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56th International Symposium on robotics, ISR Europe 2023

作者： Nguyen, Khang Dang, Tuan Huber, Manfred Learning and Adaptive Robotics Laboratory Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX76013 United States

ISBN: (纸本)9783800761418

Vision-based object manipulation in assistive mobile cobots essentially relies on classifying the target objects based on their 3D shapes and features, whether they are deformed or not. In this work, we present an auto-generated dataset of deformed objects specific for assistive mobile cobot manipulation using an intuitive Laplacian-based mesh deformation procedure. We first determine the graspable region of the robot hand on the given object’s mesh. Then, we uniformly sample handle points within the graspable region and perform deformation with multiple handle points based on the robot gripper configuration. In each deformation, we identify the orientation of handle points and prevent self-intersection to guarantee the object’s physical meaning when multiple handle points are simultaneously applied to the mesh at different deformation intensities. We also introduce a lightweight neural network for 3D deformable object classification. Finally, we test our generated dataset on the Baxter robot with two 7-DOF arms, an integrated RGB-D camera, and a 3D deformable object classifier. The result shows that the robot is able to classify real-world deformed objects from point clouds captured at multiple views by the RGB-D camera. The source code is available at https://***/mkhangg/deformable_cobot. © VDE VERLAG GMBH Berlin Offenbach.

关键词： 3D vision deformable object manipulation

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Speed Harmonic based Saturation Free Inductance Modeling and Estimation of Interior PMSM Using Measurements Under One Load Condition

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CES Transactions on Electrical Machines and Systems 2025年第1期9卷 91-99页

作者： Guodong Feng Yuting Lu Zhe Tong Beichen Ding Guishan Yan Chunyan Lai the School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhenChina the School of Advanced Manufacturing Sun Yat-sen University the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) China the Department of Electrical and Computer Engineering Concordia UniversityQCCanada

For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from multiple load/current conditions,and the estimation is relying on the accuracy of saturation model and other machine parameters in the *** harmonic produced by harmonic currents is inductance-dependent,and thus this paper explores the use of magnitude and phase angle of the speed harmonic for accurate inductance *** estimation models are built based on either the magnitude or phase angle,and the inductances can be from d-axis voltage and the magnitude or phase angle,in which the filter influence in harmonic extraction is considered to ensure the estimation *** inductances can be estimated from the measurements under one load condition,which is free of saturation ***,the inductance estimation is robust to the change of other machine *** proposed approach can effectively improve estimation accuracy especially under the condition with low current *** and comparisons are conducted on a test PMSM to validate the proposed approach.

关键词： PMSM Inductance estimation Speed harmonic High frequency signal injection

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Shape effect of prodrug nanoassemblies on treatment efficacy of cancer therapy

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Nano Research 2025年第2期18卷 616-626页

作者： Fengxiang Liu Rongzheng Liu Xiaoyuan Fan Xia Wang Kaiyuan Wang Hainan Zhao Hao Ye Shunzhe Zheng Xiao Kuang Yinxian Yang Haotian Zhang Qiming Kan Zhonggui He Jian Chen Jin Sun Department of Pharmaceutics Wuya College of InnovationShenyang Pharmaceutical UniversityShenyang 110016China School of Pharmacy Shenyang Pharmaceutical UniversityShenyang 110016China Nephrology department The First Affiliated Hospital of Jinzhou Medical UniversityJinzhou 121000China Multi-Scale Robotics Lab(MSRL) Institute of Robotics&Intelligent Systems(IRIS)ETH Zurich 8092Switzerland School of Life Science and Biopharmaceutics Shenyang Pharmaceutical UniversityShenyang 110016China Joint International Research Laboratory of Intelligent Drug Delivery Systems Ministry of EducationShenyang 110016China School of Pharmacy Shanghai Jiao Tong UniversityShanghai 200240China

The tumor microenvironment-sensitive prodrug-based nanoparticles(NPs)have emerged as a promising drug delivery system(DDS).The shape of these particles plays a crucial role in their in vivo ***,non-spherical organic NPs are rarely reported due to the inherent flexibility and variability of organic ***,we fabricate reduction-sensitive prodrug NPs and explore the impact of their morphology properties on their in vivo *** are self-assembled into spherical NPs with distearoyl phosphoethanolamine-PEG2000(DSPE-PEG2k),or into rod-shaped NPs with D-a-tocopherol polyethylene glycol 2000 succinate(TPGS2k)due to the stronger binding *** comparison with spherical NPs,the endocytosis of rod-shaped NPs predominantly relies on caveolae-mediated pathways rather than clathrin-mediated ones,potentially avoiding degradation by ***,the rod-shaped NPs exhibit prolonged circulation time,increased tumor accumulation,and enhanced antitumor *** current findings reveal the significant effect of particle shape on the behavior of prodrug NPs and introduce a novel paradigm for high-efficacy cancer therapy of prodrug NPs.

关键词： camptothecin self-assembly prodrug-based nanoparticles nanorod cancer therapy

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OFDM-structure based waveform designs for integrated sensing and communication

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science China(Information sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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Surrogate-Assisted Multiobjective Neural Architecture Search for Real-Time Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1602-1615页

作者： Lu, Zhichao Cheng, Ran Huang, Shihua Zhang, Haoming Qiu, Changxiao Yang, Fan Southern University of Science and Technology Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Huawei Technologies Co. Ltd. Hisilicon Research Department Shenzhen518055 China

The architectural advancements in deep neural networks have led to remarkable leap-forwards across a broad array of computer vision tasks. Instead of relying on human expertise, neural architecture search (NAS) has emerged as a promising avenue toward automating the design of architectures. While recent achievements on image classification have suggested opportunities, the promises of NAS have yet to be thoroughly assessed on more challenging tasks of semantic segmentation. The main challenges of applying NAS to semantic segmentation arise from two aspects: 1) high-resolution images to be processed;2) additional requirement of real-time inference speed (i.e., real-time semantic segmentation) for applications such as autonomous driving. To meet such challenges, we propose a surrogate-assisted multiobjective method in this article. Through a series of customized prediction models, our method effectively transforms the original NAS task to an ordinary multiobjective optimization problem. Followed by a hierarchical prescreening criterion for in-fill selection, our method progressively achieves a set of efficient architectures trading-off between segmentation accuracy and inference speed. Empirical evaluations on three benchmark datasets together with an application using Huawei Atlas 200 DK suggest that our method can identify architectures significantly outperforming existing state-of-the-art architectures designed both manually by human experts and automatically by other NAS methods. Code is available from here. © 2020 IEEE.

关键词： computer architecture

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A Two-Layer Network Intrusion Detection Method Incorporating LSTM and Stacking Ensemble Learning

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computers, Materials & Continua 2025年第6期83卷 5129-5153页

作者： Jun Wang Chaoren Ge Yihong Li Huimin Zhao Qiang Fu Kerang Cao Hoekyung Jung College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China Key Laboratory of Intelligent Technology for Chemical Process Industry of Liaoning Province Shenyang110142China Computer Engineering Department Paichai UniversityDaejeon35345Republic of Korea

Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network *** improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer *** first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class *** second layer further segments the minority class attacks through Stacking ensemble *** datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the *** results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,*** also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class *** with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection *** research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.

关键词： Two-layer architecture minority class attack stacking ensemble learning network intrusion detection

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Counterfactual discriminative micro-expression recognition

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Visual Intelligence 2024年第1期2卷 1-10页

作者： Li, Yong Liu, Menglin Lao, Lingjie Wang, Yuanzhi Cui, Zhen Department of Computer Science City University of Hong Kong 999077 Hong Kong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Micro-expressions are spontaneous, rapid and subtle facial movements that can hardly be suppressed or fabricated. Micro-expression recognition (MER) is one of the most challenging topics in affective computing. It aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting period. Recently, many deep learning-based MER methods have been developed. However, how to effectively capture subtle temporal variations for robust MER still perplexes us. We propose a counterfactual discriminative micro-expression recognition (CoDER) method to effectively learn the slight temporal variations for video-based MER. To explicitly capture the causality from temporal dynamics hidden in the micro-expression (ME) sequence, we propose ME counterfactual reasoning by comparing the effects of the facts w.r.t. original ME sequences and the counterfactuals w.r.t. counterfactually-revised ME sequences, and then perform causality-aware prediction to encourage the model to learn those latent ME temporal cues. Extensive experiments on four widely-used ME databases demonstrate the effectiveness of CoDER, which results in comparable and superior MER performance compared with that of the state-of-the-art methods. The visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces. © The Author(s) 2024.

关键词： Affective computing Causal graph Counterfactual reasoning Micro-expression recognition Temporal variation

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SoloGAN: Multi-domain Multimodal Unpaired Image-to-Image Translation via a Single Generative Adversarial Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 722-737页

作者： Huang, Shihua He, Cheng Cheng, Ran Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

Despite significant advances in image-to-image (I2I) translation with generative adversarial networks (GANs), it remains challenging to effectively translate an image to a set of diverse images in multiple target domains using a pair of generator and discriminator. Existing multimodal I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only. Nevertheless, we argue that the content (domain-invariance) features should be learned from images among all of the domains. Consequently, each domain-specific content encoder of existing schemes fails to extract the domain-invariant features efficiently. To address this issue, we present a flexible and general SoloGAN model for efficient multimodal I2I translation among multiple domains with unpaired data. In contrast to existing methods, the SoloGAN algorithm uses a single projection discriminator with an additional auxiliary classifier and shares the encoder and generator for all domains. As such, the SoloGAN model can be trained effectively with images from all domains so that the domain-invariance content representation can be efficiently extracted. Qualitative and quantitative results over a wide range of datasets against several counterparts and variants of the SoloGAN model demonstrate the merits of the method, especially for challenging I2I translation tasks, i.e., tasks that involve extreme shape variations or need to keep the complex backgrounds unchanged after translations. Furthermore, we demonstrate the contribution of each component using ablation studies. © 2020 IEEE.

关键词： Generative adversarial networks

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Entropy-Guided Reinforced Open World Active 3D Object Detection Learning

Entropy-Guided Reinforced Open World Active 3D Object Detect...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Haozhe Ma, Liyan Ying, Shihui Institute of Artificial Intelligence Shanghai University Shanghai200444 China School of Computer Engineering and Science School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Department of Mathematics School of Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798350368604

Traditional fully annotated closed set 3D object detection methods improve model performance but are impractical in real-world settings due to the emergence of new categories and the complexity of 3D annotations. Open-World Object Detection (OWOD) addresses these issues but relies heavily on manual labeling, which is costly. This paper focuses on open world active learning and proposes an entropy-guided reinforced open world active 3D object detection (EROA). EROA regards active learning as a reinforcement learning problem tailored for open driving scenarios. We use entropy as a reward metric for efficient reinforcement learning. We also leverage knowledge from the 2D domain using object-level large-scale vision-language models to enhance sample selection. Extensive experiments evidence that the proposed EROA meets the dynamic and cost-sensitive requirements of autonomous driving, enabling real-time detection of both known and unknown objects. © 2024 IEEE.

关键词： Active learning

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