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Remaining Useful Life Prediction of Bearing via a Double Attention-Based Deep Neural Network

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Remaining Useful Life Prediction of Bearing via a Double Att...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Yongkang Liu Donghui Pan Haifeng Zhang Kai Zhong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei Anhui China Institutes of Physical Science and Information Technology Anhui University Hefei Anhui China

Predicting the remaining useful life (RUL) of bearings is important for the secure running and reducing maintenance costs of rotating machinery. In this article, a novel feature extraction and RUL prediction framework of bearing is proposed by utilizing time-frequency representations (TFRs) and deep neural network with double attention (DA-DNN). Firstly, TFRs are employed to extract the degradation features associated with RUL prediction, which can effectively cope with the non-stationary properties of bearing vibration data. The reduced dimensional TFRs are then used as the DA-DNN model input. Afterwards, DA-DNN is developed by employing the double attention structure to extract the key degradation features more efficiently. The dual-attention structure has ability to capture the degradationrelated features from two aspects of channel attention and spatial attention, which enables the network to focus on high RUL-relevant information. Finally, the experimental results demonstrate the effectiveness of DA-DNN, which shows higher performance in terms of prediction accuracy compared to other deep learning (DL) methods.

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A Circularly Polarized Antenna for GPS L1 band with U-Shaped-Like Strips

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A Circularly Polarized Antenna for GPS L1 band with U-Shaped...

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Lulu Meng Zhixiang Huang Yingsong Li School of Electronic and Information Engineering Anhui University Hefei China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A U-shaped-like parasitic strip loading antenna is proposed and designed, which can work in L1 band of GPS with circular polarization (CP) characteristics. The antenna consists of ground plane, dielectric, radiation patch and U-shaped-like parasitic strips using a simple coaxial feed probe. The asymmetric U-shaped-like strips are to realize CP. The simulation and optimization results for the modeled directional CP antenna show that the axial ratio bandwidth is 10MHz and the impedance bandwidth is 30MHz, which can meet the requirements for GPS L1 band.

关键词： Polarization Loading Bandwidth Directive antennas Probes Loaded antennas Optimization

EventHallusion: Diagnosing Event Hallucinations in Video LLMs

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arXiv 2024年

作者： Zhang, Jiacheng Jiao, Yang Chen, Shaoxiang Zhao, Na Tan, Zhiyu Li, Hao Chen, Jingjing Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore University of Technology and Design Singapore Shanghai Academy of Artificial Intelligence for Science China Meituan China

Recently, Multimodal Large Language Models (MLLMs) have made significant progress in the video comprehension field. Despite remarkable content reasoning and instruction following capabilities they demonstrated, the hallucination problem of these VideoLLMs is less explored compared with its counterpart in the image domain. To mitigate this gap, we propose EventHallusion, a novel benchmark that focuses on assessing the VideoLLMs’ hallucination toward event, the crux of video analysis. From a hallucination attribution perspective, our EventHallusion benchmark is curated to assess a VideoLLM’s susceptibility toward language priors and vision-language biases. On the other hand, we also propose a simple yet effective method, called Temporal Contrastive Decoding (TCD), to tackle the hallucination problems of VideoLLMs. The proposed TCD method rectifies the model’s bias toward its priors during the decoding stage by comparing the original video with a modified version, in which temporal cues are ambiguited. Through comprehensive evaluation of eight open-source and three closed-source VideoLLMs on the proposed EventHallusion benchmark, we observe that the open-source models suffer significantly from hallucination problems, whereas the closed-source ones perform markedly better. By further equipping open-source VideoLLMs with the proposed TCD approach, evident performance improvements are achieved across most metrics in the EventHallusion benchmark. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

Machine learning prediction of network dynamics with privacy protection

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arXiv 2022年

作者： Xia, Xin Su, Yansen Lü, Linyuan Zhang, Xingyi Lai, Ying-Cheng Zhang, Hai-Feng The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion. Often, each client only has the time series data from a partial set of nodes and the client has access to only partial timestamps of the whole time series data and partial structure of the network. Due to privacy concerns or license related issues, the data collected by different clients cannot be shared. To accurately predict the network dynamics while protecting the privacy of different parties is a critical problem in the modern time. Here, we propose a solution based on federated graph neural networks (FGNNs) that enables the training of a global dynamic model for all parties without data sharing. We validate the working of our FGNN framework through two types of simulations to predict a variety of network dynamics (four discrete and three continuous dynamics). As a significant real-world application, we demonstrate successful prediction of State-wise influenza spreading in the USA. Our FGNN scheme represents a general framework to predict diverse network dynamics through collaborative fusing of the data from different parties without disclosing their privacy. © 2022, CC BY.

关键词： Forecasting

A Semantic Link Network Model for Supporting Traceability of Logistics on Blockchain

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arXiv 2025年

作者： Sun, Xiaoping Zhuge, Sirui Zhuge, Hai Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China King’s College London United Kingdom Publicis Sapient London United Kingdom Great Bay University Dongguan China Great Bay Institute for Advanced Study Dongguan China

The ability of tracing states of logistic transportations requires an efficient storage and retrieval of the state of logistic transportations and locations of logistic objects. However, the restriction of sharing states and locations of logistic objects across organizations from different countries makes it hard to deploy a centralized database for implementing the traceability in a cross-border logistic system. This paper proposes a semantic data model on Blockchain to represent a logistic process based on the Semantic Link Network model where each semantic link represents a logistic transportation of a logistic object between two parties. A state representation model is designed to represent the states of a logistic transportation with semantic links. It enables the locations of logistic objects to be derived from the link states. A mapping from the semantic links to the blockchain transactions is designed to enable schema of semantic links and states of semantic links to be published in blockchain transactions. To improve the efficiency of tracing a path of semantic links on blockchain platform, an algorithm is designed to build shortcuts along the path of semantic links to enable a query on the path of a logistic object to reach the target in logarithmic steps on the blockchain platform. A reward-penalty policy is designed to allow participants to confirm the state of links on blockchain. Analysis and simulation demonstrate the flexibility, effectiveness and the efficiency of Semantic Link Network on immutable blockchain for implementing logistic traceability. © 2025, CC BY.

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OmniViD: A Generative Framework for Universal Video Understanding

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arXiv 2024年

作者： Wang, Junke Chen, Dongdong Luo, Chong He, Bo Yuan, Lu Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Microsoft Cloud + AI Microsoft Research Asia China University of Maryland College Park United States

The core of video understanding tasks, such as recognition, captioning, and tracking, is to automatically detect objects or actions in a video and analyze their temporal evolution. Despite sharing a common goal, different tasks often rely on distinct model architectures and annotation formats. In contrast, natural language processing benefits from a unified output space, i.e., text sequences, which simplifies the training of powerful foundational language models, such as GPT-3, with extensive training corpora. Inspired by this, we seek to unify the output space of video understanding tasks by using languages as labels and additionally introducing time and box tokens. In this way, a variety of video tasks could be formulated as video-grounded token generation. This enables us to address various types of video tasks, including classification (such as action recognition), captioning (covering clip captioning, video question answering, and dense video captioning), and localization tasks (such as visual object tracking) within a fully shared encoder-decoder architecture, following a generative framework. Through comprehensive experiments, we demonstrate such a simple and straightforward idea is quite effective and can achieve state-of-the-art or competitive results on seven video benchmarks, providing a novel perspective for more universal video understanding. Code is available at https://***/wangjk666/OmniVid. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

A Da-Shaped Slotted Circularly-Polarization Antenna for 5G Applications

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A Da-Shaped Slotted Circularly-Polarization Antenna for 5G A...

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Lulu Meng Zhixiang Huang Yingsong Li College of Electronic and Information Engineering Anhui University Hefei China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

This paper proposes a Da-shaped slotted antenna, where the Chinese culture is integrated into the antenna design to achieve an artistic antenna that can be used in 5G communication system. The designed antenna has circular polarization characteristic, which is composed of ground plane, dielectric layer and metal sheet A engraved with Da-shaped slot, and a U-shaped wall (metal sheet B). A simple L-shaped feed probe is used to improve the working bandwidth of the antenna. A U-shaped metal sheet B is considered to improve antenna gain and axial ratio bandwidth. The simulation and optimization results show that the axial ratio bandwidth is 2.30-2.71GHz, and the impedance bandwidth is 2.44-2.7GHz, both of which can cover operating frequency in the range of 2.515-2.675GHz of 5G.

关键词： Polarization Slot antennas 5G mobile communication Metals Bandwidth Directive antennas Antenna feeds

Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

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Understanding the Robustness of 3D Object Detection with Bir...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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Pyramid Convolution and Multi-Frequency Spatial Attention for Fine-Grained Visual Categorization

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SSRN

SSRN 2022年

作者： Xu, Qin Li, Yun Zhang, Mengquan Tao, Zhifu Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China School of Big Data and Statistics Anhui University Hefei230601 China

Learning diverse detailed feature is crucial for fine-grained visual categorization (FGVC). However, most of existing methods for FGVC use the standard convolution for feature extraction which leads to the loss of many subtle but important features. Besides, in the existing attention models for FGVC, the features are aggregated by a simple global average pooling operation, which is unable to characterize complex feature information. To address these problems, we propose pyramid convolution and multi-frequency spatial attention (PCMFSA) for FGVC. To capture spatial context dependencies of different levels and avoid the loss of the subtle features, pyramid convolution, which uses convolution kernels with various sizes, is introduced in the backbone network. To reduce the influence of background noise and obtain diverse fine-grained feature information, we propose multi-frequency spatial attention (MFSA) module, in which two channel-wise grouping operations are designed and discrete cosine transform (DCT) in the frequency domain is adopted to enhance features of different spatial positions. Moreover, to mine more valuable regions, peak suppression is used to suppress high-response regions after MFSA. A large number of experimental results show that our method achieves state-of-the-art results on four popularly FGVC datasets. © 2022, The Authors. All rights reserved.

关键词： Convolution