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The polishing surface quality of single crystal diamond with GO green-enhanced diamond hybrid slurry and material removal mechanism analysis

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Wear 2025年 574-575卷

作者： Wang, Zixuan Hou, Xueyan Zhao, Ji Yao, Jie Yu, Tianbiao Qu, Sheng Zhao, Jun School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China Liaoning Provincial Key Laboratory of High-end Equipment Intelligent Design and Manufacturing Technology Shenyang110819 China School of Physics Liaoning University Shenyang110036 China School of Mechanical Engineering Shenyang University of Technology Shenyang110870 China School of Advanced Manufacturing Sun Yat-sen University Shenzhen518107 China

Single crystal diamonds, which have extremely high hardness and brittleness, can provide lower power consumption and higher frequencies for electronic devices compared with other semiconductor materials. The chemical mechanical polishing method is usually used to realize the flattening of single crystal diamond workpiece. However, the conventional oxidants may cause damage to polishing tools and experimental equipment, and even lead to environmental pollution. Therefore, a graphene oxide green-enhanced diamond hybrid slurry was introduced in this study for single crystal diamond polishing with quartz glass as a polishing disk. The effect of graphene oxide on polishing surface quality was analyzed, and the polishing experiments were carried out with different polishing pressures and durations to investigate the influence of varying process parameters. The molecular dynamics method was used to simulate the formation and removal process of graphene oxide-induced degraded layer on single crystal diamond surface. The scratched morphologies on single crystal diamond surfaces were obtained, and the amorphization degree was analyzed to reveal the subsurface damage mechanism. The simulation shows that the oxygen-containing functional groups from graphene oxide chemically react with the carbon atoms on the single crystal diamond surface, removing in the form of CO, CO2, or adhering to the abrasive grains. Also, the chemical adsorption reduced the activation energy of carbon atoms in single crystal diamond, and made it more loosely arranged and easier to be removed by mechanical scratching, which explained the experimental results. © 2025 Elsevier B.V.

关键词： Single crystals

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Less-Conservative Robust Path Tracking Control With Intrinsic Bump-Free Feature for Autonomous Vehicles: A Sub-Polytope Integrated Approach

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IEEE Transactions on intelligent Transportation Systems 2025年

作者： Zhang, Liqin Hu, Manjiang Bian, Yougang Zhang, Hui Nguyen, Anh-Tu Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University Wuxi Intelligent Control Research Institute Wuxi214115 China Beihang University Department of Transportation Science and Engineering Beijing100191 China Université Polytechnique Hauts-de-France LAMIH Laboratory UMR CNRS 8201 Valenciennes59313 France INSA Hauts-de-France Valenciennes59313 France

This paper proposes a novel sub-polytope integrated approach (sPIA) that features an intrinsic bump-free transition, aiming to reduce conservatism in the design of path tracking control for autonomous vehicles with large range time-varying longitudinal velocity. The approach encapsulates the interdependent time-varying parameters associated with longitudinal velocity as a set of finite-vertex sub-polytopes interconnected via junction points, thereby reducing the conservatism induced by modeling overbounding. The integration of junction points and the formulation of sub-region activation rules provide a theoretical foundation for avoiding abrupt changes in feedback gains, ensuring a bump-free transition between sub-regions. A gain-scheduling state feedback controller is designed, employing parameter-dependent Lyapunov functions to further attenuate design conservatism. The effectiveness of the proposed method in reducing design conservatism is demonstrated by a comparative analysis of the optimal H∞ performance indices across various sub-polytope integration schemes. Furthermore, the superiority of the method is exemplified via simulations within real-world driving scenarios, utilizing the high-fidelity CarSim-Simulink platform. The results indicate that the proposed sPIA outperforms traditional polytopic methods in path tracking performance. This improvement, together with the effective avoidance of bumps during sub-regional transitions, confirms the efficacy of the proposed approach. Moreover, the real-time performance of the method is verified by hardware-in-the-loop experiments. © 2000-2011 IEEE.

关键词： Lyapunov functions

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Deployment strategies of on-orbit service vehicles for multi-objective group and multiple spacecraft

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International Journal of Engineering Systems Modelling and Simulation 2017年第2期9卷 96-102页

作者： Zhang, Jianxin Zhang, Qiang Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China

A novel deployment method of on-orbit service vehicles (OSVs) design for multiple-objective and multiple-orbit missions is evaluated in this paper. The given deployment method mainly includes four steps, and respectively are: 1) dividing the predetermined region into unequal and independent servicing spaces;2) calculating parking-orbit for different target areas;3) computing the minimum number of OSVs;4) calculating the position elements of each parking-orbit. The proposed method can cover the service areas by using the fewest OSVs, which can improve the efficiency of the OSV program. Although the present exploration of deployment strategy is based on circular orbital models, this method can be directly extended to other orbital models. The calculation examples based on Glonass and GPS constellation orbits shows the proposed method is effective. © 2017 Inderscience Enterprises Ltd.

关键词： Orbits

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Palmprint recognition using phase congruency and region-based orientation code

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Journal of Computational and Theoretical Nanoscience 2016年第4期13卷 2461-2466页

作者： Zhang, Jianxin Liu, Ligang Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China

In this paper, a novel palmprint recognition method by a combination of phase congruency model and region-based orientation code approach is proposed. Palmprint phase congruency feature images are firstly achieved by the multi-scale and multi-orientation 2D Log-Gabor wavelet filters. Then, a region orientation code approach, which is based on the orientation competition result of the sub-region images, is put forward to give more compact representation of palmprint phase congruency feature images. The proposed palmprint recognition method can provide more robust discriminant feature. The experiment results on the public PolyU palmprint database show the good recognition performance of proposed method. © Copyright 2016 American Scientific Publishers All rights reserved.

关键词： Palmprint recognition

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of Computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China Department of Computer Science Wenzhou-Kean University Wenzhou China Jarvis Research Center Tencent YouTu Lab Shenzhen China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods.

关键词： Multi-modal learning Semantic segmentation Weakly-supervised learning

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Acoustic scene classification by enhanced high- frequency weak signal characteristic spectrum 8

Acoustic scene classification by enhanced high- frequency we...

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8th International Conference on computing and Pattern Recognition, ICCPR 2019

作者： Hao, Wenjie Zhao, Lasheng Zhang, Qiang Key Laboratory of Advanced Design Intelligent Computing Ministry of Education Dalian University China

ISBN: (纸本)9781450376570

In the acoustic scene classification task, the method of using melspectrogram to express the acoustic scene information is widely applied. However, mel-spectrogram has defects and it ignores important information about some acoustic scenes. The paper improves the mel-spectrogram and its generation algorithm. Including: I. For the sensitivity of the acoustic scene to highfrequency acoustic signals, the paper changes the filter design method of Mel Frequency Cepstrum Coefficient (MFCC). This method preserves more high frequency information by applying the equal-height triangular filter banks and increasing the number of the filters. II. Based on the previous step, an enhancement algorithm is proposed for the problem of the lack of highfrequency weak signals in the characteristic spectrum. The algorithm performs nonlinear mapping on the mel-spectrogram, which makes the transformed high-frequency weak signal feature information more obvious. The algorithm is verified by DCASE 2018 acoustic scene classification dataset and LITIS ROUEN dataset. The experimental results demonstrate the effectiveness of the proposed algorithm. © 2019 Association for computing Machinery.

关键词： Spectrographs

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Watermarking embedding algorithm for archive image based on image normalization and principal component analysis

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Journal of Computational and Theoretical Nanoscience 2014年第9期11卷 1947-1953页

作者： Wang, Ling Zhou, Changjun Archives of Dalian University Dalian 116622 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

In this paper, an algorithm that embed watermarking into archive image based on image normalization and PCA is proposed, which is based on image normalization and invariant centroid theory. For the purpose of enhancing the invisibility and resistance to geometric attack of watermarking, we first encrypt the watermarking with chaotic method before information embedding, and then eliminate the effect of geometric change to archive image by utilizing image normalization. Finally, PCA was applied to embed the watermarking in subspace. Corresponding, we perform inverse transform to the archive image which is attacked by geometric transformation before extracting the watermarking. Thus, we can prove the truth of the attacked archive image by the extracted watermarking which is recognizable. Copyright © 2014 American Scientific Publishers All rights reserved.

关键词： Principal component analysis

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Stfi: Structure-Aware and Time-Frequency Domain Interaction Network for Deformable Medical Image Registration

SSRN

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SSRN 2025年

作者： Hu, Rui Wang, Nian Zhang, Yuxin Li, Teng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Advanced Imaging Research Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas75390 United States School of Cyber Science and Engineering Southeast University No.2 Southeast University Road Nanjing211189 China

Deformable image registration is crucial in medical image analysis. Recently, unsupervised methods using intensity matching have shown promising results without labeled data. However, these methods still face challenges: (1) They often encounter local optima when handling anatomical structures with significant variations in intensity distribution;(2) Time domain convolution in decoders leads to loss of high-frequency fine features, limiting the effectiveness of generating fine deformation vector fields. To address these issues, we propose a novel structure-aware and time-frequency domain interaction (STFI) network. STFI integrates a structure-aware frequency domain loss and a time-frequency domain interaction module. The former leverages principal structural information from the weighted phase spectrum to guide the registration process, thereby reducing the risk of local optima. The latter processes and amalgamates information from both the time and frequency domains, effectively preserving high-frequency details. The efficacy and adaptability of our approach have been demonstrated on two types of data, offering a new perspective on exploiting time-frequency domain information for medical image registration. © 2025, The Authors. All rights reserved.

关键词： Image registration

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