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International Conference on Intelligent Transportation

作者： Yuying Liu Shaoyi Du Shengwei Zhao Zhiqiang Tian Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China

Unsupervised domain adaptation (UDA) suffers from misleading by misclassified samples when aligning the labeled source domain and the unlabeled target domain, which causes unreliable alignment and class overlap. In this paper, we propose a center-aware UDA algorithm for inter-domain alignment, along with class diversity regularization for target intra-domain separability. First, we align the class centers of the source and target domain by weighting the sample confidence to corresponding centers, to denoise the mislabeled samples. Second, we scatter the predictions of the target domain by minimizing the entropy of intra-class predictions and decreasing the similarity of inter-class distributions, which tackles the class overlap. The proposed method not only aligns the two domains but also increases the separability of the target domain, which results in improved target data classification performance. We validate the proposed method on behavior recognition and object recognition datasets, and the experimental results show that the method greatly alleviates the domain shift compared to the latest UDA methods. Code is available at: https://***/seizetheday00/Center-Aware-DA.

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Renewable Energy Short Circuit Ratio Prediction Based on Spiking Neural Networks

Renewable Energy Short Circuit Ratio Prediction Based on Spi...

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Chinese Automation Congress (CAC)

作者： Geng Zhang Shuangming Yang Badong Chen National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Electrical and Information Engineering Tianjin University Tianjin China

The integration of renewable energy power significantly influences the voltage support strength of the power system. With the rapid increase in renewable energy generation capacity in recent years, the power system faces mounting pressure. The short circuit ratio (SCR), a key indicator of voltage support capability, plays a crucial role in the regulation of power systems. Therefore, we propose a renewable energy SCR Prediction model based on spiking neural networks (SNNs). Moreover, this model takes into consideration the correlation between wind power generation and SCR, enabling accurate multivariate prediction. The prediction results on real-world power system data from renewable energy wind farms in north-ern China demonstrate the effectiveness of our proposed SNNs prediction model. And the SNNs model can be deployed on edge devices for low-power and efficient online prediction.

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Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS to Cryogenic Computing

Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS ...

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design, Automation and Test in Europe Conference and Exhibition

作者： Mahdi Benkhelifa Shivendra S. Parihar Anirban Kar Girish Pahwa Yogesh S. Chauhan Hussam Amrouch Chair of AI Processor Design Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Department of Electrical Engineering and Computer Sciences University of California Berkeley USA National Yang Ming Chiao Tung University International College of Semiconductor Technology Taiwan Indian Institute of Technology Kanpur India

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

As CMOS scaling approaches its fundamental limits, the explosive rise of AI and LLMs has unveiled profound bottlenecks in computing architectures. This paper presents two groundbreaking paradigms poised to reshape the landscape of high-performance computing and meet the surging demands of AI-driven workloads. The first paradigm is 3D monolithic integration, a revolutionary approach that achieves unprecedented logic density through Complementary FETs (CFETs), where pMOS and nMOS transistors are vertically stacked, and a dramatic expansion of on-chip memory capacity by integrating memory layers atop logic transistors. The second paradigm leverages the transformative potential of operating chips at cryogenic temperatures where transistors exhibit enhanced performance, and parasitic resistances are substantially minimized. These advancements hold the promise of redefining computing efficiency and performance for the AI era.

关键词： Semiconductor device modeling Three-dimensional displays High performance computing Random access memory Cryogenics Silicon System-on-chip Logic Three-dimensional integrated circuits Optimization

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Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS to Cryogenic Computing

Pushing the Boundaries of AI Chips: From Monolithic 3D CMOS ...

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2025 design, Automation and Test in Europe Conference, DATE 2025

作者： Benkhelifa, Mahdi Parihar, Shivendra S. Kar, Anirban Pahwa, Girish Chauhan, Yogesh S. Amrouch, Hussam Technical University of Munich Tum School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Ai Processor Design Munich Germany University of California Department of Electrical Engineering and Computer Sciences Berkeley United States National Yang Ming Chiao Tung University International College of Semiconductor Technology Taiwan Indian Institute of Technology Kanpur India

ISBN: (纸本)9783982674100

关键词： BiCMOS technology

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Consistency Inspection for Assembly of Bolt on Engine Using Multi-view Stereo

Consistency Inspection for Assembly of Bolt on Engine Using ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuo Liu Minglv Jiang Yuanliang Lu Jianji Wang Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China

Assembly inspection is a crucial aspect of smart manufacturing to guarantee the quality of products. To address the challenges posed by complex workshop scenes, an assembly inspection algorithm based on multi-view stereo is proposed. Specifically, to enhance the accuracy of reconstruction, the multi-view stereo method utilizes segmentation attention-assisted depth estimation and point cloud fusion to mitigate the noise in the reconstructed point cloud. And the proposed method introduces normalized depth loss to improve the reconstruction ability of foreground pixels. Futhermore, this paper presents a novel 3D point cloud-based size estimation algorithm capable of accurately estimating the size of small objects in complex working scenes. Experimental results demonstrate the efficacy of the improved multi-view stereo algorithm in assembly scenes and verify the superiority of the proposed size estimation algorithm.

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Compensation Architecture to Alleviate Noise Effects in RRAM-based Computing-in-memory Chips with Residual Resource

Compensation Architecture to Alleviate Noise Effects in RRAM...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Xiaoqing Zhao Longjun Liu Yuyi Liu Bin Gao Hongbin Sun Institute of National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an Shaanxi China School of Integrated Circuits Beijing Innovation Center for Future Chips (ICFC) Tsinghua University Beijing China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Resistive random access memory (RRAM) is a promising technology for energy-efficient in-memory computing. However, due to technology limits, RRAM device faces a series of reliability issues. Deep neural network (DNN) computing based on RRAM suffers from accuracy degradation. On the one hand, offline DNN training solutions are difficult to fully consider and simulate all nonidealities. Worse still, new error or nonideality may come up with the usage of RRAM, which further deteriorates the effectiveness of offline training. On the other hand, online training poses great challenges on programming overhead and device lifetime. The iterative write-verify technique to program multi-bit RRAM cells prolongs write latency more than 10× longer than read latency. To overcome these issues, we propose a compensation architecture and a software and hardware co-training design to mitigate the realistic network accuracy loss in RRAM-based computing-in-memory chips. Firstly, we add trainable compensation channels in crossbars utilizing the residual resource after original weight mapping. Secondly, an offline training procedure with computing output from hardware is triggered to settle down appropriate weight value in compensation channels. Experimental results demonstrate that the proposed design can guarantee ≤ 0.8% loss of accuracy in DNN on MNIST and CIFAR10 dataset even when nonidealities reduce the original accuracy down to ≤73%.

关键词： Training Accuracy Resistive RAM Noise Artificial neural networks Computer architecture Programming

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Predicting RTMS Treatment Effects Using Open-Loop Control and Neural Manifold

Predicting RTMS Treatment Effects Using Open-Loop Control an...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hongyu Shi Kaizhong Zheng Huaning Wang Baojuan Li Badong Chen National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xijing Hospital Fourth Military Medical University Xi’an China School of Biomedical Engineering Fourth Military Medical University Xi’an China

Repetitive transcranial magnetic stimulation (rTMS) is a common non-invasive treatment for medication-resistant major depressive disorder (MDD). It utilizes continuous and adjustable magnetic stimulation to modulate neural circuits implicated in the pathogenesis of depression. Nevertheless, constructing a universal and effective predictive factor for forecasting treatment outcomes remains challenging. To address this, we first collect neuroimaging data and five depression scales from 26 medication-resistant MDD patients before and after rTMS treatment. Then we propose a novel framework for predicting treatment effects precisely, which combines open-loop control and neural manifold estimation. This framework utilizes the geometric information of the manifold as a biomarker to predict the therapeutic efficacy of rTMS. Experiments based on the clinical dataset demonstrate the effectiveness and robustness of our framework.

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and machine intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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Towards Unconstrained Collision Injury Protection Data Sets: Initial Surrogate Experiments for the Human Hand

arXiv

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arXiv 2024年

作者： Kirschner, Robin Jeanne Yang, Jinyu Elshani, Edonis Micheler, Carina M. Leibbrand, Tobias Müller, Dirk Glowalla, Claudio Rajaei, Nader Burgkart, Rainer Haddadin, Sami Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich80992 Germany Department of Orthopaedics and Sports Orthopaedics Klinikum Rechts der Isar TUM School of Medicine Technical University of Munich Munich81675 Germany Institute for Machine Tools and Industrial Management TUM School of Engineering and Design Technical University of Munich Garching near Munich85748 Germany Endoprothetikzentrum der Maximalversorgung BG Hospital Murnau Murnau82418 Germany

Safety for physical human-robot interaction (pHRI) is a major concern for all application domains. While current standardization for industrial robot applications provide safety constraints that address the onset of pain in blunt impacts, these impact thresholds are difficult to use on edged or pointed impactors. The most severe injuries occur in constrained contact scenarios, where crushing is possible. Nevertheless, situations potentially resulting in constrained contact only occur in certain areas of a workspace and design or organisational approaches can be used to avoid them. What remains are risks to the human physical integrity caused by unconstrained accidental contacts, which are difficult to avoid while maintaining robot motion efficiency. Nevertheless, the probability and severity of injuries occurring with edged or pointed impacting objects in unconstrained collisions is hardly researched. In this paper, we propose an experimental setup and procedure using two pendulums modeling human hands and arms and robots to understand the injury potential of unconstrained collisions of human hands with edged objects. Pig feet are used as ex vivo surrogate samples - as these closely resemble the physiological characteristics of human hands - to create an initial injury database on the severity of injuries caused by unconstrained edged or pointed impacts. For the effective mass range of typical lightweight robots, the data obtained show low probabilities of injuries such as skin cuts or bone/tendon injuries in unconstrained collisions when the velocity is reduced to © 2024, CC BY.

关键词： Industrial robots

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CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP

arXiv

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arXiv 2024年

作者： Zuo, Zuo Dong, Jiahao Wu, Yao Qu, Yanyun Wu, Zongze National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'An710049 China China The School of Informatics Xiamen University Xiamen361005 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

Industrial anomaly classification (AC) is an indispensable task in industrial manufacturing, which guarantees quality and safety of various product. To address the scarcity of data in industrial scenarios, lots of few-shot anomaly detection methods emerge recently. In this paper, we propose an effective few-shot anomaly classification (FSAC) framework with one-stage training, dubbed CLIP-FSAC++. Specifically, we introduce a cross-modality interaction module named Anomaly Descriptor following image and text encoders, which enhances the correlation of visual and text embeddings and adapts the representations of CLIP from pre-trained data to target data. In anomaly descriptor, image-to-text cross-attention module is used to obtain image-specific text embeddings and text-to-image cross-attention module is used to obtain text-specific visual embeddings. Then these modality-specific embeddings are used to enhance original representations of CLIP for better matching ability. Comprehensive experiment results are provided for evaluating our method in few-normal shot anomaly classification on VisA and MVTEC-AD for 1, 2, 4 and 8-shot settings. The source codes are at https://***/Jay-zzcoder/clip-fsac-pp. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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