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arXiv 2021年

作者： Wang, Kafeng Xiong, Haoyi Zhang, Jie Chen, Hongyang Dou, Dejing Xu, Cheng-Zhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Shenzhen China Big Data Lab Baidu Inc. Haidian Beijing China Key Laboratory of High Confidence Software Technologies Peking University Haidian Beijing China Research Center for Intelligent Network Zhejiang Lab Zhejiang Hangzhou China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Tapia Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers’ privacy or require high deployment cost, this paper introduces a low-cost method, namely SenseMag, to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. SenseMag filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, SenseMag adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, SenseMag classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that SenseMag is with at least 90% vehicle type classification accuracy and less than 5% vehicle length classification error. Copyright © 2021, The Authors. All rights reserved.

关键词： Sensor nodes

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Bacteria Flagella-Mimicking Polymer Multilayer Magnetic Microrobots

SSRN

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SSRN 2024年

作者： Wu, Zhiguang Lu, Liang Bai, Shuang Shi, Jiaqi Zhang, Zhuotao Pan, Yunlu Wang, Wei Wu, Wenfeng Sun, Shoubin Li, Xu Hou, Gang Huang, Tianyun Jia, Yuxin Zuo, Kaiwen Granovsky, Alexander Wu, He Nikolai, Perov School of Medicine and Health Harbin Institute of Technology Harbin China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China The First Affiliated Hospital of Harbin Medical University Harbin China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and Multimorbidity National Clinical Research Center for Respiratory Diseases Institute of Respiratory Medicine Chinese Academy of Medical Sciences Department of Pulmonary and Critical Care Medicine Center of Respiratory Medicine China-Japan Friendship Hospital Beijing China College of Science Sichuan Agricultural University Chengdu China The Fourth Affiliated Hospital of Harbin Medical University Harbin China Department of Ophthalmology The Second Hospital Jilin University Changchun China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Mathematics Imperial College London United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom Magnetism Department Faculty of Physics Lomonosov Moscow State University Moscow Russia Harbin Medical University China

Learning from natural bacteria flagellum, we demonstrate a magnetic polymer multilayer conical microrobot that bestow the controllable propulsion upon external rotating magnetic field with uniform intensity. The magnetic microrobots were constructed by template-assisted Layer-by-Layer technique technique and subsequent functionalization of magnetic particle onto the big opening of the microrobots. Geometric variables of the polymer microrobots, such as the diameter and wall thickness, can be controlled by selection of porous template and layers of assembly. The microrobots perform controllable propulsion through the manipulation of magnetic field. The comparative analysis of the movement behavior reveals that the deformation of microrobots may attributed to the propulsion upon rotating magnetic field, which is similar as that of natural bacteria. The influence of actuation frequencies and frequencies on the velocity of the microrobots is studied. Such use of polymer polymer multilayer tubular micro/nanoarchitectures results in significant savings in the processing costs and provides a simple and cost-effective fabrication route for the mass production of magnetic polymer microrobots. © 2024, The Authors. All rights reserved.

关键词： Bacteria

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Synthesis of Fe₃O₄-NH₂-APTES@rGO@SiO₂ core–shell magnetic microspheres for efficient aqueous phenol photocatalytic degradation

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Results in Physics 2024年 62卷

作者： Ur Rehman, Ghani Tahir, Muhammad Goh, P.S. Baba Basha, D. Ismail, A.F. Alhazmi, Hadil Abdul Wahab, Roswanira Samavati, Alireza Khan, Ilyas Department of Chemistry Faculty of Science Universiti Teknologi Malaysia Johor Skudai 81310 UTM Malaysia Chemical and Petroleum Engineering Department United Arab Emirates University P.O. Box 15551 Al Ain United Arab Emirates Department of information systems college of computer and information sciences Majmaah University Al Majmaah -11952 Saudi Arabia Department of Mathematical Sciences College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Advanced Membrane Technology Research Centre (AMTEC) Universiti Teknologi Malaysia Johor Bahru Skudai 81310 Malaysia Department of Mathematics College of Science Al-Zulﬁ Majmaah University Al-Majmaah Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

This study aims to synthesize a ternary Fe3O4-NH2-APTES@rGO@SiO2 core–shell magnetic microspheres by modified Stöber technique to photodegrade aqueous phenol under UV-C light effectively. Results of the X-ray di... 详细信息

This study aims to synthesize a ternary Fe₃O₄-NH₂-APTES@rGO@SiO₂ core–shell magnetic microspheres by modified Stöber technique to photodegrade aqueous phenol under UV-C light effectively. Results of the X-ray diffractogram of the as-synthesized Fe₃O₄-NH₂-APTES@rGO@SiO₂ core–shell magnetic microspheres showed the nanoparticles having an average size of 126.00 nm, comparable to 150.00 nm seen in the FESEM micrograph. These sizes were supported by the EDX and FTIR results which verified the presence of Fe₃O₄, rGO, and SiO₂, and the TEM micrograph revealed them to be spherically shaped. Also, the thermally more stable microspheres than the pure individual components and binary nanocomposites were evident in their corresponding TGA ***, the photoluminescence spectra of the Fe₃O₄-NH₂-APTES@rGO@SiO₂ microspheres supported their higher photodegrading ability as a consequence of an extended e^-/h⁺ recombination rate. The microspheres were more effective in photo-catalytically degrading the highest amount of aqueous phenol (∼89 %) than Fe₃O₄-NH₂-APTES@rGO (∼74 %), Fe₃O₄-NH₂-APTES@SiO₂ (∼78 %), and pure Fe₃O₄ (∼62 %), under an optimized condition of 0.2 g/350 mL catalyst loading and 50 ppm of initial phenol concentration. The enhanced photoactivity was ascribed to the high stability and specific surface of SiO₂, the synergistic influence of Fe₃O₄, and the rGO sheet's rapid electron transport·H₂O₂ enhanced phenol degradation to 94 %. © 2024

关键词： Fe3O4-NH2-APTES@rGO@SiO2 microsphere Modified Stöber method Phenol Photodegradation

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Development of Training System and Practice Contents for Cybersecurity Education

Development of Training System and Practice Contents for Cyb...

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IIAI International Conference on advanced Applied Informatics (IIAIAAI)

作者： Sanggyu Shin Yoichi Seto Yosuke Kasai Rituna Ka Daishi Kuroki Shinichi Toyoda Koji Hasegawa Kazuhiro Midorikawa Dept. of Applied Computer Engineering TOKAI University Hiratsuka-shi Japan Master Program of Information Systems Architecture Advanced Institute of Industrial Technology Tokyo Japan Master Program of Information Systems Architecture Advanced Institute of Industrial Technology

ISBN: (纸本)9781728126289

In this paper, we propose a cybersecurity exercise system in a virtual computer environment. The human resource development for security fields is an urgent issue because of the threat of cyber-attacks, recently, is increasing, many incidents occurring, but there is a not enough security personnel to respond. Some universities and companies are conducting education using a commercial training system on the market. However, built and operates the training system is expensive, therefore difficult to use in higher education institutions and SMEs. However, to build and operates, the training system needs high cost, thus difficult to use in higher education institutions and SMEs. For this reason, we developed the CyExec: a cybersecurity exercise system consisting of a virtual computer environment using VirtualBox and Docker. We also implemented the WebGoat that is an OSS vulnerability diagnosis and learning program on the CyExec and developed an attack and defense exercise program.

关键词： computer science education educational institutions further education security of data Educational institutions Continuing Education Data Security computer science education Network security higher education institute SUPERCONDUCTING MAGNETIC ENERGY STORAGE Security Personnel Training Functional training

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Regularized Gradient Statistics Improve Generative Deep Learning Models of Super Resolution Microscopy

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Small methods 2025年 e2401900页

作者： Meri Abgaryan Xinning Cui Nandu Gopan Gabriel Della Maggiora Artur Yakimovich Ivo F Sbalzarini Dresden University of Technology Faculty of Computer Science 01187 Dresden Germany. Max Planck Institute of Molecular Cell Biology and Genetics 01307 Dresden Germany. Center for Systems Biology Dresden 01307 Dresden Germany. Center for Advanced Systems Understanding (CASUS) 02826 Görlitz Germany. Helmholtz-Zentrum Dresden-Rossendorf (HZDR) 01328 Dresden Germany. School of Computation Information and Technology Technical University of Munich 85748 Munich Germany. Institute of Computer Science University of Wrocław Wrocław 50-300 Poland. Cluster of Excellence "Physics of Life" TU Dresden 01062 Dresden Germany. Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden/Leipzig 01062 Dresden Germany.

It is shown that regularizing the signal gradient statistics during training of deep-learning models of super-resolution fluorescence microscopy improves the generated images. Specifically, regularizing the images in the training data set is proposed to have gradient and Laplacian statistics closer to those expected for natural-scene images. The BioSR data set of matched pairs of diffraction-limited and super-resolution images is used to evaluate the proposed regularization in a state-of-the-art generative deep-learning model of super-resolution microscopy, the Conditional Variational Diffusion Model (CVDM). Since the proposed regularization is applied as a preprocessing step to the training data, it can be used in conjunction with any supervised machine-learning model. However, its utility is limited to images for which the prior is appropriate, which in the BioSR data set are the images of filamentous structures. The quality and generalization power of CVDM trained with and without the proposed regularization are compared, showing that the new prior yields images with clearer visual detail and better small-scale structure.

关键词： deep learning diffusion models generative artificial intelligence image quality super‐resolution microscopy

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Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks

arXiv

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arXiv 2020年

作者： Dev, Kapal Khowaja, Sunder Ali Jaiswal, Aman Bist, Ankur Singh Saini, Vaibhav Bhatia, Surbhi CONNECT Centre Trinity College Dublin Ireland Faculty of Engineering and Technology University of Sindh Jamshoro Pakistan Department KIET Group of Institutions Ghaziabad India Signy Advanced Technologies. Gurugram Haryana India Dep Indian Institute of Technology Dehli India Department of Information Systems College of Computer Science and Information Technology King Faisal University Saudi Arabia

The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for potential alternatives to reverse transcriptionpolymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of largescale annotated data makes the clinical implementation of machine learning-based COVID detection methods difficult. Another important issue is the usage of ImageNet pretrained networks which does not guarantee to extract reliable feature representations. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from wellknown pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison to the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity. © 2020, CC-BY.

关键词： Convolution

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A Calibration Framework for Deployable Cable Driven Parallel Robots with Flexible Cables

A Calibration Framework for Deployable Cable Driven Parallel...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： R. Khorrambakht H. Damirchi S. A. Khalilpour H. D. Taghirad Advanced Robotics and Automated Systems Faculty of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

Due to their simple and inexpensive structures, suspended cable driven parallel robots are suitable choices for many real-world applications. However, only when the accurate kinematic parameters are available, can we control the robot to the best of its abilities. This is specially a stringent requirement for fast deployable cable driven robots. With the aim of addressing these needs, in this paper we propose an effective framework for calibrating the kinematic parameters of suspended cable driven parallel robots with no requirements for expensive tools and measurement devices. Moreover, the proposed algorithm utilizes the existing force sensors in the cable robot to nominate the best set of data for calibration. The integrity and effectiveness of this framework is reported through simulation and practical experiments, which verifies promising horizons for deployable real-world applications.

关键词： Calibration Robot sensing systems Kinematics Mathematical model Length measurement Jacobian matrices

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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ARAS-IREF: An Open-Source Low-Cost Framework for Pose Estimation

ARAS-IREF: An Open-Source Low-Cost Framework for Pose Estima...

引用

RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： H. Damirchi R. Khorrambakht H. D. Taghirad Advanced Robotics and Automated Systems Faculty of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

Despite the amount of research reported on state estimation and sensor fusion in the field of robotics, there are no well known low-cost solutions for a referencing system to determine the accuracy of developed methods by providing a suitable ground truth for them. In this paper an efficient and accurate 6-DoF pose measurement system is proposed and implemented on a spherical parallel robot using IR LEDs. This approach uses the perspective-n-point algorithm to derive the transformation matrix representing the accurate relative pose of the end-effector with respect to an inertial frame. Exploiting a visible light filter in front of the camera has rendered this approach robust against illumination changes. Furthermore, it allows for mitigating the rolling shutter effects by reducing the exposure time. Finally, a custom made testing module is proposed to verify the accuracy of the proposed device, and the calibration process proves the accuracy and efficiency of the system.

关键词： Cameras Light emitting diodes Robot vision systems Pose estimation Robot kinematics

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