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Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 527-536页

作者： Wu, Huanyu Li, Siyang Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518063 Peoples R China

Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%.

关键词： Feature extraction Training Electroencephalography Task analysis Supervised learning Tongue Time-domain analysis Brain-computer interface electroencephalogram motor imagery self-supervised learning

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A Real-Time Monitoring Method for Selective Laser Melting of TA1 Materials Based on Radiation Detection of a Molten Pool

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MICROMACHINES 2024年第5期15卷 570-570页

作者： Zhou, Tao Huang, Wei Chen, Congyan Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Shenzhen Res Inst Shenzhen 518000 Peoples R China

Selective laser melting (SLM) technology is a promising additive manufacturing technology. However, due to the numerous influencing factors in this complex process, a reliable real-time method is needed to monitor the forming process of SLM. The molten pool is the smallest forming unit in the SLM process, the consistency of which can effectively reflect the quality of the printing process. By using a coaxial optical path structure and a compound amplifier circuit, high-speed acquisition of molten pool radiation can be realized. Next, single factor analysis and orthogonal experimentation were used to investigate the influence levels of key process parameters on the radiation of molten pool. In addition, numerical simulation was carried out with the same parameter setting schemes, the results of which are consistent with those in radiation detection experiments. It is shown that the laser power has the greatest effect on the radiation of the molten pool, while the scanning speed and the hatch spacing have little effect on the radiation. Finally, the positioning experiment involving the small hole structure was carried out, and the experimental results showed that the device could accurately locate the position coordinates of the given hole structure.

关键词： selective laser melting radiation detection molten pool orthogonal experiment numerical simulation

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VMEMDA: Verifiable Multidimensional Encrypted Medical Data Aggregation Scheme for Cloud-Based Wireless Body Area Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第10期11卷 18647-18662页

作者： Zhao, Jie Huang, Hejiao Zhang, Xiaojun He, Daojing Choo, Kim-Kwang Raymond Jiang, Zoe Lin Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China BGP Inc China Natl Petr Corp Natl Engn Res Ctr Oil & Gas Explorat Comp Software Zhuozhou 072751 Peoples R China Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Pengcheng Lab Dept New Networks Shenzhen 518055 Guangdong Peoples R China

Compared to conventional wireless body area networks (WBANs), the amount of data processed and the analytical capabilities offered by cloud-based WBANs are significantly more extensive. Nevertheless, the paramount consideration in such contexts remains the security and privacy ramifications. Concurrently, the process where medical cloud server (MCS) computes the response aggregation data may be opaque and there is a risk that (partially) invalid aggregation results may be presented to the task requester, either intentionally (e.g., malicious or cost-saving) or unintentionally (e.g., corruption or processing error). Furthermore, with the different roles played by each data requester, relying solely on a single data aggregation type is no longer sufficient to satisfy the diverse data aggregation requests from these requesters. To this end, this article proposes a novel verifiable multidimensional encrypted medical data aggregation scheme (VMEMDA) for cloud-based WBANs, where we integrate an extended super-increasing sequence with a modified Paillier cryptosystem. Doing so allows us to ensure that each dimensional medical data collected by wireless sensor devices and corresponding square values can be encrypted into a single ciphertext with the chronological time series. This enables MCS to select various aggregation types, such as spatial/temporal data aggregation, to aggregate the multisource encrypted medical data into a single ciphertext. Then, the task requester can conduct diverse privacy-preserving statistical analyses, including sum, average, and variance. Moreover, we utilize a homomorphic hash function to guarantee the encrypted data integrity in a highly efficient way, and we design an unpredictable random sequence and integrate it into the provable data possession mechanism to achieve aggregated data correctness guarantee. Performance evaluation demonstrates that VMEMDA exhibits considerably lower computation and communication overhead compared to other exi

关键词： Data aggregation Cloud computing Task analysis Data privacy Internet of Things Data integrity Wireless sensor networks Aggregated data correctness guarantee encrypted data integrity (EDI) multidimensional data aggregation statistical analysis wireless body area networks (WBANs)

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Study on the adhesion of PTFE/PI composite films by interlocking synergistic effects

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JOURNAL OF POLYMER ENGINEERING 2024年第3期44卷 204-210页

作者： Zhang, Yicai Ke, Zhao Qian, Guangtao Chen, Chunhai Li, Dandan Donghua Univ Coll Mat Sci & Engn Ctr Adv Low Dimens Mat State Key Lab Modificat Chem Fibers & Polymer Mat Shanghai 201620 Peoples R China Donghua Univ Collaborat Innovat Ctr Civil Aviat Composites 2999 North Renmin Rd Shanghai 201620 Peoples R China

Composite films of polytetrafluoroethylene (PTFE) and polyimide (PI) combine the advantages of both materials like excellent inertness to chemicals and thermal stability, which provide great potential in applications under harsh environments. However, strong adhesion between PTFE and PI layers still remains a big challenge. In this study, fluorinated ethylene propylene (FEP) as an intermediate layer was found to significantly enhance the adhesion of PTFE films to PI film, with peel strength up to 350 N/m in the manufactured direction comparing to the value of 0 for PTFE/PI composite film without FEP. It's superior to that of commercially available products (25 N/m) under the same test conditions. The improved performance is attributed to the formation of interlocks of molecules at the interfaces between the PTFE and FEP layers, and FEP and PI layers as observed in the images obtained by atomic force microscope (AFM) and scanning electron microscopy (SEM).

关键词： polytetrafluoroethylene polyimide fluorinated ethylene propylene adhesion interlock peel strength

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An Experimental Study on the Effect of Nanofluids on the Thermal Conductivity and Rheological Properties of a Coolant for Liquids

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ENERGIES 2024年第6期17卷 1313页

作者： Sun, Le Geng, Jiafeng Dong, Kaijun Sun, Qin Chinese Acad Sci Guangzhou Inst Energy Convers Guangzhou 510640 Peoples R China Changan Univ Sch Water & Environm Xian 710064 Peoples R China

Thermal conductivity and viscosity are important properties for nanofluids as they significantly affect the flow and heat transfer process. To date, the rheological properties of water-based nanofluids have been well studied, while the results are scarce for non-aqueous nanofluids. In this study, the thermal conductivity and rheological properties of two different kinds of oxide nanofluids (CuO and Al2O3) in a typical commercial data center focusing on liquid coolants were systematically investigated at different mass fractions and temperatures. The results showed that the addition of nanoparticles can significantly improve the heat conduction capacity of mineral oil coolants. There is an average increase in thermal conductivity of up to 20-25%. The shear rate-shear stress and shear rate-viscosity curves all showed that mineral oil coolant-based oxide nanofluids behaved as Newtonian fluids and that nanoparticles did not cause the increment in viscosity. The effect of temperature on rheological properties was also studied, and the result showed that high temperatures resulted in low viscosity and shear stress. Finally, the effect of particle type was investigated, and it was found that no matter what kind of nanoparticles were added, their effects on the rheological behaviors were the same.

关键词： liquid cooling nanofluids heat conductivity viscosity rheology

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Energy-Efficient Wireless Power Transfer for Sustainable Federated Learning

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WIRELESS PERSONAL COMMUNICATIONS 2024年第2期134卷 831-855页

作者： Hu, Youqiang Huang, Hejiao Yu, Nuo Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Peoples R China Anhui Polytech Univ Sch Elect Engn Wuhu Peoples R China

Federated Learning (FL) has attracted great attention in recent years and is considered as an enabling technology in future smart wireless networks. Nevertheless, this learning paradigm faces a severe challenge in its implementation procedure, i.e., energy shortage issue. Different from the traditional centralized training paradigm, the training procedure of FL is carried out on mobile devices. Generally, the training tasks are computation-intensive and may involve several communication rounds for transmitting large-sized machine learning models, which indicates that they are high energy-consuming. This characteristic increases burden on mobile devices with limited battery capacity. In this paper, we employ the Radio Frequency (RF)-based Wireless Power Transfer (WPT) technology and time switching energy harvesting architecture to realize a sustainable FL framework, and then design a resource optimization strategy based on the dual and line search methods to minimize the amount of Transferred Energy (TE) required for completing the learning. Moreover, we interpret the Karush-Kuhn-Tucker (KKT) conditions of the formulated problem and obtain some engineering insights. Simulation results verify the convergence of the proposed resource optimization strategy and demonstrate the advantage of the proposed framework over the existing work in terms of TE.

关键词： Wireless power transfer Sustainable federated learning Resource optimization Dual and line search methods

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Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 1703-1714页

作者： Chen, Xiaoqing Wang, Ziwei Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518000 Peoples R China

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs. To mitigate this problem, we propose alignment-based adversarial training (ABAT), which performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary. The integration of data alignment and adversarial training can make the trained EEG classifiers simultaneously more accurate and more robust. Experiments on five EEG datasets from two different BCI paradigms (motor imagery classification, and event related potential recognition), three convolutional neural network classifiers (EEGNet, ShallowCNN and DeepCNN) and three different experimental settings (offline within-subject cross-block/-session classification, online cross-session classification, and pre-trained classifiers) demonstrated its effectiveness. It is very intriguing that adversarial attacks, which are usually used to damage BCI systems, can be used in ABAT to simultaneously improve the model accuracy and robustness.

关键词： Electroencephalography Brain modeling Training Robustness Data models Covariance matrices Perturbation methods Electroencephalogram brain-computer interface adversarial attack adversarial training data alignment

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TiO2/CoOx heterostructure decorated MIL-100(Fe) by atomic layer deposition for enhanced photocatalytic oxygen production

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DALTON TRANSACTIONS 2025年第8期54卷 3467-3477页

作者： Hu, Wenhui Song, Zhongxin Sun, Lingna Zhang, Lei Zhang, Qianling Ren, Xiangzhong Li, Yongliang Shenzhen Univ Coll Chem & Environm Engn Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Guangdong Flexible Wearable Energy & Tools Engn Te Shenzhen 518060 Peoples R China

Efficient separation of photogenerated charge carriers is essential for maximizing the photocatalytic efficiency of semiconductor materials in oxygen evolution reactions (OER). This study presents a novel trimetallic photocatalyst, MIL-100(Fe)/TiO2/CoOx, synthesized through a facile microwave-assisted hydrothermal method followed by atomic layer deposition (ALD). The porous MIL-100(Fe) serves as a support for the sequential deposition of TiO2 and CoOx layers via ALD, which enhances electron-hole pair separation and minimizes their recombination. Characterization using transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) confirms the uniform deposition of TiO2 and CoOx layers on the MIL-100(Fe) surface. The optimized photocatalyst, processed with 20 deposition cycles of Co, exhibits exceptional photocatalytic OER performance, with an oxygen evolution rate of 558.3 mu mol g-1 h-1. This enhancement in activity is attributed to the synergistic interaction between the spatially separated TiO2 and CoOx cocatalysts, which facilitates efficient charge transfer and increases the number of active sites. These findings highlight the potential of ALD-fabricated heterostructures in the development of advanced photocatalysts for sustainable energy production.

关键词： Titanium dioxide

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HomeOSD: Appliance Operating-Status Detection Using mmWave Radar

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SENSORS 2024年第9期24卷 2911页

作者： Sheng, Yinhe Li, Jiao Ma, Yongyu Zhang, Jin Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Dept Comp Sci & Engn Shenzhen Key Lab Safety & Secur Next Generat Ind I Shenzhen 518055 Peoples R China

Within the context of a smart home, detecting the operating status of appliances in the environment plays a pivotal role, estimating power consumption, issuing overuse reminders, and identifying faults. The traditional contact-based approaches require equipment updates such as incorporating smart sockets or high-precision electric meters. Non-constant approaches involve the use of technologies like laser and Ultra-Wideband (UWB) radar. The former can only monitor one appliance at a time, and the latter is unable to detect appliances with extremely tiny vibrations and tends to be susceptible to interference from human activities. To address these challenges, we introduce HomeOSD, an advanced appliance status-detection system that uses mmWave radar. This innovative solution simultaneously tracks multiple appliances without human activity interference by measuring their extremely tiny vibrations. To reduce interference from other moving objects, like people, we introduce a Vibration-Intensity Metric based on periodic signal characteristics. We present the Adaptive Weighted Minimum Distance Classifier (AWMDC) to counteract appliance vibration fluctuations. Finally, we develop a system using a common mmWave radar and carry out real-world experiments to evaluate HomeOSD's performance. The detection accuracy is 95.58%, and the promising results demonstrate the feasibility and reliability of our proposed system.

关键词： Internet of Things smart home appliance operating-status detection mmWave radar vibration

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Hierarchical Predictions of Fine-to-Coarse Time Span and Atmospheric Field Reconstruction for Typhoon Track Prediction

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ATMOSPHERE 2024年第5期15卷 605-605页

作者： Yan, Shengye Zhang, Zhendong Zheng, Wei Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Shenzhen Youjia Innov Tech Co Ltd Shenzhen 518000 Peoples R China

The prediction of typhoon tracks in the Northwest Pacific is key to reducing human casualties and property damage. Traditional numerical forecasting models often require substantial computational resources, are high-cost, and have significant limitations in prediction speed. This research is dedicated to using deep learning methods to address the shortcomings of traditional methods. Our method (AFR-SimVP) is based on a large-kernel convolutional spatio-temporal prediction network combined with multi-feature fusion for forecasting typhoon tracks in the Northwest Pacific. In order to more effectively suppress the effect of noise in the dataset to enhance the generalization ability of the model, we use a multi-branch structure, incorporate an atmospheric reconstruction subtask, and propose a second-order smoothing loss to further improve the prediction ability of the model. More importantly, we innovatively propose a multi-time-step typhoon prediction network (HTAFR-SimVP) that does not use the traditional recurrent neural network family of models at all. Instead, through fine-to-coarse hierarchical temporal feature extraction and dynamic self-distillation, multi-time-step prediction is achieved using only a single regression network. In addition, combined with atmospheric field reconstruction, the network achieves integrated prediction for multiple tasks, which greatly enhances the model's range of applications. Experiments show that our proposed network achieves optimal performance in the 24 h typhoon track prediction task. Our regression network outperforms previous recurrent network-based typhoon prediction models in the multi-time-step prediction task and also performs well in multiple integration tasks.

关键词： tropical cyclone tracking forecast hierarchical temporal feature atmospheric field reconstruction self-distillation

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