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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Zhu, Songsong Chen, Jiwei Xie, Ruitong Song, Yuhao Zhang, Mian Zhao, Mengxiong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798350354010

Accurate fault diagnosis of planetary gearboxes (PGs) is crucial for preventing equipment failures and extending their lifespan. To enhance the efficiency and accuracy of PG fault diagnosis, this paper proposes a novel method integrating intrinsic time scale decomposition (ITD) with convolutional neural network (CNN). The proposed method leverages the demodulation capability of ITD to decompose and reconstruct the original signal, effectively removing irrelevant noise and isolating core fault features, and the learning ability of CNN to accurately identify the fault features. Compared to methods relying on CNN only, the proposed method not only improves the computational efficiency but also achieves higher fault diagnosis accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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An Order Demodulation Analysis Method for Planetary Gearboxes

An Order Demodulation Analysis Method for Planetary Gearboxe...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Chen, Jiwei Xie, Ruitong Zhu, Songsong Zhao, Mengxiong Wang, Zhiyuan Zhang, Mian Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechanical System Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Binshuixidao 391 Xiqing District Tianjin300384 China State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan430072 China

ISBN: (纸本)9783031694820

In addressing challenges such as the speed change of planetary gearbox (PG) during operation and the complexity in extracting fault features, an order demodulation analysis method based on order tracking (OT) and variational mode decomposition (VMD) is proposed. Non-stationary time domain signals are transformed into relatively stable angular domain signals through angular resampling. The intrinsic mode function (IMF) is obtained by demodulation of the angular multi-component signal using the VMD method. The instantaneous order of a proper IMF is calculated via Hilbert Transform. The sensitive single component is selected by matching the meshing order and the central order of the instantaneous order. Finally, the amplitude of the characteristic order components extracted from the instantaneous order spectrum was used as features and Support Vector Machine (SVM) was employed for the classification of four different conditions in the sun gear. Comparing the results of the proposed method with Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), the experimental results demonstrate the effectiveness of the proposed method in diagnosing PGs under non-stationary conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Epicyclic gears

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Deep Reinforcement Learning with Human Guidance for Autonomous Driving Car Following

Deep Reinforcement Learning with Human Guidance for Autonomo...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Wenbo Wang Qingchao Xu Xingfu Yang Yandong Liu School of Software Engineering Dalian University Dalian China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

To solve the complex modeling and difficult parameter adjustment of traditional car-following control methods, we propose the car-following framework KgPPO-CF. The KgPPOCF brings expert experience into the interactive process of Deep Reinforcement Learning, which improves the speed and robustness of training policies. We use four different reward functions and compare the effects of policies trained by different designs on car-following control. In the Carla simulator, KgPPO-CF achieves car-following control in a continuous action space. The experimental results show that, compared with the traditional control methods, the control strategy obtained by KgPPO-CF training has better adaptability.

关键词： Training Accuracy Mechatronics Aerospace electronics Deep reinforcement learning Robustness Automobiles Robotics and automation Standards Autonomous vehicles

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Nonlinear Deep Subspace Network for Micro-expression Recognition 3

Nonlinear Deep Subspace Network for Micro-expression Recogni...

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3rd Workshop on Facial Micro-Expression: advanced Techniques for Multi-Modal Facial Expression Analysis, FME 2023

作者： Feng, Weijia Xu, Manlu Chen, Yuanxu Wang, Xiaofeng Guo, Jia Dai, Lei Wang, Nan Zuo, Xinyu Li, Xiaoxue College of Computer and Information Engineering Tianjin Normal University Tianjin China Ping An Technology Shenzhen China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400702853

Deep learning (DL) models have been widely studied in the field of micro-expression recognition (MER). However, micro-expressions (MEs) suffer from small number of samples and difficulty in extracting subtle and transient features, resulting in limited improvement in the recognition performance. In addition, DL models are prone to overfitting problems and are difficult to extract discriminative features of facial actions from ME images or sequences. To address these issues, we propose a MER method that combines a nonlinear deep subspace network and optical flow features. Firstly, facial motion features are captured using optical flow computation, and then the optical flow features are input into a kernel principal component analysis network (KPCANet) to further learn deeper spatio-temporal features. Finally, a linear support vector machine (SVM) is used for ME classification. Experiments conducted on four public spontaneous ME datasets including SMIC, CASME, CASME II and SAMM validate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method achieves even better recognition performance compared to existing state-of-the-art MER methods. © 2023 ACM.

关键词： Principal component analysis

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MFN: Explainable DNA triple helixes Stabilized design based on mCGR and flow network

MFN: Explainable DNA triple helixes Stabilized Design based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoru Wen Lijun Sun Lei Xie Yanfen Zheng Ben Cao Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

DNA triple helix structure, as a highly specific gene targeting tool, enable gene regulation by precisely identifying and binding to target DNA sequences. However, the limits of design quality and efficiency affect their wide application in gene therapy. Therefore, in this paper, we propose an antiparallel DNA triple helixes design method-MFN based on matrix chaotic game representation (mCGR) and flow network. Leveraging the structural characteristics of DNA sequences, this method employs the mCGR algorithm to construct an initial matrix, generating a set of DNA sequences that conform to foundational constraints. Then, these sequences are mapped as stream network nodes to screen crosstalk structures by path search, and the whole process is observable and interpretable. Experimental results show that MFN significantly improves the design efficiency of triple helix structure and reduces crosstalk phenomenon. Wet experiments further verify the effectiveness of the method. In summary, MFN achieves an efficient and high-quality design of DNA triple helixes and provides a new idea for targeted gene therapy.

关键词： Scalability design methodology DNA Crosstalk Genomics Games Reliability engineering Regulation Gene therapy Bioinformatics

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Multi-image encryption combining four-dimensional chaotic systems and multi-layer embedding

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The European Physical Journal Special Topics 2024年 1-18页

作者： Zhou, Kai Zhang, Jiaxin Xiang, Jianhong Zhong, Yu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Sichuan Key Laboratory of Agile Intelligent Computing Chengdu China

A multi-image encryption technique combining a four-dimensional chaotic system and multi-layer embedding is proposed because images are easily attacked from the outside during network transmission and that traditional image encryption algorithms have limitations such as long encryption time, low ciphertext image security, and poor information diffusion when a single image is encrypted multiple times. algorithm first creates a four-dimensional chaotic system to produce chaotic sequences; the system’s positive Lyapunov exponent indicates that it has chaotic properties in four directions; then, multiple images are processed using multi-layer embedding and chaotic sequences to create a composite image. Next, block scrambling and variable parameter Arnold’s scrambling are combined to perform block sorting and pixel disorder on the composite image. Lastly, the composite image is processed in separate blocks to execute various encryption techniques. To obtain the ciphertext image, several blocks are finally treated using either same-or-different-or diffusion processing. Simulation experiments show that this algorithm has a faster encryption time, better security and information diffusion in the encrypted image, a maximum correlation value of $$-$$ 0.00244 between adjacent pixels in the ciphertext image, and UACI of 33.4663% and 99.6086%, respectively (the theoretical values are 0, 99.61% and 33.46%)

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Rsta: A Reversible Signal Transmission Architecture for Breaking the Limitation of Unidirectional Transmission

SSRN

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SSRN 2025年

作者： Liu, Xin Cui, Shuang Zhang, Xun Zhou, Fuxing Sheng, Kaikai Wang, Bin Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian116622 China

Molecular circuits are essential tools for biosensing and molecular information processing. However, the long-distance and reversible transmission of orthogonal signals remains a significant challenge in molecular circuits, preventing molecular signals from achieving reversible transmission similar to that of electrical signals in wires. To overcome the limitations of unidirectional signal transmission and facilitate the integration of antagonistic circuits. We propose a reversible signal transmission architecture (RSTA) with wire-like characteristics to achieve reversible signal transmission based on fundamental molecular devices known as reversible signal fan-out devices (RSFDs). The polarity of the input signal dictates the transmission direction, establishing the initial condition for the output signals. Cascaded RSFDs within RSTA offer two key advantages. First, the temporal response to signals. Second, the reversible transformation of opposite circuit types without structural modification. We experimentally validated the principles and functionality of RSTA using temporal logic device and integrated molecular circuits. This work advances the field of reversible molecular signal transmission, enabling the rational design of complex molecular circuits and molecular information processing systems. We envision applications of this technology in nanomachines, molecular computing, and biosensing. © 2025, The Authors. All rights reserved.

关键词： Temporal logic

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Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

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Science China(Technological Sciences) 2023年第6期66卷 1815-1830页

作者： LIU Yue DING Lin YANG ZhengWei GE XianYuan LIU DaHui LIU Wei YU Tao AVDEEV Maxim SHI SiQi School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Institute of Functional Materials Central Iron&Steel Research InstituteBeijing 100081China Australian Nuclear Science and Technology Organisation Locked Bag 2001Kirrawee DC NSW 2232Australia School of Chemistry The University of SydneySydney 2006Australia Zhejiang Laboratory Hangzhou 311100China

Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity ***,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal ***,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic ***,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility *** this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in *** extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.

关键词： Nickel-based single crystal superalloys text mining named entity recognition credibility analysis domain knowledge

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Nonreciprocity of energy transfer in a nonlinear asymmetric oscillator system with various vibration states

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Applied Mathematics and Mechanics(English Edition) 2023年第5期44卷 727-744页

作者： Jian’en CHEN Jianling LI Minghui YAO Jun LIU Jianhua ZHANG Min SUN Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China School of Aeronautics and Astronautics Tiangong UniversityTianjin 300387China Agricultural Information Institute of Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big DataMinistry of Agriculture and Rural AffairsBeijing 100081China School of Science Tianjin Chengjian UniversityTianjin 300384China

The nonreciprocity of energy transfer is constructed in a nonlinear asymmetric oscillator system that comprises two nonlinear oscillators with different parameters placed between two identical linear *** slow-flow equation of the system is derived by the complexification-averaging *** semi-analytical solutions to this equation are obtained by the least squares method,which are compared with the numerical solutions obtained by the Runge-Kutta *** distribution of the average energy in the system is studied under periodic and chaotic vibration states,and the energy transfer along two opposite directions is *** effect of the excitation amplitude on the nonreciprocity of the system producing the periodic responses is analyzed,where a three-stage energy transfer phenomenon is *** the first stage,the energy transfer along the two opposite directions is approximately equal,whereas in the second stage,the asymmetric energy transfer is *** energy transfer is also asymmetric in the third stage,but the direction is reversed compared with the second ***,the excitation amplitude for exciting the bifurcation also shows an asymmetric *** vibrations are generated around the resonant frequency,irrespective of which linear oscillator is *** excitation threshold of these chaotic vibrations is dependent on the linear oscillator that is being *** addition,the difference between the energy transfer in the two opposite directions is used to further analyze the nonreciprocity in the *** results show that the nonreciprocity significantly depends on the excitation frequency and the excitation amplitude.

关键词： nonreciprocity strong nonlinearity energy transfer chaotic vibration higher branch of response

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Frequency-Domain Assisted Fundus Image Enhancement Network Based on the Mamba Model 9

Frequency-Domain Assisted Fundus Image Enhancement Network B...

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9th IEEE International Conference on Progress in Informatics and computing, PIC 2024

作者： Luo, Bin Wen, Yang Ji, Jianhua Cao, Wenming Li, Ping Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronic and Information Engineering Shenzhen China College of Electronic and Information Engineering Shenzhen University Shenzhen China The Hong Kong Polytechnic University Department of Computing and School of Design Hong Kong Hong Kong

ISBN: (纸本)9798350363203

Enhancing fundus images is crucial for early diagnosis and monitoring of retinal diseases. Although CNN and Transformer-based methods have made great progress, CNNs struggle with long-range dependencies, and Transformers face challenges with large model sizes and low computational efficiency. To address these issues, Selective Structured State-Space Models (SSMs) offer an efficient solution with linear complexity. However, standard Mamba models still suffer from local pixel information loss and channel redundancy, limiting their performance in fundus image enhancement. We propose MambaFE, which incorporates localized refinement and an advanced channel attention mechanism to resolve challenges such as pixel detail loss and excessive channel information. Considering that retinal structures play a vital role in fundus analysis, retaining these structures during the enhancement process is crucial. As retinal structures are predominantly located within the high-frequency components of fundus images, we introduce a frequency-guided approach to capture reliable representations of these features, ensuring improved retention of essential information during enhancement. Comprehensive experimental findings indicate that the proposed MambaFE outperforms contemporary fundus image enhancement techniques, significantly enhancing the quality of clinically low-quality and synthetically degraded fundus images, while offering robust support for real-world medical applications. © 2024 IEEE.

关键词： Pixels

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