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Design and Force Control Analysis of a Novel Waist Massage Instrument

Recent Patents on Mechanical engineering 2023年第4期16卷 296-307页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Wan, Yanjian Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China School of Mechanical Engineering Anhui University of Technology Anhui Ma’anshan243000 China

Background: There is a shortage of rehabilitation doctors and treatment equipment for patients with lumbar spondylosis. Therefore, a waist massage instrument (WMI) is designed to help patients alle-viate lumbar pain. Objective: The objective of this study is to design a new WMI with the advantages of simple structure, lightweight, and function of effective force control, and to verify its function of palmar push, palmar press, and palmar vibration based on the on-off control, PID control, and fuzzy-PID control. Methods: Firstly, the movement characteristics of the Chinese massage manipulation are analyzed to clarify the design principle of the massage instrument. Then, a new WMI composed of massage head, vibration exciter, and actuator is designed to simulate the three massage manipulations. Next, the electric control system and control methods of on-off, PID, and fuzzy-PID controllers are introduced to realize the force control of WMI. Finally, the force control experiments of WMI are carried out to verify the function of the palmar press, palmar push, and palmar vibration based on the on-off control, PID control, and fuzzy-PID control, respectively. Results: The experimental results indicate that the proposed WMI can implement the massage manipulations of palmar push, palmar press, and palmar vibration well, and has good force control ability after using fuzzy-PID control. Conclusion: The newly designed WMI is effective in providing palmar push, palmar press, and palmar vibration massage manipulations for patients with lumbar spondylosis, and the fuzzy-PID controller im-proves the force control ability of the instrument. The WMI has the potential to be used as an effective treatment for lumbar pain. © 2023 Bentham Science Publishers.

关键词： Three term control systems

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Three-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning

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International Journal of Ophthalmology(English edition) 2022年第3期15卷 495-501页

作者： Jing-Jing Xu Yang Zhou Qi-Jie Wei Kang Li Zhen-Ping Li Tian Yu Jian-Chun Zhao Da-Yong Ding Xi-Rong Li Guang-Zhi Wang Hong Dai School of Medicine Tsinghua UniversityBeijing 100084China Visionary Intelligence Company Limited Beijing 100872China Department of Ophthalmology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical ScienciesBeijing 100730China Key Lab of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijing 100872China

AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema(DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness(CRT) and apply it in follow-up of DME ***: Optical coherence tomography(OCT) scans of 229 eyes from 160 patients were *** manually annotated cystoid macular edema(CME), subretinal fluid(SRF) and fovea as ground *** convolution neural networks(DCNNs) were constructed including U-Net, sASPP, HRNetV2-W48, and HRNetV2-W48+Object-Contextual Representation(OCR) for fluid(CME+SRF) segmentation and fovea detection respectively, based on which the thickness maps of CME, SRF and retina were generated and divided by Early Treatment Diabetic Retinopathy Study(ETDRS) ***: In fluid segmentation, with the best DCNN constructed and loss function, the dice similarity coefficients(DSC) of segmentation reached 0.78(CME), 0.82(SRF), and 0.95(retina).In fovea detection, the average deviation between the predicted fovea and the ground truth reached 145.7±117.8 μ*** generated macular edema thickness maps are able to discover center-involved DME by intuitive morphometry and fluid volume, which is ignored by the traditional definition of CRT>250 μ*** maps could also help to discover fluid above or below the fovea center ignored or underestimated by a single OCT ***: Compared to the traditional unidimensional indicator-CRT, 3D macular edema thickness maps are able to display more intuitive morphometry and detailed statistics of DME, supporting more accurate diagnoses and follow-up of DME patients.

关键词： diabetic macular edema fluid segmentation fovea detection 3D macular edema thickness maps deep learning

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Structure-Based Uncertainty Estimation for Source-Free Active Domain Adaptation

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IET Computer Vision 2025年第1期19卷

作者： Ouyang, Jihong Zhang, Zhengjie Meng, Qingyi Chi, Jinjin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Active domain adaptation (active DA) provides an effective solution by selectively labelling a limited number of target samples to significantly enhance adaptation performance. However, existing active DA methods often struggle in real-world scenarios where, due to data privacy concerns, only a pre-trained source model is available, rather than the source samples. To address this issue, we propose a novel method called the structure-based uncertainty estimation model (SUEM) for source-free active domain adaptation (SFADA). To be specific, we introduce an innovative active sample selection strategy that combines both uncertainty and diversity sampling to identify the most informative samples. We assess the uncertainty in target samples using structure-wise probabilities and implement a diversity selection method to minimise redundancy. For the selected samples, we not only apply standard-supervised loss but also conduct interpolation consistency training to further explore the structural information of the target domain. Extensive experiments across four widely used datasets demonstrate that our method is comparable to or outperforms current UDA and active DA methods. © 2025 The Author(s). IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： computer vision convolutional neural nets image classification

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Recognition of field roads based on improved U-Net++Network

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International Journal of Agricultural and Biological engineering 2023年第2期16卷 171-178页

作者： Lili Yang Yuanbo Li Mengshuai Chang Yuanyuan Xu Bingbing Hu Xinxin Wang Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Kunlun Beidou Intelligence Technologies Co.Ltd. Beijing 102200China

Unmanned driving of agricultural machinery has garnered significant attention in recent years,especially with the development of precision farming and sensor *** achieve high performance and low cost,perception tasks are of great *** this study,a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural *** approach of this study utilized point clouds,with low-resolution Lidar point clouds as inputs,generating high-resolution point clouds and Bird's Eye View(BEV)images that were encoded with several basic *** a BEV representation,road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++neural *** enhancements were proposed for U-Net++:1)replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block(ACBlock);2)adding a multi-branch Asymmetric Dilated Convolutional Block(MADC)in the highest semantic information layer;3)adding an Attention Gate(AG)model to the long-skip-connection in the decoding *** results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds,which was 7.35 percentage points higher than U-Net++.Furthermore,the average processing time of the model was about 70 ms,meeting the time requirements of unmanned driving in agricultural *** proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.

关键词： image segmentation unmanned agricultural machinery field roads point cloud super-resolution point cloud bird’s eye view

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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Leveraging differentiable NAS and abstract genetic algorithms for optimizing on-mobile VSR performance

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Machine Learning 2025年第7期114卷 1-23页

作者： Liu, Xuncheng Zhang, Weizhan Gong, Tieliang Yan, Caixia Li, Rui School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China

The proliferation of mobile video applications has for real-time Video Super-Resolution (VSR) technologies, necessitating architectures that simultaneously achieve superior fidelity and ultra-low latency. Existing Neural Architecture Search (NAS) methods achieve efficient performance within fixed topologies and feature dimensionalities, but inherently struggle to meet the flexible design demands of lightweight on-mobile architectures. When applied to on-mobile VSR, their insufficient exploration under real-time constraints results in suboptimal performance, exposing a critical gap in current approaches. To address these challenges, we propose a novel bilevel NAS framework designed to systematically explore diverse topological configurations and feature dimensionalities within an expanded search space. Our framework incorporates a collection of independent differentiable search spaces. At the upper level, we employ an Abstract Genetic Algorithm (AGA) to explore a broad spectrum of topological variations and feature dimensionality configurations. At the lower level, we introduce a latency-aware differentiable NAS method, which facilitates efficient fidelity-latency optimization within each differentiable search space. Additionally, we provide theoretical convergence guarantees for our bilevel NAS framework, ensuring the discovery of optimal architectures. Experimental validation demonstrates that our method achieves state-of-the-art performance, delivering a 5.78ms inference latency while significantly improving fidelity metrics across standard benchmarks. The proposed framework not only breaks the fidelity-latency trade-off but also demonstrates remarkable efficiency in early AGA generations, reducing architecture search iterations from 100 to several generations while maintaining superior results. Our implementation is available at https://***/liuxunchenglxc/Reducio .

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Research on Destination Tourism data Based on CNN-BiLSTM and STL-LSTM Methods for data Continuity Problem

Research on Destination Tourism Data Based on CNN-BiLSTM and...

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2024 International Conference on Culture-Oriented Science and Technology, CoST 2024

作者： Lei, Zhengguang Zhao, Wenqing Li, Caihong Yang, Yi Wei, Xingbao Dunhuang Academy Dunhuang736200 China Lanzhou University School of Information Science and Engineering Lanzhou730000 China Ministry of Culture and Tourism Key Laboratory of Tourism Information Fusion Processing and Data Ownership Protection Lanzhou730000 China

ISBN: (纸本)9798350380347

This study addresses challenges in sentiment classification accuracy and time series forecasting precision by analyzing visitor reviews and flow data for the Dunhuang Mogao Grottoes. Using a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model for sentiment analysis, the study demonstrated superior performance in classifying positive reviews compared to traditional machine learning and Text Convolutional Neural Network (TextCNN) models, despite slightly lower recall and accuracy for negative reviews. Time series forecasting with the Seasonal-Trend Decomposition using Loess-Long Short-Term Memory (STLLSTM) model exhibited higher accuracy for short periods (e.g., 2 days), with an average MAE of 35.71, RMSE of 59.02, and R2 of 0.93, though accuracy decreased over longer periods (e.g., 30 days). This research provides insights into visitor sentiments and patterns, offering management and strategy recommendations to enhance visitor experiences. Future studies should refine these models to improve accuracy and efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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NcoVis: A Visual Analysis Framework for Exploring Academic Collaboration Networks under New Collaborative Relationships 25

NcoVis: A Visual Analysis Framework for Exploring Academic C...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with data Privacy Protection

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Computer Modeling in engineering & Sciences 2023年第10期137卷 717-732页

作者： You Lu Linqian Cui YunzheWang Jiacheng Sun Lanhui Liu School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Most studies have conducted experiments on predicting energy consumption by integrating data formodel ***, the process of centralizing data can cause problems of data ***,many laws and regulationson data security and privacy have been enacted, making it difficult to centralize data, which can lead to a datasilo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning ***, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg)method is used to directly weight the model parameters on average, which may have an adverse effect on te ***, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple userscollaboratively training the model. Each household trains a local model on local data. These local data neverleave the local area, and only the encrypted parameters are uploaded to the central server to participate in thesecure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assignweights to each locally uploaded local model. And the model has improved predictive performance. We validatethe performance of the FedRL model by testing it on a real-world dataset and compare the experimental results withother models. The performance of our proposed method in most of the evaluation metrics is improved comparedto both the centralized and distributed models.

关键词： Energy consumption forecasting federated learning data privacy protection Q-learning

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RAF-Unet: A Remote Sensing Identification Method for Forest Land Information with Modified Unet

RAF-Unet: A Remote Sensing Identification Method for Forest ...

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2024 International Conference on Computational Intelligence and Communication System, CCICS 2024

作者： Wang, Zhiyuan Chen, Lingyan Shen, Wanling Xiao, Jiawen Xu, Zhanghua Liu, Jinfu Forestry College Fujian Agriculture and Forestry University Fuzhou China College of Environment and Safety Engineering Academy of Geography and Ecological Environment Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University Fuzhou China

Carrying out remote sensing refinement identification of forest land in complex environment is of great significance for timely mapping of forest distribution. Aiming at the problem that remote sensing images have bias in the extraction of forest land information data, based on the semantic segmentation algorithm Unet, combining the ResNet50 deep learning network, the attention mechanism module and the feature pyramid structure, we construct RAF-Unet (ResNet+Attention+FPN+Unet) to improve the extraction of forest land information data. The ResNet50 classification network is used as the encoder of the Unet network to extract the feature maps at five different scales;then, the attention mechanism module is introduced in the decoder stage of the Unet network to extract the key task goal information by learning the weight values of the features;finally, the feature pyramid structure is used in the output stage of the encoder to fuse the information from the shallow network and the deep network to extract the remote sensing forest land information in the image. The results show that the RAF-Unet algorithm outperforms the Unet algorithm in all the indexes, with a precision of 95.24%, a recall of 91.80%, an F1-score value of 93.49%, an intersection over union of 87.63%, and an accuracy of 93.68%;the validity of the modules is verified by the ablation experiments, and the ResNet network, the attention mechanism, and the feature pyramid structure are all effective in improve the classification effect. It helps the forestry department to better manage and dynamically monitor forestry information, which is of great significance to the scientific development, utilization and protection of forest land resources. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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