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15th Space Computing Conference

作者： Lakey, Daniel Schlippe, Tim IU Int Univ Appl Sci Berlin Germany

ISBN: (纸本)9798350384543;9798350384536

Anomaly detection in spacecraft telemetry is critical for the success and safety of space missions. Traditional methods often rely on forecasting and threshold techniques to identify anomalies [1]-[5]. This paper presents a comprehensive comparison of traditional forecast-based anomaly detection against two innovative classification methods, including a direct classification and an image classification through Gramian Angular Field (GAF) transforms [6], which have only been analysed in other domains but not for spacecraft anomaly detection. All our investigated systems leverage deep learning architectures and use the popular real SMAP/MSL spacecraft data from [2]. Our findings suggest that direct classification provides a marginal but statistically significant improvement in anomaly detection over traditional methods. However, image classification, while less successful, offers promising directions for future research. The study aims to guide the selection of appropriate anomaly detection techniques for spacecraft telemetry and contribute to the advancement of automated monitoring systems in space missions.

关键词： anomaly detection time series classification image classification

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Road type classification using deep learning for Tire-Pavement interaction noise data in autonomous driving vehicle

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APPLIED ACOUSTICS 2023年第1期212卷

作者： Lee, Sang -Kwon Yoo, Jinhwan Lee, Chang-Hun An, Kanghyun Yoon, Young -Sam Lee, Jaehun Yeom, Gi-Ho Hwang, Seoung-Uk Inha Univ Acoust & Vibrat Signal Proc Lab Incheon South Korea Hyundai Motor Co Seoul South Korea NEXEN Tire Corp Yangsan South Korea

This study presents a novel artificial intelligence model (AIM) for the real-time classification of 13 different road types in an autonomous vehicle. The model was developed based on a combination of a continuous wavelet transform (CWT) and convolutional neural network (CNN). Previously, three methods have been used for the classification of road types, depending on the type of sensor. First, a camera sensor has been widely used because it can capture the road type directly. Second, a vibration sensor has been used, since the vibration level measured on the suspension or inside the tire depends on the road type. Finally, an acoustic sensor has been used, especially in measuring tire-pavement interaction noise (TPIN). In a previous feasibility study, an AIM was developed to classify road types using TPIN signals, which vary depending on the road type. It can distinguish between two road types: asphalt and snow. Recently, CNN has been widely used as an AIM for classification, but it is limited as the input size of the CNN should be optimized for real-time processing due to its long calculation times, even for a 2D convolution process. Its input is image data, which can be produced through the CWT of the TPIN signal. This study proposes an AIM that can classify 13 different road surfaces in real-time while driving. In this study, a method to determine the optimal filter band and data length used for CWT is proposed. The method was developed based on the classification accuracy of an AIM. The developed AIM was successfully applied to the real-time classification of road types with an accuracy of 95% on a public road.

关键词： Continuous wavelet transform Convolution neural network Tire -pavement interaction noise Classification Road surfaces

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Research on online measurement method of fineness modulus of manufactured sand based on deep learning

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CONSTRUCTION AND BUILDING MATERIALS 2024年 414卷

作者： Huang, Feizhi Yang, Jianhong Fang, Huaiying Pan, Weijun Wei, Yixing Huaqiao Univ Coll Mech Engn & Automat Xiamen 361021 Fujian Peoples R China

The fineness modulus(FM) represents the level of particle size of manufactured sand. real-time feedback of FM of manufactured sand is important for industrial sand production, but extracting the particle profile from densely stacked images is a great challenge. In this study, a deep learning and regression analysis -based online measurement method for FM of manufactured sand is proposed. Firstly, the real fineness modulus of the sand produced by the sand -making machine in real time was obtained by the vibration -screening method(VSM). Then, the particle size fraction of larger particles (0.6-4.75 mm) was obtained based on machine vision combined with a convolutional neural network and image processing. Secondly, a multiple linear regression model was developed for the percentage of particle size and FM. Finally, the percentage of particle size was substituted into the regression model as the independent variable to achieve a fast prediction of the unknown FM. The experimental results show that the maximum repeatability errors for FM of different manufactured sands are 0.09 and 0.13 respectively, and the maximum absolute errors of the FM prediction results are 0.18 and 0.17 respectively. The calculation efficiency and error level of this research method can meet the online testing at sand making sites.

关键词： Manufactured sand Fineness modulus deep learning Online measurement image processing

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Spatial-channel transformer network based on mask-RCNN for efficient mushroom instance segmentation

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International Journal of Agricultural and Biological Engineering 2024年第4期17卷 227-235页

作者： Jiaoling Wang Weidong Song Wengang Zheng Qingchun Feng Mingfei Wang Chunjiang Zhao Northwest Agriculture and Forestry University Xi’an 712199China Nanjing Institute of Agricultural Mechanization Ministry of Agriculture and Rural AffairsNanjing 210014China Intelligent equipment Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Zhejiang Provincial Key Laboratory of Agricultural Intelligent Equipment and Roboics/College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China

Edible mushrooms are rich in nutrients;however,harvesting mainly relies on manual *** localization of each mushroom is necessary to enable a robotic arm to accurately pick edible *** studies used detection algorithms that did not consider mushroom pixel-level *** these algorithms are combined with a depth map,the information is ***,in instance segmentation algorithms,convolutional neural network(CNN)-based methods are lightweight,and the extracted features are not *** guarantee real-time location detection and improve the accuracy of mushroom segmentation,this study proposed a new spatial-channel transformer network model based on Mask-CNN(SCT-Mask-RCNN).The fusion of Mask-RCNN with the self-attention mechanism extracts the global correlation outcomes of image features from the channel and spatial ***,Mask-RCNN was used to maintain a lightweight structure and extract local features using a spatial pooling pyramidal structure to achieve multiscale local feature fusion and improve detection *** results showed that the SCT-Mask-RCNN method achieved a segmentation accuracy of 0.750 on segm_Precision_mAP and detection accuracy of 0.638 on Bbox_Precision_*** to existing methods,the proposed method improved the accuracy of the evaluation metrics Bbox_Precision_mAP and segm_Precision_mAP by over 2%and 5%,respectively.

关键词： edible mushrooms picking instance segmentation deep learning algorithm

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Efficient Temporally-Aware deepFake Detection using H.264 Motion Vectors

Efficient Temporally-Aware DeepFake Detection using H.264 Mo...

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IS and T International Symposium on Electronic Imaging 2024: Media Watermarking, Security, and Forensics, MWSF 2024

作者： Grönquist, Peter Ren, Yufan He, Qingyi Verardo, Alessio Süsstrunk, Sabine Image and Visual Represenation Lab École Polytechnique Fédérale de Lausanne Lausanne Switzerland

Video deepFakes are fake media created with deep learning (DL) that manipulate a person's expression or identity. Most current deepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements between frames. Some newer methods employ optical flow models to capture this temporal aspect, but they are computationally expensive. In contrast, we propose using the related but often ignored Motion Vectors (MVs) and Information Masks (IMs) from the H.264 video codec, to detect temporal inconsistencies in deepFakes. Our experiments show that this approach is effective and has minimal computational costs, compared with per-frame RGB-only methods. This could lead to new, real-time temporally-aware deepFake detection methods for video calls and streaming. © 2024 Society for Imaging Science and Technology. All rights reserved.

关键词： deep learning

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An Efficient Information-Reinforced Lidar deep Completion Network without RGB Guided

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REMOTE SENSING 2022年第19期14卷 4689-4689页

作者： Wei, Ming Zhu, Ming Zhang, Yaoyuan Sun, Jiaqi Wang, Jiarong Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Due to the sparsity of point clouds obtained by LIDAR, the depth information is usually not complete and dense. The depth completion task is to recover dense depth information from sparse depth information. However, most of the current deep completion networks use RGB images as guidance, which are more like a processing method of information fusion. They are not valid when there is only sparse depth data and no other color information. Therefore, this paper proposes an information-reinforced completion network for a single sparse depth input. We use a multi-resolution dense progressive fusion structure to maximize the multi-scale information and optimize the global situation by point folding. At the same time, we re-aggregate the confidence and impose another depth constraint on the pixel depth to make the depth estimation closer to the ground trues. Our experimental results on KITTI and NYU Depth v2 datasets show that the proposed network achieves better results than other unguided deep completion methods. And it is excellent in both accuracy and real-time performance.

关键词： depth completion lidar data processing image processing deep learning

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LIGHTWEIGHT DUAL-DOMAIN NETWORK FOR real-time MEDICAL image SEGMENTATION 29

LIGHTWEIGHT DUAL-DOMAIN NETWORK FOR REAL-TIME MEDICAL IMAGE ...

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IEEE International Conference on image processing (ICIP)

作者： Liu, Libo Fan, Xinxin Zhang, Xiaodong Hu, Qingmao Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

With the development of deep learning, deep convolution neural networks for medical image segmentation tasks have become more and more complex in pursuit of higher accuracy. In most scenarios, medical image segmentation pursues accuracy rather than speed, However, real-time performance is crucial in some scenarios, such as surgical navigation and diagnosis of acute stroke. So design of high-precision, lightweight and real-time medical image segmentation network has become an urgent need. To this end, a novel lightweight dual-domain network (LDD-Net) has been proposed in this paper. LDD-Net is comprised of two branches, learning respectively from the frequency domain and the spatial domain. In the frequency domain branch, the image spatial resolution is compressed via discrete cosine transform to have a large receptive field, so that better semantic context features can be learned. In the spatial domain branch, high-resolution feature representations with more details are learned. Finally, the learned features of these two branches are fused to yield high accuracy with low computational cost. The proposed method has been validated on two medical image segmentation datasets to yield the state-of-the-art performances with greatly reduced inference time and parameters of the learned models.

关键词： Medical image segmentation deep learning lightweight convolution neural networks lightweight dual-domain network

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Automatic Fish Species Identification Using Convolutional Neural Networks (CNN) 4

Automatic Fish Species Identification Using Convolutional Ne...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mary, I. Mettildha Ragunthar, T. Priyadharsini, M. Shyam Krishnaa, H. Sujit, M.K. Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Vellore Institute Technology Department of Information Security Tamil Nadu Vellore India Siemens Healthineers Bangalore India Atheniac Bangalore India

ISBN: (纸本)9798331540364

Accurate identification of fish species is critical for applications in fisheries management., biodiversity monitoring., and conservation efforts. Conventional manual identification techniques take a lot of time and are prone to mistakes., particularly when working with big datasets and species that have similar physical characteristics. Automating tasks related to species identification has shown promise in recent breakthroughs in machine learning., especially in deep learning techniques like Convolutional Neural Networks (CNNs) and transfer learning. However., these systems face several challenges., including high variability in fish appearances., limited availability of labelled datasets., and the computational overhead required for real-time classification. This research study presents a fish species classification system using a transfer learning approach based on MobileNetV2. MobileNetV2., known for its efficiency and lightweight architecture., is fine-tuned on a custom fish dataset to enhance species identification accuracy while minimizing computational requirements. To increase the model's resilience and ability to generalise to new species., data augmentation approaches are used. The system is designed to deliver high classification accuracy while maintaining computational efficiency., making it suitable for deployment in real-world applications with limited resources. Experimental results demonstrate that the proposed system achieves significant improvements in accuracy compared to traditional methods., while also addressing the challenges of dataset variability and overfitting. © 2024 IEEE.

关键词： convolutional neural networks deep learning Fish species prediction image classification MobileNetV2 transfer learning

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Vehicle Detection, Counting and Classification Using YOLOv8 deep learning Model and OpenCV for Urban Traffic Management System 9

Vehicle Detection, Counting and Classification Using YOLOv8 ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Manoj, N. Mohana Manu, A.S. Mallikarjun, N.Y. Ramakanth Kumar, P. Sreelakshmi, K. Rv College of Engineering® Bengaluru India Rv College of Engineering® Bengaluru India Rv College of Engineering® Bengaluru India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

In this paper YOLOv8 deep learning model is proposed for vehicle detection, classification, and counting for urban traffic surveillance applications on custom dataset. The model was trained with images and annotations done using the CVAT annotation tool, and it implemented in real-time using a mobile phone camera as a webcam via USB. The model accurately detected and classified four categories of vehicles such as two wheelers, three wheelers, four wheelers and large vehicles. A unique tracking ID approach was implemented to count the vehicles accurately. The obtained results prove the potential of custom model for vehicle detection and classification. The proposed model detects two wheelers, three wheelers, four wheelers and large vehicles with an accuracy of 93.6%, 86.4%, 88% and 89% respectively. © 2024 IEEE.

关键词： image annotation

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deep Q-Network for Cognitive Radar Anti-Jamming Strategy in Complex Electromagnetic Environment 12th

Deep Q-Network for Cognitive Radar Anti-Jamming Strategy in ...

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12th China Conference on Command and Control

作者： Liu, Chunhui Yuan, Yue Mao, Shuo Wang, Qiancheng Wang, Ben Nanjing Marine Radar Inst Nanjing Peoples R China Natl Key Lab Elect Effect & Secur Marine Equipmen Nanjing Peoples R China

ISBN: (纸本)9789819777723;9789819777709;9789819777693

The contemporary landscape of sophisticated and intelligent jamming techniques presents significant challenges for traditional radar anti-jamming methods, necessitating advancements in radar operational capabilities. To address the intricate and dynamic nature of jamming scenarios, alongside the limitations in performance assurance of manually crafted anti-jamming strategies and the suboptimal real-time responsiveness of radar systems, this study introduces an intelligent decision-making model founded on deep reinforcement learning (DRL). This model is meticulously structured, comprising a defined action space, state space, and reward function. Concurrently, the paper advocates a novel radar anti-jamming strategy learning approach based on the deep Q-Network (DQN), adept at mitigating external malicious interference. This approach enhances the integration efficiency and the doppler frequency resolution in radar echo processing. Comparative simulation outcomes affirm the superiority of the proposed intelligent decision model and training methodology over established methods like Proximal Policy Optimization (PPO) and Q-learning. Notably, the model demonstrates enhanced jamming suppression, robust generalization capabilities, accelerated response times, and a significant augmentation in the radar's autonomous decision-making prowess.

关键词： DQN Complex Electromagnetic Environment Cognitive Radar Anti-Jamming Decision

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