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A Constant-Force End-Effector With Online Force Adjustment for Robotic Ultrasonography

IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 2547-2554页

作者： Bao, X. Wang, S. Housden, R. Hajnal, J. Rhode, K. Kings Coll London Kings Hlth Partners St Thomas Hosp Sch Biomed Engn & Imaging Sci London SE1 7EH England Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this letter, we propose a novel constant-force end-effector (CFEE) to address current limitations in robotic ultrasonography. The CFEE uses a parallel, motor-spring-based solution to precisely generate constant operating forces over a wide range and enable the ultrasound (US) probe to adapt to the abdominal contours autonomously. A displacement measurement unit was developed to realize the acquisition of probe position and precise control of the operating force. Moreover, the operating force can be adjusted online to maintain safety and continuity of operation. Simulations and experiments were carried out to evaluate the performance. Results show that the proposed CFEE can provide constant forces of 4-12 N with displacements of 0-8 mm. The maximum relative error of force generation is 8.28%, and the accuracy and precision for displacement measurement are 0.29 mm and +/- 0.16 mm, respectively. Various operating forces can be adjusted online during the same operation. Ultrasound images acquired by the proposed CFEE are of equally good quality compared to a manual sonographer scan. The proposed CFEE would have potential further medical applications.

关键词： Robotic ultrasonography online force adjustment constant force end-effector

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An Optimal control Based Trajectory Planning Method for Cooperative Merging Maneuvers

An Optimal Control Based Trajectory Planning Method for Coop...

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Intelligent Robotics and systems (ISoIRS), International Symposium on

作者： Qi Wang Hao Ge Xuting Duan Jianshan Zhou State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control State Key Laboratory of Air Traffic Management System and Technology School of Transportation Science and Engineering Beihang University Beijing China State Key Laboratory of Air Traffic Management System and Technology Nanjing Research Institute of Electronic Engineering Nanjing China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China

Due to the prediction errors and diverse driving styles of surrounding vehicles, traffic congestion and even accidents can occur in the traffic bottleneck area where vehicles merge into the main road. To ensure that connected and automated vehicles (CAVs) can quickly and efficiently merge into the main road, this paper proposes a cooperative trajectory planning method based on optimal control for vehicle cut-in scenarios. A optimal control model is constructed, and a quintic polynomial is used to generate the initial solution of the optimal control problem to accelerate the solution process and improve the success rate. Finally, The obtained merging trajectory is then synchronized with the movement of vehicles on the main road to optimize their actions and ensure a safe merging process. Joint simulation with Robot Operating System (ROS) and RVIZ is utilized, and the results show that the proposed algorithm can quickly obtain cooperative results, the locally successful merging trajectory meets the requirements of smoothness and safety, and if the solution does not allow merging, the merging vehicle is instructed to terminate the execution.

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Dissecting and Mitigating Semantic Discrepancy in Stable Diffusion for Image-to-Image Translation

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 705-718页

作者： Yuan, Yifan Yang, Guanqun Wang, James Z. Zhang, Hui Shan, Hongming Wang, Fei-Yue Zhang, Junping Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Fuxi Team NetEase Corp Hangzhou Peoples R China Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100098 Peoples R China Fudan Univ Inst Sci & Technol Brain inspired Intelligence Shanghai 200433 Peoples R China Fudan Univ MOE Frontiers Ctr Brain Sci Shanghai 200433 Peoples R China Shanghai Ctr Brain Sci & Brain inspired Technol Shanghai 200031 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

Finding suitable initial noise that retains the original image's information is crucial for image-to-image (I2I) translation using text-to-image (T2I) diffusion models. A common approach is to add random noise directly to the original image, as in SDEdit. However, we have observed that this can result in "semantic discrepancy" issues, wherein T2I diffusion models mis-interpret the semantic relationships and generate content not present in the original image. We identify that the noise introduced by SDEdit disrupts the semantic integrity of the image, leading to unintended associations between unrelated regions after U-Net upsampling. Building on the widely-used latent diffusion model, Stable Diffusion, we propose a training-free, plug-and-play method to alleviate semantic discrepancy and enhance the fidelity of the translated image. By leveraging the deterministic nature of denoising diffusion implicit models (DDIMs) inversion, we correct the erroneous features and correlations from the original generative process with accurate ones from DDIM inversion. This approach alleviates semantic discrepancy and surpasses recent DDIM-inversion-based methods such as PnP with fewer priors, achieving a speedup of 11.2 times in experiments conducted on COCO, ImageNet, and ImageNet-R datasets across multiple I2I translation tasks.

关键词： Translation Correlation Codes Image edge detection Semantics Noise Noise reduction Text to image Diffusion models Erosion DDIM inversion diffusion model image-to-image translation semantic discrepancy stable diffusion

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Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第5期10卷 2312-2324页

作者： Xia, Kun Duch, Wlodzislaw Sun, Yu Xu, Kedi Fang, Weili Luo, Hanbin Zhang, Yi Sang, Dong Xu, Xiaodong Wang, Fei-Yue Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Nicolaus Copernicus Univ Ctr Modern Interdisciplinary Technol Sch Phys Astron & Informat Dept InformatNeurocognit Lab PL-87100 Torun Poland Zhejiang Univ Key Lab Biomed Engn Minist Educ Hangzhou 310027 Peoples R China Zhejiang Univ Natl Clin Res Ctr Child Hlth Sch Med Hangzhou 310027 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China Zhejiang Univ Qiushi Acad Adv Studies QAAS Dept Biomed Engn Hangzhou 310027 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Coll Publ Adm Wuhan 430074 Peoples R China NetsUn Clearing Corp Beijing 100045 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100049 Peoples R China

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, and so on. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence, privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This article fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs.

关键词： Electroencephalography Privacy Data privacy Computational modeling Data models Brain modeling Feature extraction Brain-computer interfaces (BCIs) electroencephalogram (EEG) machine learning (ML) privacy

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Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 673-683页

作者： Teng, Siyu Chen, Long Ai, Yunfeng Zhou, Yuanye Zhe Xuanyuan Hu, Xuemin BNU HKBU United Int Coll Zhuhai 519087 Peoples R China Hong Kong Baptist Univ Kowloon Hong Kong 999077 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Waytous Inc Qingdao Qingdao 266109 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Malardalen Univ S-72214 Vasteras Sweden Hubei Univ Sch Comp Sci & Informat Engn Wuhan 430062 Peoples R China

End-to-end autonomous driving provides a simple and efficient framework for autonomous driving systems, which can directly obtain control commands from raw perception data. However, it fails to address stability and interpretability problems in complex urban scenarios. In this paper, we construct a two-stage end-to-end autonomous driving model for complex urban scenarios, named HIIL (Hierarchical Interpretable Imitation Learning), which integrates interpretable BEV mask and steering angle to solve the problems shown above. In Stage One, we propose a pretrained Bird's Eye View (BEV) model which leverages a BEV mask to present an interpretation of the surrounding environment. In Stage Two, we construct an Interpretable Imitation Learning (IIL) model that fuses BEV latent feature from Stage One with an additional steering angle from Pure-Pursuit (PP) algorithm. In the HIIL model, visual information is converted to semantic images by the semantic segmentation network, and the semantic images are encoded to extract the BEV latent feature, which are decoded to predict BEV masks and fed to the IIL as perception data. In this way, the BEV latent feature bridges the BEV and IIL models. Visual information can be supplemented by the calculated steering angle for PP algorithm, speed vector, and location information, thus it could have better performance in complex and terrible scenarios. Our HIIL model meets an urgent requirement for interpretability and robustness of autonomous driving. We validate the proposed model in the CARLA simulator with extensive experiments which show remarkable interpretability, generalization, and robustness capability in unknown scenarios for navigation tasks.

关键词： Semantics Data models Autonomous vehicles Cameras Reinforcement learning Predictive models Robustness Autonomous driving imitation learning motion planning end-to-End driving interpretability

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A Novel Coiled Cable-conduit-driven Hyper-redundant Manipulator for Remote Operating in Narrow Spaces

A Novel Coiled Cable-conduit-driven Hyper-redundant Manipula...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Luo, Mingrui Tian, Yunong Li, En Chen, Minghao Kang, Cunfeng Yang, Guodong Tan, Min Univ Chinese Acad Sci Sch Artificial Intelligence 19A Yuquan Rd Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Engn Lab Ind Vis & Intelligent Equipment Technol 95 Zhongguancun East Rd Beijing 100190 Peoples R China Beijing Univ Technol Beijing 100124 Peoples R China

ISBN: (纸本)9781665491907

Operating in narrow spaces is an important challenge in the development of robots. Redundant manipulators are one way to solve this problem, but their mechanism design and control method still have much room for improvement. In this paper, we propose a coiled cable-conduit-driven hyper-redundant manipulator (C-CDHRM) with great slenderness and flexibility. In terms of mechanism design, it considers both compactness and operability. By imitating the structure and behavior of a constricting snake, it can be uncoiled sequentially from a coiled storage state, led by the head. In terms of control methods, we propose a multi-layer control system that can make remote operations more accurate and reliable. On the one hand, guiding, segmenting, and following the path overcome the planning ambiguity caused by redundancy. On the other hand, conduit transmission modeling and cable length correction overcome the nonlinear mapping of cable-driven joints and were verified in experiments. Through tests, the mobile integrated system composed of C-CDHRM has an excellent performance in operation precision and accuracy, ensuring safety and accessibility in narrow spaces. Finally, in field experiments, the inspection and cleaning of various types of electrical equipment have been successfully completed, showing excellent application prospects.

关键词： Cables

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HackGAN: Harmonious Cross-Network Mapping Using CycleGAN With Wasserstein-Procrustes Learning for Unsupervised Network Alignment

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2023年第2期10卷 746-759页

作者： Yang, Linyao Wang, Xiao Zhang, Jun Yang, Jun Xu, Yancai Hou, Jiachen Xin, Kejun Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266200 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Nanjing Joinmap Data Res Inst Nanjing 211100 Peoples R China

Network alignment (NA) that identifies equivalent nodes across networks is an effective tool for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter-network node similarities based on labeled anchor links, which are costly, time-consuming, and difficult to acquire. Therefore, a few unsupervised network alignment (UNA) methods propose solving NA problems without anchor links. However, most existing UNA methods rely on discriminative attributes to capture nodes' similarities and are hard to obtain optimal one-to-one alignments. Toward these issues, this article proposes a novel method named HackGAN to solve the UNA problem solely based on the structural information. Specifically, HackGAN represents nodes with embeddings based on an unsupervised graph neural network (GNN) to capture their global and local structural features. After that, it initializes mapping functions to transform the embedding spaces of different networks into the same vector space by iteratively solving the Wasserstein-Procrustes problem. The mapping functions are then refined by an adversarial model with cycle-consistency and Sinkhorn distance losses to obtain optimized one-to-one mappings. Based on the distances between mapped embeddings, accurate and robust results are obtained with a collective alignment algorithm. Experimental comparisons on both synthetic and real-world datasets demonstrate the superiority of HackGAN.

关键词： Task analysis Optimization Generative adversarial networks Computational modeling Automation Training Standards Embedding generative adversarial network network alignment (NA) optimal transport unsupervised learning

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Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?

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JOURNAL OF DIGITAL IMAGING 2022年第5期35卷 1079-1090页

作者： Hu, Shasha Zhu, Yongbei Dong, Di Wang, Bei Zhou, Zuofu Wang, Chi Tian, Jie Peng, Yun Capital Med Univ Beijing Childrens Hosp Natl Ctr Children Hlth Dept Radiol Beijing 100045 Peoples R China Chinese Acad Sci Beijing Key Lab Mol Imaging Inst Automat State Key Lab Management & Control Complex Syst CAS Key Lab Mol Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing 100191 Peoples R China Fujian Med Univ Fujian Prov Matern & Childrens Hosp Dept Radiol Fuzhou 350000 Peoples R China

Clinical symptoms and inflammatory markers cannot reliably distinguish the etiology of CAP, and chest radiographs have abundant information related with CAP. Hence, we developed a context-fusion convolution neural network (CNN) to explore the application of chest radiographs to distinguish the etiology of CAP in children. This retrospective study included 1769 cases of pediatric pneumonia (viral pneumonia, n = 487;bacterial pneumonia, n = 496;and mycoplasma pneumonia, n = 786). The chest radiographs of the first examination, C-reactive protein (CRP), and white blood cell (WBC) were collected for analysis. All patients were stochastically divided into training, validation, and test cohorts in a 7:1:2 ratio. Automatic lung segmentation and hand-crafted pneumonia lesion segmentation were performed, from which three image-based models including a full-lung model, a local-lesion model, and a context-fusion model were built;two clinical characteristics were used to build a clinical model, while a logistic regression model combined the best CNN model and two clinical characteristics. Our experiments showed that the context-fusion model which integrated the features of the full-lung and local-lesion had better performance than the full-lung model and local-lesion model. The context-fusion model had area under curves of 0.86, 0.88, and 0.93 in identifying viral, bacterial, and mycoplasma pneumonia on the test cohort respectively. The addition of clinical characteristics to the context-fusion model obtained slight improvement. Mycoplasma pneumonia was more easily identified compared with the other two types. Using chest radiographs, we developed a context-fusion CNN model with good performance for noninvasively diagnosing the etiology of community-acquired pneumonia in children, which would help improve early diagnosis and treatment.

关键词： Chest radiographs Convolution neural network Community-acquired pneumonia Pediatric Etiology

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The preparation of SiC ceramic photosensitive slurry for rapid stereolithography

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JOURNAL OF THE EUROPEAN CERAMIC SOCIETY 2021年第15期41卷 7516-7524页

作者： Tang, Jie Guo, Xiaotian Chang, Haotian Hu, Kehui Shen, Zhen Wang, Weixing Liu, Meng Wei, Yuquan Huang, Zhengren Yang, Yong Chinese Acad Sci Shanghai Inst Ceram State Key Lab High Performance Ceram & Superfine Shanghai 200050 Peoples R China Univ Chinese Acad Sci Coll Mat Sci & Optoelect Technol Beijing 100049 Peoples R China Tsinghua Univ State Key Lab Tribol Beijing 100084 Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Stereolithography is one of the most widely used additive manufacturing techniques for preparing high precision and complex ceramic components. Due to the high optical absorbance and refractive index of SiC powder, the rapid stereolithography of SiC ceramics components has become a key challenge. Here, we innovatively use graded silica to improve the curing thickness, rheological and settling performance of the slurry. And we presented a preparation method of SiC ceramic slurry for stereolithography with high solid content, low viscosity, low sedimentation rate and high curing thickness. The printable precision of the slurry is more than 75 mu m, the dynamic viscosity is less than 2 Pa.s, and the 24 h sedimentation height is less than 5%. This strategy demonstrates a tantalizing possibility and promising prospect to rapid stereolithography of large size SiC ceramic green body.

关键词： Stereolithography Graded SiO2 3D printing SiC ceramic slurry

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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks ⁎

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IFAC-PapersOnLine 2024年第10期58卷 176-181页

作者： Yi Li Shuangshuang Han Yongqiang Bai Fei-Yue Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Key Laboratory of Ocean Observation Technology MNR Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Beijing Huairou Academy of Parallel Sensing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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