated in the upper layers of the automation hierarchy. Greater local computing capabilities allow the online operation of such algorithms while accounting for increased performance requirements and lower sampling periods of the control loops. In this work we present the concept of a cognitive robotic cell that collects, stores and processes data in situ for enabling the control of a robotic arm in a production setting. The main features that characterise the robotic cell are embedded computing, open interfaces, and standards-based industrial communication with hardware peripherals and digital twin models for validation. An application of part classification is presented that uses the YOLOv4 image processing algorithm for real-time and online assessment that guides the control of an ABB IRB120C robotic arm. Results illustrate the feasibility and robustness of the approach in a real application. Quantitative evaluation underlines the performance of the implemented system.

关键词： robot control deep learning part classification YOLOv4 image processing

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Research on Motion Posture Based on Convolutional Neural Network Algorithm

Research on Motion Posture Based on Convolutional Neural Net...

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cognitive computing and complex data (ICCD), international conference on the

作者： Yunpeng Tang Yu Zou Physical Education Teaching Department Guizhou Business School Guiyang China Guizhou Kangsitian Science and Technology Development Co. Ltd Guiyang China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Recognition of motion postures has been a highly popular research field, aiming to analyze and identify human movements and behavior patterns through observations and description using natural language and other methods. This study introduces a posture motion feature extraction algorithm that incorporates spatiotemporal weighting and utilizes a deep convolutional neural network (CNN) approach. The algorithm extracts spatiotemporal motion key frames from selected motion samples and presents them as static images. It then performs initial motion image preprocessing, such as motion target detection and image enhancement, and vectorizes motion features using CNN. The spatiotemporal weighted adaptive interpolation method is employed to minimize errors in motion edge detection, enabling the extraction of posture motion features by combining posture edge features and spatiotemporal features. The extraction results are presented as the output. Comparative experiments with traditional algorithms demonstrate that the proposed neural network intelligent recognition algorithm not only achieves intelligent recognition of motion postures but also improves overall accuracy and reduces computational load compared to some other algorithms.

关键词： Accuracy Image edge detection Scalability Neural networks Training data Feature extraction Prediction algorithms Spatiotemporal phenomena Convolutional neural networks Resource management

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An Improved NGO Algorithm for Optimizing VMD-CNN-BILSTM-Cross Attention Bearing Fault Diagnosis Method

An Improved NGO Algorithm for Optimizing VMD-CNN-BILSTM-Cros...

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cognitive computing and complex data (ICCD), international conference on the

作者： Jiapeng Luo Xiaobing Chen Faculty of Computer and Software Engineering Huaiyin Insititute of Technology Huai’an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Precisely identifying the fault-related operating state of the gear-box bearing represents a vital problem within industrial production. A bearing fault diagnosis method based on effectively improved NGO algorithm optimized VMD and CNN-BILSTM-Cross Attention is proposed for bearing fault diagnosis. The method improves the NGO algorithm by introducing the SMP chaotic mapping to initialize the eagle swarm distribution, introducing the sine function and the inverse learning strategy to enhance the algorithm’s computational precision and search ability. The CNN-BILSTM model is integrated with the Cross Attention module, which aims to merge the bearing signal features in multiple ways and boost the model’s accuracy and robustness. The improved SCNGO algorithm is utilized to iteratively optimize crucial parameters, namely the intrinsic mode coefficient, and the quadratic penalty term [K, α]within the VMD denoising technology. The CNN is responsible for seizing the spatial features of the bearing vibration signal at diverse frequencies, while the BILSTM is in charge of capturing the temporal features of the same signal. Moreover, the Cross Attention mechanism is introduced to blend the spatial features extracted by the CNN and the temporal features obtained by the BILSTM. In comparison to other models, the proposed model demonstrates stronger generalization and robustness.

关键词： Fault diagnosis Vibrations Accuracy Chaotic mapping Computational modeling Switched mode power supplies Feature extraction data models Robustness Optimization

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Remote Sensing Object Detection Based On Adaptive Spatial Attention

Remote Sensing Object Detection Based On Adaptive Spatial At...

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cognitive computing and complex data (ICCD), international conference on the

作者： Xuanchen Liu Yuqi Yao Gang Wang Zhiyu Wu Tingting Jia School of Software Xinjiang University Urumqi China Department of Stomatology The First Medical Centre Chinese PLA General Hospital Beijing China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Object detecting in remote sensing images field is utilized for analyze images of the earth's surface to monitor and understand environmental changes, and to support scientific decision-making and resource management. The existing methods do not fully integrate the context information of the scene, which brings about the decline of detection capability, missed detection and false detection. To tackle this challenge, this paper proposes adopts a remote sensing image object detection method with adaptive spatial attention. This method uses multi-scale convolution kernels to extract feature maps of different scales, thus strengthening the model's feature extraction prowess. The spatial attention is utilized to adaptively adjust the receptive field and leverage contextual information, thereby enhancing the recognition capability for small objects. Experiments on the DOTAv1.0, HRSC2016 and VEDAI remote sensing image datasets demonstrate that the proposed method enhances detection accuracy and effectively mitigates issues of missed and false detections. The source code will be available at https://***/yyq0828/ASA.

关键词： Adaptation models Accuracy Computational modeling Source coding Object detection Feature extraction data models Stability analysis Remote sensing Context modeling

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Heuristic Structures of cognitive Aerospace Remote Sensing Radars 3

Heuristic Structures of Cognitive Aerospace Remote Sensing R...

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3rd ieee international conference on System Analysis and Intelligent computing, SAIC 2022

作者： Volosyuk, Valeriy Zhyla, Simeon Pavlikov, Vladimir Vlasenko, Dmytro Inkarbaieva, Olha Kolesnikov, Denys National Aerospace University 'Kharkiv Aviation Institute' Aerospace Radio-Electronic Systems Department Kharkiv Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

cognitive radars are the most complex in structure and the most flexible radio engineering systems in terms of tasks to be solved. Much attention is paid to their construction and improvement because of their high potential. In this paper, it is proposed to apply the technology of cognitive dynamic systems to airborne synthetic aperture radars (SAR). An approach to optimizing the structure of onboard cognitive radars using the theory of nonlinear filtering is proposed. Heuristically, summarizing the experience of the authors in the synthesis of optimal algorithms for the functioning of onboard multichannel SAR and the existing results of optimization of tracking systems, several structures of onboard cognitive remote sensing radars are proposed. © 2022 ieee.

关键词： Synthetic aperture radar

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Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification 11

Fine Tuning Swin Transformer Based Pretrained Model for Micr...

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11th international conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Anggara, Muchamad Galih Hindasyah, Ahmad Amda, Julfa Muhammad Waskita, A.A. Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Computing National Research and Innovation Agency Cibinong Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

Accurate image classification of microscopic pathogenic fungi is a challenge in the field of mycology. This study proposes a Swin Transformer-based approach to automate the identification process. The Swin Transformer model, known for its ability to capture complex spatial interactions, is adapted from ImageNet’s pre-trained model. The adaptation process is focused on fine-tuning the final classification layer to match the 5 classes of fungi present in the dataset. This classification layer is replaced by a new fully-connected layer that has the number of output according to the number of fungal classes. The weights on these layers are randomly initialized and trained in conjunction with several final convolutional layers. In addition, regularization techniques such as dropouts are applied to prevent overfitting. Adaptation using 5-fold cross-validation was carried out to find the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−3, StepLR scheduler, batch size of 128 images, and a total of 9 epochs / iterations. After that, fine-tuning is carried out on the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−5, StepLR scheduler, batch size of 32 images, and a total of 100 epochs / iterations. The final result showed excellent performance, with accuracy, precision, recall and an average F1-score of 95%. These results show the potential of the Swin Transformer in becoming an effective tool for mycologists. © 2024 ieee.

关键词： Deep learning

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