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Rethinking development and major research plans of Industrial Internet in China

Fundamental Research 2024年第1期4卷 3-7页

作者： Chunxiao Jiang Yang Cong Jiming Chen Chenghong Wang Guozheng Wu Ruizhen Zhao Zhiheng Wang Bin Xiao Ting Chen Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Division III Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China University of Electronic Science and Technology of China Chengdu 611731China

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial *** this foundation,we outline the development and current status of the Industrial Internet in China and ***,we detail the avant-garde paradigms encompassed within the National Natural science Foundation of China(NSFC)’s“Future Industrial Internet Fundamental Theory and Key Technologies”research plan and its corresponding management *** research initiative endeavors to enhance interdisciplinary collaborations,aiming for a synergistic alignment of industry,academia,research,and practical *** primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial *** is poised to traverse the“first mile”,encompassing foundational research and pioneering innovations specific to the industrial internet,and seamlessly bridges to the“last mile”,ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market *** anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

关键词： Industrial Internet Academic connotation Major research plan Scientific problem Policy advice

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Localization of elastic waves in one-dimensional detuned phononic crystals with flexoelectric effect

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International Journal of Smart and Nano Materials 2022年第2期13卷 244-262页

作者： Cheng Shen Yifan Kong Tian Jian Lu Shasha Yang State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjingPRChina MIIT Key Laboratory of Multifunctional Lightweight Materials and Structures Nanjing University of Aeronautics and AstronauticsNanjingPRChina Space Structure and Mechanism Technology Laboratory of China Aerospace Science and Technology Group Co.Ltd ShanghaiP.R China School of Mechanical Engineering Nanjing Vocational University of Industry TechnologyNanjingP.R China

Although forbidden band effect in perfectly periodic phononic crystals(PC)is very attractive,random disordered(***)phenomenon is inevitable in engineering processing,thus explor-ing the effect of detuning on the wave characteristics of PC becomes a *** this study,fundamental governing equa-tions and boundary conditions are derived from the principle of virtual *** characteristics and localization factor of one-dimensional(1D)detuned nano-PC are investigated based on the transfer matrix method,with flexoelectric effect duly accounted ***,with BaTiO_(3)􀀀SrTiO_(3) nano-PC taken for illustration,forbidden band properties and localization factor of 1D elastic waves in harmonic and detuned states are systematically *** is demonstrated that localization factor can characterize the energy band structure of 1D PC *** effect tends to widen the width of forbidden band with increasing detuning,and detuning is linearly related to the *** forbidden band is more sensitive to flexoelectric coefficient detuning than thickness *** research results provide useful theoretical guidance for designing high-frequency nanoscaled devices with the function of filtering based on band gap effect of nano-PC.

关键词： Flexoelectric effect phononic crystal detuning localization factor forbidden band

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Multi-Satellite Mission Planning Method for Dynamic Targets Based on Reinforcement Learning

Multi-Satellite Mission Planning Method for Dynamic Targets ...

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Chinese Automation Congress (CAC)

作者： Yilong Liu Cong Zhang Sihang Zhang Jiaxing Li Beijing Institute of Control Engineering Beijing China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing China

The autonomous mission planning problem of space targets tracking with multi-satellite is studied in this paper. For effective evaluation of different candidate tracking resource combinations, a tracking performance evaluation model based on GDOP is proposed firstly, then a tracking priorities evaluation model for maneuverable dynamic targets is designed to autonomous evaluate and update the planning priorities during the tracking. To solve the problem of conflict resolution of tracking resources scheduling, the reinforcement learning algorithm is applied, considering of restricted computation resources onboard. A pointer network is utilized as the strategy model, with the tracking targets and corresponding priorities, the tracking performance evaluation of each potential candidate tracking resources combinations as input tensors, and the tracking tasks allocation of multi-satellites will be inferenced by the network accordingly. The actor-critic reinforcement learning algorithm is selected for training. It is shown that the algorithm proposed can formulate better planning strategies than the heuristic method, which reduces the mean tracking error by 21% during the whole tracking process.

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A Time-Independent control Method for Variable-Speed Load-Bearing Walking with Suspended Backpack

A Time-Independent Control Method for Variable-Speed Load-Be...

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第43届中国控制会议

作者： Zhiyuan He Yu Cao Yifei Guo Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems Key Laboratory for Image Processing and Intelligent ControlSchool of Artificial Intelligence and AutomationHuazhong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Walking with a load brings a significant burden to human shoulders,resulting in increasing metabolic energy consumption and the risk of skeleton and muscle *** backpack has been widely conducted for load-bearing walking,but exiting time-dependent control methods and event-based control methods are unable to exploit the characteristics of human gait *** this case,a time-independent control(TIC) method capable of eliminating the time dependence and adapting to variable speed is *** on the dynamical model of human-backpack system,an assistance profile along with the parameter optimization method are introduced which allows the backpack to reduce the burden on human shoulders during load-bearing *** the simulation evaluation,the proposed TIC method is compared with an impedance controller based time-dependent method and a locked backpack,under both constant speed and variable speed *** results demonstrates that the TIC case achieves an 89.1% reduction in dynamic load on shoulder under the condition of constant speed and a 78.2% reduction under the condition of variable speed,compared with the LOCKED case.

关键词： suspended backpack load carriage time-independent control

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Superpixel Edge Refinement and Differential Feature Enhancement for Transformer-Based Remote Sensing Image Change Detection

Superpixel Edge Refinement and Differential Feature Enhancem...

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Robotics, Intelligent control and Artificial intelligence (RICAI), International Conference on

作者： Jingjing Liang Xinlin Xie Tihao Cui Haoxue Zhang Shanxi Provincial Key Laboratory of Advanced Control and Industrial Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Change detection in remote sensing images is a challenging task, particularly when identifying small and complex objects such as buildings. The task is often hindered by issues such as high fragmentation rates, blurred edges, and elevated false and missed detection rates. These issues arise due to the inherent complexity of the scenes and the problem of class imbalance. To address these challenges, we propose a superpixel edge refinement and differential feature enhancement for Transformer-based change detection networks. First, a region-enhanced Transformer encoder is introduced, incorporating a superpixel edge refinement module. This module leverages pixel-level features extracted by the Transformer to generate superpixel feature embeddings, thereby enhancing local and edge information. Second, a differential feature enhancement module is designed to emphasize true change regions while suppressing pseudo-change areas. This is achieved through a dual-branch structure comprising feature enhancement and change extraction, further augmented by a saliency module. Finally, a Dice loss function is employed as an auxiliary loss term, complementing the cross-entropy loss in the construction of an edge-optimized loss function. The resulting loss function aims to balance overall detection accuracy with edge refinement. The proposed algorithm was evaluated on the widely used datasets LEVIR-CD and DSIFN-CD. Compared to existing algorithms, the proposed method effectively improved edge detail and overall consistency while maintaining detection accuracy. Additionally, false detections and missed detections were reduced.

关键词： Measurement Accuracy Change detection algorithms Image edge detection Buildings Feature extraction Transformers Object recognition Remote sensing Robots

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Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification

Knowledge-Data Fusion Based Source-Free Semi-Supervised Doma...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruimin Peng Jiayu An Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Electroencephalogram (EEG)-based seizure sub-type classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutuaISHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Diver-gence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification.

关键词： Training Adaptation models Data privacy Fuses Brain modeling Transformers Data models Electroencephalography Decision trees Cybernetics

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Hierarchical Spatio-temporal Decoupling for Text-to- Video Generation

Hierarchical Spatio-temporal Decoupling for Text-to- Video G...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiwu Qing Shiwei Zhang Jiayu Wang Xiang Wang Yujie Wei Yingya Zhang Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group Fudan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite diffusion models having shown powerful abilities to generate photorealistic images, generating videos that are realistic and diverse still remains in its infancy. One of the key reasons is that current methods intertwine spatial content and temporal dynamics together, leading to a notably increased complexity of text-to-video generation (T2V). In this work, we propose HiGen, a diffusion model-based method that improves performance by decoupling the spatial and temporal factors of videos from two perspectives, i.e., structure level and content level. At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser. Specifically, we generate spatially coherent priors using text during spatial reasoning and then generate temporally coherent motions from these priors during temporal reasoning. At the content level, we extract two subtle cues from the content of the input video that can express motion and appearance changes, respectively. These two cues then guide the model's training for generating videos, enabling flexible content variations and enhancing temporal stability. Through the decoupled paradigm, HiGen can effectively reduce the complexity of this task and generate realistic videos with semantics accuracy and motion stability. Extensive experiments demonstrate the superior performance of HiGen over the state-of-the-art T2V methods. We have released our source code and models.

关键词： Training Source coding Semantics Spatial coherence Cognition Stability analysis Complexity theory

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Transformer-Based Differential Superpixel Selection Network for Remote Sensing Images Change Detection

Transformer-Based Differential Superpixel Selection Network ...

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Artificial intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Tihao Cui Xinlin Xie Jingjing Liang Haoxue Zhang Shanxi Provincial Key Laboratory of Advanced Control and Industrial Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

In remote sensing images change detection, the results of Transformer often face the low precision and edge fitting problems, which are caused by the highly complex background of building categories and the imbalance of sample ratio. To address these challenges, we propose a Transformer-based differential superpixel selection network. First, a differential superpixel selection module is proposed which introduces difference selecting to refine the edge feature representation of the object region by using the boundary information of the selected superpixel. Second, we propose a multi-scale feature-aware enhancement module, which fuses multi-scale feature differences with the features extracted by the hierarchical Transformer encoder. This module enhances difference awareness while suppressing interference from complex backgrounds. Finally, we construct a composite loss function that combines the focal loss function with the binary cross-entropy loss function. The composite loss function can pay more attention to hardly classifying samples and suppress the influence of easily classifying samples. Our proposed algorithm is tested on the widely used LEVIR-CD and DSIFN-CD datasets. The results show that it effectively improved detection accuracy and edge fitting compared to existing methods.

关键词： Accuracy Sensitivity Image edge detection Fitting Buildings Interference Transformers Feature extraction Manufacturing Remote sensing

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Named Entity Recognition Based on Knowledge Fusion and Boundary Feature Sharing

Named Entity Recognition Based on Knowledge Fusion and Bound...

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Advanced Algorithms and control Engineering (ICAACE), International Conference on

作者： Anrui Li Housheng Su School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan P. R. China Key Laboratory of Image Processing and Intelligence Control of Ministry of Education Huazhong University of Science and Technology Wuhan P. R. China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Named entity recognition is a core task in the field of information extraction, which aims to recognize a relevant entity from unstructured text and classify that entity. Aiming at the problem that existing nested named entity recognition models cannot achieve better compatibility in recognizing long entities and the performance of the models, this study proposes a nested named entity recognition model based on knowledge fusion and boundary feature sharing. First, a joint training network based on the concepts of lexicon and knowledge graph is constructed to improve the text feature representation to obtain richer contextual information; second, the bimodal attention mechanism is used to capture the semantic correlation between lexical items and generate the spanning semantic representation matrix; furthermore, the boundary feature sharing strategy is designed to improve the generalization ability of the model for the rough transition problem of entity spanning boundaries. To verify the effectiveness of the model, it is tested on three benchmark datasets, including ACE2004, ACE2005, and SciERC datasets. The experimental results show that the proposed model outperforms the corresponding baseline model in terms of entity recognition F1 index.

关键词： Training Knowledge engineering Attention mechanisms Text recognition Semantics Named entity recognition Knowledge graphs Lexicon Information retrieval Context modeling

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TMMM: Transformer in Multimodal Sentiment Analysis under Missing Modalities

TMMM: Transformer in Multimodal Sentiment Analysis under Mis...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huanyu Wu Siyang Li Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The cross-task gap presents a significant challenge for multimodal models because of the differences in input-output workflows. For instance, multimodal pre-trained transformers may encounter uni-modal data during testing. To mitigate the gap, this paper introduces a Transformer in Multimodal Sentiment Analysis under Missing Modalities (TMMM) aims to perform well using missing-modal data during testing. TMMM uses a missing multimodal training approach to prevent accuracy degradation in testing. At the same time, a new network architecture allows the model to reconstruct missing modalities during testing. Classification token fusion and Mixture-of-Experts structures further enhance the model’s performance. A pre-training method utilizing contrastive learning, which can construct negative samples with positive samples, is proposed to overcome insufficient labeled data. Our experiments demonstrated the effectiveness of TMMM on two datasets with no modalities missing, i.e., it consistently achieved the highest classification accuracy and Macro-F1, which outperformed the best state-of-the-art baseline on each dataset by about 2% and 2.5%. Additionally, TMMM usually performs better than other baselines on datasets with missing modalities during testing.

关键词： Training Sentiment analysis Accuracy Contrastive learning Network architecture Transformers Brain modeling

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