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A decouple-decomposition noise analysis model for closed-loop mode-localized tilt sensors

Microsystems & Nanoengineering 2023年第6期9卷 297-307页

作者： kunfeng wang XingYin Xiong Zheng wang Liangbo Ma BoWen wang WuHao Yang Xiaorui Bie ZhiTian Li XuDong Zou The State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCAUSA Shangdong Key Laboratory of Low-altitude Airspace Surveillance Network Technology QiLu Aerospace Information Research InstituteJinanChina

The development of mode-localized sensors based on amplitude output metrics has attracted increasing attention in recent years due to the potential of such sensors for high sensitivity and resolution.Mode-localization phenomena leverage the interaction between multiple coupled resonant modes to achieve enhanced performance,providing a promising solution to overcome the limitations of traditional sensing technologies.Amplitude noise plays a key role in determining the resolution of mode-localized sensors,as the output metric is derived from the measured AR(amplitude ratio)within the weakly coupled resonator system.However,the amplitude noise originating from the weakly coupled resonator's closed-loop circuit has not yet been fully investigated.This paper presents a decouple-decomposition(DD)noise analysis model,which is applied to achieve high resolution in a mode-localized tilt sensor based on a weakly coupled resonator closed-loop circuit.The DD noise model separates the weakly coupled resonators using the decoupling method considering the nonlinearity of the resonators.By integrating the decoupled weakly coupled resonators,the model decomposes the weakly coupled resonator's closed-loop circuit into distinct paths for amplitude and phase noise analyses.The DD noise model reveals noise effects at various circuit nodes and models the system noise in the closed-loop circuit of the weakly coupled resonators.MATLAB/Simulink simulations verify the model's accuracy when compared to theoretical analysis.At the optimal operating point,the mode-localized tilt sensor achieves an input-referred instability of 3.91×10^(-4°)and an input-referred AR of PSD of 2.01×10^(-4°)⁄√Hz using the closed-loop noise model.This model is also applicable to other varieties of mode-localized sensors.

关键词： weakly couple overcome

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A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1361-1370页

作者： Xuesong Li Yating Liu kunfeng wang Fei-Yue wang State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the University of Chinese Academy of SciencesBeijing 100049China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The movement of pedestrians involves temporal continuity,spatial interactivity,and random diversity.As a result,pedestrian trajectory prediction is rather challenging.Most existing trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many assumptions.In this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian trajectories.The RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling module.The temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different pedestrians.The spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current time.The randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random noise.We conduct extensive experiments on several public datasets.The results demonstrate that our method outperforms many that are state-ofthe-art.

关键词： Deep learning long short-term memory(LSTM) recurrent attention and interaction(RAI)model trajectory prediction

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Training and Testing Object Detectors With Virtual Images

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 539-546页

作者： Yonglin Tian Xuan Li kunfeng wang Fei-Yue wang Department of Automation University of Science and Technology of China Hefei 230027 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijirtg 100190 China School of Automation Beijing Institute of Technology Beijing 100081. China Qingdao Academy of Intelligent Industries Qingdao 266000 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha 410073 China IEEE

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.

关键词： Deep learning object detection parallel vision virtual dataset

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基于偶极子天线辐射反向创建空间球形聚焦光斑

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光学学报 2024年第10期44卷 428-435页

作者：王俊杰杨艳芳何英李琦王昆峰上海大学理学院物理系上海200444

与传统的仅能生成光轴方向的球形聚焦光斑不同,本文提出了一种在4Pi聚焦系统中通过反聚焦偶极子天线辐射场产生具有规定空间方向和间距的球形聚焦光斑的方法。该方法是将规定好长度和极化方向的空间偶极子天线置于4Pi聚集系统的焦点处,... 详细信息

与传统的仅能生成光轴方向的球形聚焦光斑不同,本文提出了一种在4Pi聚焦系统中通过反聚焦偶极子天线辐射场产生具有规定空间方向和间距的球形聚焦光斑的方法。该方法是将规定好长度和极化方向的空间偶极子天线置于4Pi聚集系统的焦点处,通过逆问题解析求解出生成球形聚焦光斑的物镜瞳孔面上的输入场。然后使用瞳孔面处的场,并选择合适的偶极子天线长度,就能获得球形聚焦光斑。数值结果表明,创建的球形聚焦光斑的空间方向与设置的偶极子天线的极化方向一致,球形聚焦光斑之间的距离也等于偶极子天线的长度。本文提出的方法比传统方法更灵活,可以创建具有规定空间位置的球形聚焦光斑,这对空间任意位置捕获纳米粒子具有很大的应用价值。

关键词：物理光学球形聚焦光斑偶极子天线 4Pi聚焦系统

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Prediction of methane storage in covalent organic frameworks using big-data-mining approach

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Chinese Journal of Chemical Engineering 2021年第11期34卷 286-296页

作者： Huan Zhang Peisong Yang Duli Yu kunfeng wang Qingyuan Yang State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical TechnologyBeijing 100029China Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical TechnologyBeijing 100029China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China

A combination of computational materials screening and machine learning(ML)technique is being adopted as a popular approach to study various materials toward application of interest.In this work,we began with high-throughput molecular simulations to calculate the methane storage(6.5 MPa)and deliverable(6.5-0.58 MPa)capacities of 404,460 covalent organic frameworks(COFs)at 298 K.Then,the full data sets with 23 features were randomly split into training and test sets in a ratio of 20:80,which were applied to evaluate the prediction abilities of several ML algorithms,including gradient boosting decision tree(GBDT),neural network(NN),support vector machine(SVM),random forest(RF)and decision tree(DT).The results indicate that the RF model has the highest prediction accuracy,which was further employed to reduce the dimension of features space and quantitatively analyze the relative importance of each feature value.The binary classification predictors built using the features with the highest influence weight can give a successful identification of top-performing candidates from the test set containing 323,168 COFs with an accuracy exceeding 96%.The deliverable capacities of the identified COFs were found to outperform those reported so far for various adsorbents.The findings may provide a useful guidance for the design and synthesis of new high-performance materials for methane storage application.

关键词： Covalent organic framework Monte Carlo simulation Methane Machine learning Model

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Generative Adversarial Networks:Introduction and Outlook

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 588-598页

作者： kunfeng wang Chao Gou Yanjie Duan Yilun Lin Xinhu Zheng Fei-Yue wang IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Department of Computer Science and Engineering University of Minnesota Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea.The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution.Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application fields.Then, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

关键词： ACP approach adversarial learning generative adversarial networks(GANs) generative models parallel intelligence zero-sum game

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自适应视觉惯性地磁紧耦合定位系统

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光学精密工程 2024年第7期32卷 1023-1033页

作者：付鹏万振华王琨锋赵开春清华大学精密仪器系北京100084 北京信息科技大学仪器科学与光电工程学院北京100192 广西大学机械工程学院广西南宁530004

为了解决视觉惯性组合导航系统绝对航向信息缺失与姿态发散的问题,进一步提升系统定位精度,设计并实现了针对未知磁场环境下的自适应视觉惯性地磁紧耦合定位系统。基于常用的三轴磁力计内外参数联合标定原理,提出了一种地磁信息全局及... 详细信息

为了解决视觉惯性组合导航系统绝对航向信息缺失与姿态发散的问题,进一步提升系统定位精度,设计并实现了针对未知磁场环境下的自适应视觉惯性地磁紧耦合定位系统。基于常用的三轴磁力计内外参数联合标定原理,提出了一种地磁信息全局及帧间约束残差构建方法。通过磁强度差异动态调整融合权重,利用非线性优化方法设计视觉/惯性/地磁紧耦合系统实现自身运动状态估计。最后,在校园环境下开展户外实验。实验结果表明:该系统能够在部分建筑及车辆磁场干扰环境下稳定运行,定位精度均优于0.8%(RMSE),相比于VINS位置误差平均降低了约24%,且具有良好的实时性。磁力计及自适应融合方法的引入可以有效改善现有视觉惯性导航系统的定位性能,为无人系统提供高可靠性的实时定位结果。

关键词：组合导航地磁场自适应系统非线性优化

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基于GSN的高速智能领航横向功能安全需求分解

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自动化与仪器仪表 2024年第3期 1-5,25页

作者：刘海艳王亚东王坤峰北京化工大学信息科学与技术学院北京100029

针对避免车辆高速智能领航HNP功能控制车辆非预期的侧向移动这个横向功能安全目标,采用目标结构表示法(goal structuring notation,GSN)对该安全目标进行拆解来推导功能安全需求。首先,采用GSN工具推导出支撑本条安全目标的安全策略;其... 详细信息

针对避免车辆高速智能领航HNP功能控制车辆非预期的侧向移动这个横向功能安全目标,采用目标结构表示法(goal structuring notation,GSN)对该安全目标进行拆解来推导功能安全需求。首先,采用GSN工具推导出支撑本条安全目标的安全策略;其次利用泳道图展示了安全策略在架构要素上的分配以及这些安全策略需要由哪些具体的要素来实现;最后,对不同架构要素包括执行器EPS、摄像头、毫米波雷达、ADS域控制器的安全需求作了描述。

关键词：高速智能领航功能安全 GSN 自动驾驶智能网联汽车

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具身智能驾驶：概念、方法、现状与展望

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智能科学与技术学报 2024年第1期6卷 17-32页

作者：沈甜雨李志伟范丽丽张庭祯唐丹丹周美华刘华平王坤峰北京化工大学信息科学与技术学院北京理工大学信息与电子学院燕山大学信息科学与工程学院北京同仁医院眼科研究所清华大学计算机科学与技术系

具身智能突破了传统人工智能的界限，强调了机器与物理世界交互的重要性，旨在通过促进软硬件一体化智能体的环境适应性学习和智能行为的演化，解决更多智能系统在现实应用中的问题。在这一理念的启发下，提出了具身智能驾驶的概念与框... 详细信息

具身智能突破了传统人工智能的界限，强调了机器与物理世界交互的重要性，旨在通过促进软硬件一体化智能体的环境适应性学习和智能行为的演化，解决更多智能系统在现实应用中的问题。在这一理念的启发下，提出了具身智能驾驶的概念与框架，将具身智能思想融入自动驾驶汽车的开发与应用中，通过物理智能体、虚拟智能体和真实交通场景之间的持续交互，实现智能系统在复杂交通场景中的精准感知、高效执行、自主进化，提升自动驾驶车辆对复杂开放交通环境的长期适应能力。基于提出的具身智能驾驶框架，进一步总结了其中涉及的核心技术，并剖析了相关技术的发展现状与存在问题。此外，通过探索虚实互动数据智能、基础模型与基础智能、持续学习与平行智能在其中的重要作用和应用潜力，提出了对这一领域的思考与展望。旨在推进具身智能驾驶的系列创新性研究和自动驾驶技术在更广泛场景的应用落地，并为智能车辆等移动机器人系统的发展，提供新的思路和解决方案。

关键词：具身智能自动驾驶具身感知具身执行具身进化

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An Advanced Image Processing Technique for Backscatter-Electron Data by Scanning Electron Microscopy for Microscale Rock Exploration

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Journal of Earth Science 2024年第1期35卷 301-305页

作者： Zhaoliang Hou kunfeng Qiu Tong Zhou Yiwei Cai Frontiers Science Center for Deep-time Digital Earth China University of GeosciencesBeijing 100083China Department of Geology University of ViennaVienna 1090Austria State Key Laboratory of Geological Processes and Mineral Resources School of Earth Sciences and ResourcesChinaUniversity of GeosciencesBeijing 100083China

Backscatter electron analysis from scanning electron microscopes(BSE-SEM)produces high-resolution image data of both rock samples and thin-sections,showing detailed structural and geochemical(mineralogical)information.This allows an in-depth exploration of the rock microstructures and the coupled chemical characteristics in the BSE-SEM image to be made using image processing techniques.Although image processing is a powerful tool for revealing the more subtle data“hidden”in a picture,it is not a commonly employed method in geoscientific microstructural analysis.Here,we briefly introduce the general principles of image processing,and further discuss its application in studying rock microstructures using BSE-SEM image data.

关键词： Image processing rock microstructures electron-based imaging data mining

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