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NASTCNN: a spatio-temporal convolutional neural network with non-local attention mechanism for image-induced brain activity pattern recognition

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Optoelectronics Letters 2025年

作者： TAN Xiangxin SHAO Mingxiong ZENG Ke GU Yue School of Computer Science and Engineering， Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， Tianjin University of Technology Krembil Research Institute， University Health Network

Deciphering the brain’s visual processing mechanisms has always been a hot topic. Using neural signal patterns to predict the corresponding external visual stimuli is crucial in understanding the interaction of real visual scenes. There are some challenges in the study of image-induced brain activity pattern recognition based on electroencephalogram (EEG)， e.g. how to fully utilize the effective features of EEG signals to improve the classification， and how the frequency and the length of EEG effect the model performance. We design an experimental paradigm that minimizes the interaction between visual stimuli， and propose a spatio-temporal convolutional neural network with non-local attention mechanism (NASTCNN) for image-induced brain activity pattern recognition. The framework utilizes non-local feature extraction operations to realize global information interaction between EEG signals over long distances and enhance the feature expression of some important information. Our model achieves the best performance on both MNIST-EEG and ImageNet-EEG datasets. The classification results are 24.00% and 50.10% respectively， higher than the random probability. We also analyze the effects of different EEG frequency bands and time subsequences of different lengths on the classification performance and which brain regions are activated over time. The results show that when full frequency band and entire length sequence are selected， the classification result is optimal. The occipital region is essential for processing visual tasks， and the temporal region and some frontal regions also play important role in the later stage of visual decoding. This research can help us better understand the visual processing mechanism of the brain.

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Skill learning and inference framework

Skill learning and inference framework

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6th International Conference on Artificial General Intelligence, AGI 2013

作者： Lee, Sang Hyoung Suh, Il Hong Education Center for Network-Based Intelligent Robotics Hanyang University Seoul Korea Republic of Department of Computer Science and Engineering Hanyang University Seoul Korea Republic of

ISBN: (纸本)9783642395208

We propose a skill learning and inference framework, which includes five processing modules as follows: 1) human demonstration process, 2) autonomous segmentation process, 3) process of learning dynamic movement primitives, 4) process of learning Bayesian networks, 5) process of constructing motivation graph and inferring skills. based on the framework, the robot learns and infers situation-adequate and goal-oriented skills to cope with uncertainties and human perturbations. To validate the framework, we present the experimental results when using a robot arm that performs a daily-life task. © 2013 Springer-Verlag.

关键词： Bayesian networks

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Learning of motor skills based on grossness and fineness of movements in daily-life tasks

Learning of motor skills based on grossness and fineness of ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sang Hyoung Lee Nam Jun Cho Il Hong Suh Education Center for Network-based Intelligent Robotics Hanyang University Seoul Korea Department of Electronics and Computer Engineering Hanyang University Seoul Korea

In this paper, we propose a novel method for learning motor skills based on grossness and fineness of movements involved in daily-life tasks. Grossness and fineness depend on the degrees of complexity (i.e., linear combinations between basis vectors) and repeatability (i.e., repeat accuracies between multiple trials) of such movements. In such a daily-life task, a robot's movements are usually related to a task-relevant object. Therefore, the complexity and the repeatability should be acquired from datasets that include the spatial and temporal relationships between a robot and a task-relevant object. To measure the degree of complexity, correlations are first obtained from each data by canonical correlation analysis. To measure the degree of repeatability, variations are then obtained from covariances between datasets acquired by multiple trials. The grossness and fineness are finally acquired by combining the correlations and the variations. To learn a motor skill, a Gaussian Mixture Model (GMM) is estimated using well-known methods as Principal Component Analysis (PCA), k-means, Bayesian Information Criterion (BIC), and Expectation-Maximization (EM) algorithms. First, initial parameters of a GMM are estimated by weighting a conventional k-means algorithm with the grossness and fineness. based on PCA, BIC, and EM algorithms, the GMM is then estimated using the initial parameters and a robot's motion trajectories. To validate our proposed methods, the GMM is evaluated in terms of reproduction and recognition using a robot arm that performs two daily-life tasks: cookie-decorating and constrained-delivering tasks.

关键词： Correlation Robots Indexes Clustering algorithms Trajectory Principal component analysis Complexity theory

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A Novel Grades Prediction Method for Undergraduate Students by Learning Explicit Conditional Distribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4837-4848页

作者： Zhang, Na Liu, Ming Wang, Lin Liu, Shuangrong Sun, Runyuan Yang, Bo Zhu, Shenghui Li, Chengdong Yang, Cheng Cheng, Yuhu China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Quan Cheng Laboratory Jinan250100 China Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China Shandong Jianzhu University School of Information and Electrical Engineering Jinan250101 China Ministry of Education Engineering Research Center of Intelligent Control for Underground Space Xuzhou221116 China

educational data mining (EDM) offers an effective solution to predict students' course grades in the next term. Conventional grade prediction methods can be viewed as regressing an expectation of the probability distribution of the student's grade, typically called single-value grade prediction. The reliable prediction outcomes of these methods depend on the complete input information related to students. However, next-term grade prediction often encounters the challenge of incomplete input information due to the inaccessibility of future data and the privacy of data. In this scenario, single-value grade prediction struggles to assess students' academic status, as it may not be represented and assessed by relying on a singular expectation value. This limitation increases the risk of misjudgment, and may lead to errors in educational decision-making. Considering the challenge of collecting complete input information, we shift from traditional single-value predictions to forecasting the explicit probability distribution of the course grade. The probability distribution of the grade can assess the students' academic status by providing probabilities corresponding to all possible grade values rather than relying solely on an expectation value, which offers the foundation to support the educators' decision-making. In this article, the course grade distribution prediction (CGDP) model is proposed, aiming to estimate an explicit conditional probability distribution of course grades in the next term. This model can identify at-risk students, offering comprehensive decision-making information for educators and students. To ensure precise distribution predictions, a calibration method is also employed to improve the alignment between predicted and actual probabilities. Experimental results verify the effectiveness of the proposed model in early grade warning for undergraduates, based on real university data. © 2020 IEEE.

关键词： Probability distributions

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Learning of social skills for Human-Robot Interaction by hierarchical HMM and interaction dynamics

Learning of social skills for Human-Robot Interaction by hie...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Min Gu Kim Sang Hyoung Lee Il Hong Suh Department of Electronics and Computer Engineering Hanyang University Seoul Korea Education Center for Network-based-Intelligent Robots Hanyang University Seoul Korea

In Human-Robot Interaction, an intelligent robot should be able to learn social skills and reproduce such skills according to dynamic human's behaviors. To this end, both motion trajectories of a human and a robot are autonomously segmented, after which social skills are represented by combining hierarchical hidden Markov models and interaction dynamics (i.e., mass-spring-damper) to include three abilities of recognition, reproduction, and adaptation. To validate this, we present the experimental results when using a humanoid robot that performs several social skills.

关键词： Hidden Markov models Trajectory Dynamics Motion segmentation Human-robot interaction intelligent robots

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Natural Human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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An Integrated Bifunctional Pressure‒Temperature Sensing System Fabricated on a Breathable Nanofiber and Powered by Rechargeable Zinc–Air Battery for Long‑Term Comfortable Health Care Monitoring

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Advanced Fiber Materials 2024年第4期6卷 1037-1052页

作者： Peng Wang Gengsheng Liu Guifen Sun Chuizhou Meng Guozhen Shen Yang Li School of Mechanical Engineering University of JinanJinan 250022China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China School of Microelectronics Shandong UniversityJinan 250101China

Bulky external power supplies largely limit the continuous long-term application and miniaturization development of smart sensing ***,we fabricate a flexible and wearable integrated sensing system on an electrospun all-nanofiber *** three parts of the sensing system are all obtained by a facile ink-based direct writing *** resistive pressure sensor is realized by decorating MXene sheets on TPU ***,the resistive temperature sensor is prepared by compositing MXene sheets into poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS).The thin-film zinc–air battery(ZAB)includes an interdigital zinc–air electrode that is bonded with a gel polymer *** can supply a high open-circuit voltage of 1.39 V and a large areal capacity of 18.2 mAh cm^(-2) for stable and reliable power-supplying sensing parts *** to the hydrophobic nature of TPU and open-ended micropores in the TPU nanofiber,the sensing system is waterproof,self-cleaning,and air and moisture *** application,the above-mentioned functional components are seamlessly integrated into an intelligent electronic wristband,which is comfortably worn on a human wrist to monitor pulse and body temperature in real time with continuous operation of up to 4 *** the novel design and remarkable performance,the proposed integrated all-nanofiber sensing system presents a promising solution for developing advanced multifunctional wearable electronics.

关键词： Pressure‒temperature sensor Rechargeable zinc–air battery Electrospun nanofibers Permeable electronic Integrated sensing system

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Efficient Federated Learning with Spike Neural networks for Traffic Sign Recognition

arXiv

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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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Brightness-Enhanced Electroluminescence Driven by Triboelectric Nanogenerators Through Permittivity Manipulation and Impedance Matching

SSRN

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SSRN 2022年

作者： Gu, Mingwei Chen, Yunfeng Gu, Shaoqiang Wang, Can Chen, Liguo Shen, Hao Chen, Guanyu Sun, Xuhui Huang, Haibo Zhou, Yunlei Wen, Zhen Institute of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou215123 China Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices Soochow University Suzhou215123 China College of Chemical Engineering and Technology Hebei University of Technology Tianjin300130 China Key Laboratory of Advanced Technologies of Materials Ministry of Education School of Materials Science and Engineering Southwest Jiaotong University Chengdu610031 China State Key Laboratory of Digital Manufacturing Equipment and Technology Flexible Electronics Research Center Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Triboelectric nanogenerator-driven electroluminescence (TDEL) that can convert human kinetic energy into visible light has attracted extensive attention in wearable electronics. However, the brightness is usually low due to the low electric field strength on the phosphors caused by impedance mismatch. In this work, a TDEL system with optimal brightness has been proposed by adjusting the dielectric constant of nanocomposite and matching impedance with TENG. The circuit model of the TDEL system is established and the voltage at both ends of the EL layer is predicted through simulation, which error is within 4.7%. Finite element simulation is used to simulate the optimal field strength of phosphors under the dual effects of voltage drop and polarization enhancement of EL layer caused by the increase of dielectric constant of nanocomposites in TDEL system. When the volume ratio of ZnS:Cu is 40% and the dielectric constant of the composite increases from 12 to 22.8, the optimal brightness value increases by 172%. Finally, a patterned TDEL system driven by walking or running to realize the functions of night alarm and information exchange is demonstrated. This study opens the window to improve the brightness of TDEL system through permittivity manipulation and impedance matching, which is applicable to different types of TENG, luminescent particles and dielectric nanocomposites. © 2022, The Authors. All rights reserved.

关键词： Nanogenerators

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Exploring nutritional supplement use for countering respiratory tract infections through an X (formerly Twitter)-based survey

Current Research in Biotechnology

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Current Research in Biotechnology 2025年 9卷

作者： Singla, Rajeev K. Mondal, Himel Singla, Shailja De, Ronita Behzad, Sahar Găman, Mihnea-Alexandru Sai Chandragiri, Siva Cenanovic, Merisa Patra, Jayanta Kumar Depew, Jennifer R. Boyina, Hemanth Kumar Maigoro, Abdulkadir Yusif Lee, Soojin Atrooz, Omar M. Das, Gitishree Schultz, Fabien Abdallah, Emad Mohamed Chopra, Hitesh Ahmad, Jamil Gautam, Rupesh K. Patnaik, Sourav S. Goh, Bey Hing Babiaka, Smith B. Vats, Sharad Okoh, Michael P. Ahmed, Atallah F. Dubey, Ankit Kumar Lordan, Ronan Subramani, Parasuraman Aiya Singh, Amit Kumar Alvarez-Suarez, José M. Chellappan, Dinesh Kumar Paswan, Shravan Kumar Semwal, Prabhakar Khan, Johra Sheshe, Sadeeq Sethiya, Neeraj Kumar Karpiński, Tomasz M. Riaz, Muhammad Emam-Djomeh, Zahra Gupta, Girish Kumar Madaan, Reecha Kumar, Suresh Choudhary, Neeraj Parisi, Salvatore Willschke, Harald Pirgozliev, Vasil Rayan, Rehab A. Ritschl, Valentin Mondal, Shaikat Zengin, Gokhan Verma, Pritt Kapoor, Bhupinder Gulati, Monica Balla, Gareeballah Osman Adam Le, Dan Khoa Pittalà, Valeria El-Demerdash, Amr Khalid, Garba M. Simal-Gandara, Jesus Alzahrani, Qushmua E. Russo, Gian Luigi Kharat, Kiran R. Bishayee, Anupam Wang, Dongdong Orhan, Ilkay Erdogan Ullah, Hammad Heinrich, Michael Baral, Bikash Tzvetkov, Nikolay T. Yeung, Andy Wai Kan Dias-Ferreira, João M.L. Olea, Scarlett Perez Mohanta, Yugal Kishore Kureshi, Azazahemad A. Supuran, Claudiu T. Rani, Neeraj Gundamaraju, Rohit Mulholland, Eoghan Joseph Lonardo, Sara Di Dinkova-Kostova, Albena T. González-Burgos, Elena Hritcu, Lucian Badhe, Pravin Singh, Abhilasha Al-Rimawi, Fuad Sureda, Antoni Abiri, Rambod Braidy, Nady Kapral, Lorenz Abdullahi, A.N. Medina, Christhian Delfino Villanueva Sheridan, Helen Lucarini, Massimo Durazzo, Alessandra Giampieri, Francesca Barreca, Davide Maria, Witkowska Anna Andrade, José Carlos Fimognari, Carmela Akram, Faizan Tikhonov, Aleksei Battino, Maurizio Oladipupo, Akolade R. Emerald, Mila Efferth, Thomas Amrani, Said Echeverría, Javier Maria Louka, Anna Tripathi, Surya Kant Fiebich, Bernd L. Es-Safi, Nour Eddine Khan Department of Critical Care Medicine and Institutes for Systems Genetics Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu China Department of Physiology All India Institute of Medical Sciences Jharkhand Deoghar India iGlobal Research and Publishing Foundation New Delhi India Evidence-based Phytotherapy and Complementary Medicine Research Center Alborz University of Medical Sciences Karaj Iran Department of Hematology Center for Clinical and Basic Research (CCBR Clinic) Bucharest Romania Department of Cellular and Molecular Pathology Stefan S. Nicolau Institute of Virology Romanian Academy Bucharest Romania Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK United States Sarajevo Bosnia and Herzegovina Research Institute of Integrative Life Sciences Dongguk University-Seoul Goyang-Si South Korea United States University Institute of Pharma Sciences Chandigarh University Punjab Mohali 140413 India Department of Microbiology and Molecular Biology College of Bioscience and Biotechnology Chungnam National University Daejeon 34134 South Korea Department of Biological Sciences Mutah University Jordan Ethnopharmacology & Zoopharmacognosy Research Group Department of Agriculture and Food Sciences Neubrandenburg University of Applied Sciences Germany Department of Biology College of Science Qassim University Buraydah 51452 Saudi Arabia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai 602105 India Department of Human Nutrition The University of Agriculture Peshawar Khyber Pakhtunkhwa 25120 Pakistan Department of Pharmacology Indore Institute of Pharmacy IIST Campus Rau (M.P.) Indore 453331 India The University of Texas Southwestern Medical Center Dallas TX United States Sunway Biofunctional Molecules Discovery Centre (SBMDC) School of Medical and Life Sciences Sunway University S

Background: Respiratory tract infections are a common health issue, driving interest in preventive strategies like nutritional supplements, while evidence on their usage and effectiveness remains limited. In this context, social media platforms, particularly X (formerly Twitter), provide a unique opportunity to gather large-scale public health-related data. Objectives: In this study, we aimed to survey participants’ uses and opinions on nutritional supplements in prevention or treatment of respiratory tract infections, by using X. Methods: A survey was conducted between 1st and 15th December 2022. A single open-ended question “Which are the best dietary supplements to counteract respiratory infections?“ was asked. One week after the start of the survey, a poll was posted to get more relevant information and boost the survey's reach. Total endorsements were calculated for each tweet posted as the total sum of replies, retweets, and likes. Results: The open-ended question received a total of 118 retweets, 39 quotes, and 371 likes, while the poll received 56 retweets, 13 quotes, and 67 likes. A total of 495 replies, 2,251 retweets, 5,118 likes, and 148 quotes were received for the question and its related tweets. Vitamin D (1,607 endorsements), zinc (1,347 endorsements), vitamin C (803 endorsements), magnesium (694 endorsements), and honey (661 endorsements) were the nutritional supplements that received most endorsements. Conclusion: Various foods, drinks, and natural ingredients have been suggested as potentially helpful for counteracting respiratory infections. Approximately half of respondents indicated using such supplements for themselves. The result of this study supports the idea that the X platform can be used as an effective survey tool to study global health-related behaviours and trends. © 2025 The Authors

关键词： Bacteria COVID-19 Nutritional supplements Phytomedicine Respiratory tract infections X (formerly Twitter)

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