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Brain-inspired Intelligent Robotics:Theoretical Analysis and systematic Application

Machine intelligence Research 2023年第1期20卷 1-18页

作者： Hong Qiao Ya-Xiong Wu Shan-Lin Zhong Pei-Jie Yin Jia-Hao Chen The State Key Laboratory of Management and Control for Complex System Institute of AutomationChineseAcademyof SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Chinese Academy of Science Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China

Traditional joint-link robots have been widely used in production lines because of their high precision for single *** the development of the manufacturing and service industries,the requirement for the comprehensive performance of robotics is *** types of bio-inspired robotics have been investigated to realize human-like motion control and manipulation.A study route from inner mechanisms to external structures is proposed to imitate humans and animals *** this idea,a brain-inspired intelligent robotic system is constructed that contains visual cognition,decision-making,motion control,and musculoskeletal *** paper reviews cutting-edge research in brain-inspired visual cognition,decision-making,motion control,and musculoskeletal *** software systems and a corresponding hardware system are established,aiming at the verification and applications of next-generationbrain-inspired musculoskeletal robots.

关键词： Brain-inspired intelligent robot software and hardware decision making muscle control cognitive intelligence.

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Structure and corrosion behavior of FeCoCrNiMo high-entropy alloy coatings prepared by mechanical alloying and plasma spraying

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International Journal of Minerals,Metallurgy and Materials 2024年第12期31卷 2692-2705页

作者： Yun Tian Jianing Liu Mingming Xue Dongyao Zhang Yuxin Wang Keping Geng Yanchun Dong Yong Yang School of Materials Science and Engineering Hebei University of TechnologyTianjin 300132China Key Lab.for New Type of Functional Materials in Hebei Province School of Materials Science and EngineeringState Key Laboratory of Reliability and Intelligence of Electrical EquipmentHebei University of TechnologyTianjin 300132China School of Mechanical Engineering Tianjin Sino-German University of Applied SciencesTianjin 300132China

FeCoCrNiMox composite powders were prepared using the mechanical alloying technique and made into high-entropy alloy(HEA)coatings with the face-centered cubic phase using plasma spraying to address the element segregation problem in HEAs and pre-pare uniform HEA *** electron microscopy,transmission electron microscopy,and X-ray diffractometry were employed to characterize these coatings’microstructure and phase *** hardness,elastic modulus,and fracture toughness of coatings were tested,and the corrosion resistance was analyzed in simulated *** show that the hardness of the coating is HV0.1606.15,the modulus of elasticity is 128.42 GPa,and the fracture toughness is 43.98 MPa·m^(1/2).The corrosion potential of the coating in 3.5wt%NaCl solution is-0.49 V,and the corrosion current density is 1.2×10^(−6)A/cm^(2).The electrochemical system comprises three parts:the electrolyte,the adsorption and metallic oxide films produced during immersion,and the FeCoNiCrMo HEA *** in-creasingly long periods,the corrosion reaction rate increases first and then decreases,the corrosion product film comprising metal oxides reaches a dynamic balance between formation and dissolution,and the internal reaction of the coating declines.

关键词： high-entropy alloy coatings plasma spray mechanical alloying microstructure corrosion behavior mechanical property

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Color liquid crystal grating based color holographic 3D display system with large viewing angle

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Light(Science & Applications) 2024年第1期13卷 103-112页

作者： Di Wang Yi-Long Li Fan Chu Nan-Nan Li Zhao-Song Li Sin-Doo Lee Zhong-Quan Nie Chao Liu Qiong-Hua Wang School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing 100191China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Display Technology Research Center Seoul National UniversityGwanak-ro 1Gwanak-guSeoul 08826Republic of Korea Key Lab of Advanced Transducers and Intelligent Control System Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China

Holographic 3D display is highly desirable for numerous applications ranging from medical treatments to military ***,it is challenging to simultaneously achieve large viewing angle and high-fidelity color reconstruction due to the intractable constraints of existing ***,we conceptually propose and experimentally demonstrate a simple and feasible pathway of using a well-designed color liquid crystal grating to overcome the inevitable chromatic aberration and enlarge the holographic viewing angle,thus enabling large-viewing-angle and color holographic 3D *** use of color liquid crystal grating allows performing secondary diffraction modulation on red,green and blue reproduced images simultaneously and extending the viewing angle in the holographic 3D display *** principle,a chromatic aberration-free hologram generation mechanism in combination with the color liquid crystal grating is proposed to pave the way for on such a superior holographic 3D *** proposed system shows a color viewing angle of~50.12°,which is about 7 times that of the traditional system with a single spatial light *** work presents a paradigm for achieving desirable holographic 3D display,and is expected to provide a new way for the wide application of holographic display.

关键词： holographic angle crystal

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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy systems 2025年第6期33卷 1899-1912页

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points. Our code is available at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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Revealing the Semantics of Data Wrangling Scripts with Comantics

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IEEE Transactions on Visualization and Computer Graphics 2023年第1期29卷 117-127页

作者： Xiong, Kai Luo, Zhongsu Fu, Siwei Wang, Yongheng Xu, Mingliang Wu, Yingcai State Key Lab of CAD&CG Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China Zhejiang University of Technology Hangzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. Then, we develop Comantics, a three-step pipeline that automatically detects the semantics of data transformation scripts. The first step focuses on the detection of table differences for each line of wrangling code. Second, we incorporate a characteristic-based component and a Siamese convolutional neural network-based component for the detection of transformation types. Third, we derive the parameters of each data transformation by employing a 'slot filling' strategy. We design experiments to evaluate the performance of Comantics. Further, we assess its flexibility using three example applications in different domains. © 2022 IEEE.

关键词： Semantics

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Application and Implementation of Convolutional Neural Network Accelerator Based on FPGA in Environmental Sound Classification 8

Application and Implementation of Convolutional Neural Netwo...

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8th International Conference on Computer and Communication systems, ICCCS 2023

作者： Zhang, Jianwei Yan, Haonan School of Biomedical Engineering School of Artificial Intelligence Dalian University of Technology Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian China School of Biomedical Engineering Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian China

ISBN: (纸本)9781665456128

With the development of artificial intelligence, convolutional neural network (CNN) has achieved great success in various learning tasks, and has been applied to environmental sound classification tasks, achieving a high classification accuracy. With the development of convolutional neural network in the application of environmental sound classification, the design of hardware accelerator for convolutional neural network has become a hot topic. Compared with CPU, GPU and ASIC, FPGA has the unique advantages of low power consumption and high energy efficiency in accelerating deep learning, and has become the preferred hardware platform for neural network hardware implementation. In this paper, a convolution neural network accelerator based on FPGA is proposed, which can realize convolution, tanh nonlinear activation, average pooling and other operations. The hardware accelerator can realize the inference process of environmental sound classification, and achieve high energy efficiency and low resource consumption while having a high classification accuracy. The network was tested on Xilinx Virtex VC-707 FPGA board. The experimental results show that its energy efficiency is 547.60 and 377.36 times higher than that of CPU and GPU respectively. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Augmentation-based discriminative meta-learning for cross-machine few-shot fault diagnosis

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Science China(Technological Sciences) 2023年第6期66卷 1698-1716页

作者： XIA PengCheng HUANG YiXiang WANG YuXiang LIU ChengLiang LIU Jie State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Lab of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Mechanical and Aerospace Engineering Carleton UniversityOttawa ON K1S 5B6Canada

Deep learning methods have demonstrated promising performance in fault diagnosis *** the scarcity of data in industrial scenarios limits the practical application of such methods,transfer learning effectively tackles this issue through crossmachine knowledge ***,the cross-machine few-shot problem,which is a more general industrial scenario,has been rarely *** studies have not considered the cross-machine domain shift problem,which results in poor testing *** paper proposes an augmentation-based discriminative meta-learning method to address this *** the meta-training process,signal transformation is proposed to increase the meta-task diversity for more robust feature learning,and multi-scale learning is combined for more adaptive feature *** the meta-testing process,limited labeled fault information is used to promote model generalization in the target domain through quasi-meta-training based on data ***,a novel hyperbolic prototypical loss is proposed for more discriminative feature representation and separable category prototypes by designing a hyperbolic decision ***-machine few-shot diagnosis experiments were conducted using three datasets from different machines,namely,the bearing,motor,and gear *** effectiveness of the proposed method was verified through ablation and comparison studies.

关键词： fault diagnosis few-shot learning meta-learning data augmentation cross-machine discriminative loss

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FEDERATED NEAREST NEIGHBOR MACHINE TRANSLATION 11

FEDERATED NEAREST NEIGHBOR MACHINE TRANSLATION

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11th International Conference on Learning Representations, ICLR 2023

作者： Du, Yichao Zhang, Zhirui Wu, Bingzhe Liu, Lemao Xu, Tong Chen, Enhong University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent AI Lab China

To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithms (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for translation tasks due to the vast communication overheads and heavy synchronization. In this paper, we propose a novel Federated Nearest Neighbor (FedNN) machine translation framework that, instead of multi-round model-based interactions, leverages one-round memorization-based interaction to share knowledge across different clients and build low-overhead privacy-preserving systems. The whole approach equips the public NMT model trained on large-scale accessible data with a k-nearest-neighbor (kNN) classifier and integrates the external datastore constructed by private text data from all clients to form the final FL model. A two-phase datastore encryption strategy is introduced to achieve privacy-preserving during this process. Extensive experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg, while maintaining promising translation performance in different FL settings. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Nearest neighbor search

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ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective Reasoning 25

ImageScope: Unifying Language-Guided Image Retrieval via Lar...

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34th ACM Web Conference, WWW 2025

作者： Luo, Pengfei Zhou, Jingbo Xu, Tong Xia, Yuan Xu, Linli Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Business Intelligence Lab Baidu Research Beijing China Baidu Inc Beijing China

ISBN: (纸本)9798400712746

With the proliferation of images in online content, language-guided image retrieval (LGIR) has emerged as a research hotspot over the past decade, encompassing a variety of subtasks with diverse input forms. While the development of large multimodal models (LMMs) has significantly facilitated these tasks, existing approaches often address them in isolation, requiring the construction of separate systems for each task. This not only increases system complexity and maintenance costs, but also exacerbates challenges stemming from language ambiguity and complex image content, making it difficult for retrieval systems to provide accurate and reliable results. To this end, we propose ImageScope, a training-free, three-stage framework that leverages collective reasoning to unify LGIR tasks. The key insight behind the unification lies in the compositional nature of language, which transforms diverse LGIR tasks into a generalized text-to-image retrieval process, along with the reasoning of LMMs serving as a universal verification to refine the results. To be specific, in the first stage, we improve the robustness of the framework by synthesizing search intents across varying levels of semantic granularity using chain-of-thought (CoT) reasoning. In the second and third stages, we then reflect on retrieval results by verifying predicate propositions locally, and performing pairwise evaluations globally. Experiments conducted on six LGIR datasets demonstrate that ImageScope outperforms competitive baselines. Comprehensive evaluations and ablation studies further confirm the effectiveness of our design. © 2025 Copyright held by the owner/author(s).

关键词： Photointerpretation

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Fight Back Against Jailbreaking via Prompt Adversarial Tuning 38

Fight Back Against Jailbreaking via Prompt Adversarial Tunin...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Mo, Yichuan Wang, Yuji Wei, Zeming Wang, Yisen State Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Shanghai Jiao Tong University China School of Mathematical Sciences Peking University China Institute for Artificial Intelligence Peking University China

While Large Language Models (LLMs) have achieved tremendous success in various applications, they are also susceptible to jailbreaking attacks. Several primary defense strategies have been proposed to protect LLMs from producing harmful information, mostly focusing on model fine-tuning or heuristical defense designs. However, how to achieve intrinsic robustness through prompt optimization remains an open problem. In this paper, motivated by adversarial training paradigms for achieving reliable robustness, we propose an approach named Prompt Adversarial Tuning (PAT) that trains a prompt control attached to the user prompt as a guard prefix. To achieve our defense goal whilst maintaining natural performance, we optimize the control prompt with both adversarial and benign prompts. Comprehensive experiments show that our method is effective against both grey-box and black-box attacks, reducing the success rate of advanced attacks to nearly 0%, while maintaining the model's utility on the benign task and incurring only negligible computational overhead, charting a new perspective for future explorations in LLM security. Our code is available at https://***/PKU-ML/PAT. © 2024 Neural information processing systems foundation. All rights reserved.

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