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American control Conference (ACC)

作者： Zewen Yang Songbo Dong Armin Lederer Xiaobing Dai Siyu Chen Stefan Sosnowski Georges Hattab Sandra Hirche Center for Artificial Intelligence in Public Health Research (ZKI - PH) Robert Koch Institute Berlin Germany Chair of Information-oriented Control (ITR) TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Computer Science ETH Zurich Zurich Switzerland Walker Department of Mechanical Engineering University of Texas at Austin Austin TX USA Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative al-gorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures.

关键词： Uncertainty Protocols Correlation Decentralized control Switches Gaussian processes Topology

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Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data

arXiv

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arXiv 2023年

作者： Li, Yifei Liu, Wenjie Sun, Jian Wang, Gang Xie, Lihua Chen, Jie The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Control Science and Engineering Tongji University Shanghai201804 China

This paper proposes a novel approach to addressing the output synchronization problem in unknown heterogeneous multi-agent systems (MASs) using noisy data. Unlike existing studies that focus on noiseless data, we introduce a distributed data-driven controller that enables all heterogeneous followers to synchronize with a leader’s trajectory. To handle the noise in the state-input-output data, we develop a data-based polytopic representation for the MAS. We tackle the issue of infeasibility in the set of output regulator equations caused by the noise by seeking approximate solutions via constrained fitting error minimization. This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization. Stability conditions in the form of data-dependent semidefinite programs are derived, providing stabilizing controller gains for each follower. The proposed distributed data-driven control protocol achieves near-optimal output synchronization by ensuring the convergence of the tracking error to a bounded polytope, with the polytope size positively correlated with the noise bound. Numerical tests validate the practical merits of the proposed data-driven design and theory. © 2023, CC BY.

关键词： Multi agent systems

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Character Recognition of Vedic Sanskrit using Deep Learning Algorithms

Character Recognition of Vedic Sanskrit using Deep Learning ...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Mrinal Pandey Rashmikiran Pandey Alexey Nazarov Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781665462013

One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. This information gives us an idea about the art and the culture of that respective era. In this paper we proposed a model for digital recognition of textual characters written in Sanskrit language using the 4-fold Convolutional Neural Network (CNN) architecture. At the first stage we remove the noise and subsequently segmentation of characters from the input image is performed, later we convert the image into binary image format by the help of image processing techniques. The research is performed over the Devanagari script which was developed during 1 st to the 4 th century CE and is common to many languages from the Indian subcontinent.

关键词： Training Deep learning Support vector machines Computational modeling Natural languages Graphics processing units Information retrieval

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Mathematical Model Validation of Search Protocols in MP2P Networks

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computers, Materials & Continua 2021年第8期68卷 1807-1829页

作者： Ajay Arunachalam Vinayakumar Ravi Moez Krichen Roobaea Alroobaea Saeed Rubaiee Centre for Applied Autonomous Sensor Systems(AASS) Örebro UniversityÖrebroSweden Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Faculty of CSIT Al-Baha UniversitySaudi Arabia ReDCAD LaboratoryUniversity of SfaxTunisia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringUniversity of JeddahJeddahSaudi Arabia

Broadcasting is a basic technique in Mobile ad-hoc network(MANET),and it refers to sending a packet from one node to every other node within the transmission *** is a type of broadcast where the received packet is retransmitted once by every *** naive flooding technique,floods the network with query messages,while the random walk technique operates by contacting the subsets of every node’s neighbors at each step,thereby restricting the search *** of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried *** earlier works have mainly focused on the simulation-based analysis of flooding,and its variants under a wired ***,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of P2P systems running over *** this paper,we describe how P2P resource discovery protocols perform badly over *** address the limitations,we propose a new protocol named ABRW(Address Broadcast Random Walk),which is a lightweight search approach,designed considering the underlay topology aimed to better suit the unstructured *** provide the mathematical model,measuring the performance of our proposed search scheme with different widely popular benchmarked search ***,we also derive three relevant search performance metrics,i.e.,mean *** steps needed to find a resource,the probability of finding a resource,and the mean *** message *** validated the analytical expressions through *** simulation results closely matched with our analyticalmodel,justifying our *** proposed search algorithm under such highly dynamic self-evolving networks performed better,as it reduced the search latency,decreased the overall message overhead,and still equally had a good success rate.

关键词： Search protocols random walk MANET P2P networks P2P MANET mathematical model peer-to-peer wireless ad hoc network flooding mobile P2P unstructured P2P NS-2 network simulator

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Radiomics Model Based on Computed Tomography Images for Prediction of Radiation-Induced Optic Neuropathy Following Radiotherapy of Brain and Head and Neck Tumors

SSRN

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SSRN 2024年

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Noraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Computer Science University of Copenhagen Copenhagen Denmark Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and Methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. The radiomics features were extracted from the area segmented by Swin UNETR version 2, which included the right and left optic nerves and chiasm. We ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31% of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97% and 95% respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision can predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024, The Authors. All rights reserved.

关键词： Decision trees

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Output Regulation of Nonlinear Systems by an Emulation-Based Approach

Output Regulation of Nonlinear Systems by an Emulation-Based...

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American control Conference (ACC)

作者： Jieshuai Wu Maobin Lu Fang Deng Jie Chen School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Department of Control Science and Engineering Tongji University Shanghai China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing China

In this paper, we investigate the semi-global robust output regulation problem of a class of nonlinear networked control systems by an emulation approach. We propose a class of sampled-data control laws to solve this problem. In particular, by the emulation approach, we first develop a class of sampled-data dynamic output feedback control laws. Then, based on the internal model principle, we convert the semi-global robust output regulation problem into a semi-global robust stabilization problem of an augmented hybrid system composed of the internal model and the original system. Next, we show that semi-global robust stabilization of the augmented hybrid system can be achieved by a sampled-data control law and thus leading to the solution of the semi-global robust output regulation problem. Finally, an example is given to illustrate our control approach.

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Semantic-Consistent Deep Quantization for Cross-modal Retrieval 11

Semantic-Consistent Deep Quantization for Cross-modal Retrie...

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11th International Conference on Intelligent control and Information Processing, ICICIP 2021

作者： Ma, Liya Zhang, Nian Shu, Kuang I. Zou, Xitao Southwest University College of Computer and Information Science Chongqing400715 China University of the District of Columbia Department of Electrical and Computer Engineering WashingtonDC20008 United States Liuzhou Yunde Electronic Technology Co. Ltd Research and Develop Center Guangxi Autonomous Region Liuzhou545005 China

ISBN: (纸本)9781665425155

With making up for the deficiency of the constraint representation capability of hashing codes for high-dimensional data, the quantization method has been found to generally perform better in cross-modal similarity retrieval research. However, in current quantization approaches, the codebook, as the most critical basis for quantization, is still in a passive status and detached from the learning framework. To improve the initiative of codebook, we propose a semantic-consistent deep quantization (SCDQ), which is the first scheme to integrate quantization into deep network learning in an end-to-end fashion. Specifically, two classifiers following the deep representation learning networks are formulated to produce the class-wise abstract patterns with the help of label alignment. Meanwhile, our approach learns a collaborative codebook for both modalities, which embeds bimodality semantic consistent information in codewords and bridges the relationship between the patterns in classifiers and codewords in codebook. By designing a novel algorithm architecture and codebook update strategy, SCDQ enables effective and efficient cross-modal retrieval in an asymmetric way. Extensive experiments on two benchmark datasets demonstrate that SCDQ yields optimal cross-modal retrieval performance and outperforms several state of-the-art cross-modal retrieval methods. © 2021 IEEE.

关键词： Deep neural networks

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Physical Activity,Sedentary Behavior,and the Risk of Cardiovascular Disease in Type 2 Diabetes Mellitus Patients:The MIDiab Study

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engineering 2023年第1期20卷 26-35页

作者： Yafei Wu Guijun Qin Guixia Wang Libin Liu Bing Chen Qingbo Guan Zhongshang Yuan Xu Hou Ling Gao Chao Xu Haiqing Zhang Xu Zhang Qiu Li Yongfeng Song Fei Jing Shizhan Ma Shanshan Shao Meng Zhao Qingling Guo Nanwei Tong Hongyan Zhao Xiaomin Xie Chao Liu Zhongyan Shan Zhifeng Cheng Xuefeng Yu Shulin Chen Tao Yang Yangang Wang Dongmei Li Zhaoli Yan Lixin Guo Qiuhe Ji Wenjuan Wang Jiajun Zhao Department of Endocrinology&Shandong Clinical Research Center of Diabetes and Metabolic Diseases Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinan 250021China Shandong Key Laboratory of Endocrinology and Lipid Metabolism&Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases Shandong First Medical UniversityJinan 250021China Department of Endocrine and Metabolic Diseases The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052China Department of Endocrinology and Metabolism First Hospital of Jilin UniversityChangchun 130021China Department of Endocrinology and Metabolism Fujian Medical University Union HospitalFuzhou 350001China Endocrinology Department The Southwest Hospital of AMUChongqing 400038China Department of Biostatistics School of Public HealthShandong UniversityJinan 250012China Department of Endocrinology and Metabolism West China Hospital of Sichuan UniversityChengdu 610041China Department of Endocrine and Metabolic Diseases Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200020China Department of Endocrinology The First People’s Hospital of YinchuanYinchuan 750001China Department of Endocrinology Nanjing Integrated Traditional Chinese and Western Medicine Hospital(the Affiliated Hospital of Nanjing University of Traditional Chinese Medicine)Nanjing 210046China Department of Endocrinology and Metabolism&Institute of Endocrinology the First Hospital of China Medical UniversityShenyang 110000China Department of Endocrinology the Fourth Affiliated Hospital of Harbin Medical UniversityHarbin 150001China Division of Endocrinology Department of Internal MedicineTongji HospitalTongji Medical College of Huazhong University of Science and TechnologyWuhan 430030China Department of Endocrinology Yantai Yuhuangding Hospital Affiliated to Qingdao University Medical CollegeYantai 264000China Department of Endocrinology the First Affiliated Hospital of Nanjing Medical University

The aim of this study was to explore the associations of moderate-to-vigorous-intensity physical activity(MVPA)time and sedentary(SED)time with a history of cardiovascular disease(CVD)and multifactorial(i.e.,blood pressure(BP),body mass index(BMI),low-density lipoprotein cholesterol(LDL-C),and glycated hemoglobin A1c(HbA1c))control status among type 2 diabetes mellitus(T2DM)patients in China.A cross-sectional analysis of 9152 people with type 2 diabetes from the Multifactorial Intervention on Type 2 Diabetes(MIDiab)study was *** were grouped according to their self-reported MVPA time(low,<150 min·week−1;moderate,150 to<450 min·week−1;high,≥450 min·week−1)and SED time(low,<4 h·d–1;moderate,4 to<8 h·d–1;high,≥8 h·d–1).Participants who self-reported a history of CVD were identified as having a CVD *** ratios(ORs)and 95%confidence intervals(CIs)of CVD risk and multifactorial control status associated with MVPA time and SED time were estimated using mixed-effect logistic regression models,adjusting for China’s geographical region *** participants had a mean±standard deviation(SD)age of(60.87±8.44)years,44.5%were women,and 25.1%had *** adjustment for potential confounding factors,an inverse association between high MVPA time and CVD risk that was independent of SED time was found,whereas this association was not observed in the moderate-MVPA group.A higher MVPA time was more likely to have a positive effect on the control of *** with the reference group(i.e.,those with MVPA time≥450 min·week−1 and SED time<4 h·d–1),CVD risk was higher in the low-MVPA group:The OR associated with an SED time<4 h·d–1 was 1.270(95%CI,1.040–1.553)and that associated with an SED time≥8 h·d–1 was 1.499(95%CI,1.149–1.955).We found that a high MVPA time(i.e.,≥450 min·week−1)was associated with lower odds of CVD risk regardless of SED time among patients with T2DM.

关键词： Type 2 diabetes Physical activity Sedentary time Car diovascular disease

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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