Yu Xuan Yang - Academia.edu (original) (raw)

Papers by Yu Xuan Yang

Frontiers in Neuroscience

BackgroundUpper extremity dysfunction after stroke is an urgent clinical problem that greatly aff... more BackgroundUpper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As an emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals and provide feedback to form a closed-loop rehabilitation, which is currently being studied for functional restoration after stroke. However, there is no reliable medical evidence to support the effect of BMI-based training on upper extremity function after stroke. This review aimed to evaluate the efficacy and safety of BMI-based training for improving upper extremity function after stroke, as well as potential differences in efficacy of different external devices.MethodsEnglish-language literature published before April 1, 2022, was searched in five electronic databases using search terms including “brain-computer/machine interface”, “stroke” and “upper extremity.” The identified articles were screened, data were e...

Procedia CIRP

Shrouded blisks play an important role in improving the efficiency and reliability of rocket engi... more Shrouded blisks play an important role in improving the efficiency and reliability of rocket engines and aeroengines. With a ring on the top of the blades, the channel of an integral shrouded blisk is generally twisted and half-opened, which inevitably brings challenges to its machining. Considering the low machining efficiency of electric discharge machining, an electrode simplification method was proposed for shrouded blisk pre-rough machining, in which the simplified electrode feeds along a straight line and removes the unwanted material on the blisk as much as it can. According to the experimental results, the machining time reduced more than 30% by using the simplified electrode.

2020 IEEE 15th International Conference on Nano/Micro Engineered and Molecular System (NEMS)

We adopt a pseudo-module methodology for investigating the degradation behaviors of silver nanowi... more We adopt a pseudo-module methodology for investigating the degradation behaviors of silver nanowire (AgNW) transparent conductors (TC). The pseudo-module allows assembly and disassembly for accessing TC during aging test without causing artificial damage to the AgNWs. Aging tests on the AgNWs inside pseudo-module were performed under ultraviolet (UV) irradiation at elevated temperature and damp heat conditions. There was no deterioration concerning electrical DC conductivity for both UVA/75ºC and damp heat aging conditions after aging for 69 days and 100 days, respectively. However, microscopy and spectroscopy results indicated that damp heat aging resulted in significant damage to the morphologies of AgNWs, inferring the importance of encapsulation to the stability of pristine AgNWs inside modules. Morphological and junctional change of AgNWs networks caused the decrease in optical transmittance of TC. The UVA/75ºC aging results showed early sign of sulfidation after aging for 100 days. Electrical stress tests by applying stepwise current were conducted. The failure mode of both pristine AgNW TC and AgNW TC inside pseudo-module were identical. However, the AgNW TC inside pseudo-module generated higher heating temperature. Preliminary outdoor exposure has been conducted by our research team, and comprehensive investigation will be carried out in the near future.

Journal of Psychological Science, Jan 20, 2021

2019 IEEE International Conference on Prognostics and Health Management (ICPHM), 2019

Remaining useful life (RUL) estimation is a key technology in prognostics and health management (... more Remaining useful life (RUL) estimation is a key technology in prognostics and health management (PHM). Considering the problem that operating condition (OC) is easily overlooked and sample fusion is mainly determined by experience, this paper presents an OC-matched similarity method with dynamic sample fusion. The method contains two main stages, including training stage for obtaining the library of OC-based degradation models and the trained parameter for dynamic sample fusion, and testing stage for RUL prediction. In the training stage, we extract the sensor data on account of the linear correlation coefficient and expand the library of degradation models through adding OC information. Then, cross-validation is implemented to train the parameter for dynamic sample fusion and parameter is optimally selected by minimizing the target function. When estimating RUL of test data, OC-matched similarity is measured by calculating the distance between test data and OC-matched model. Eventually, RUL is estimated by the weighted average of each sample based on the similarity measurement. This method is validated by the 2008 PHM Conference Challenge Data, which contains both sensor measurements and operating settings. The results have suggested significant improvement comparing with traditional similarity method.

International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021), 2022

With the vigorous promotion of urban energy internet and smart grid, the risk of various maliciou... more With the vigorous promotion of urban energy internet and smart grid, the risk of various malicious cyber attacks on existing cyber physics power systems has increased significantly after they are transformed into integrated energy cyber physics systems（IEGS）. In order to ensure the safe and reliable operation of urban integrated electricity-gas system, this paper proposes a loss assessment model for integrated electricity-gas system under cyber-physical coordinated attack. Firstly, the branch fault scenario is randomly generated, and then the real operation state of the branch is concealed by injecting false data. Secondly, the DC power flow model and probability model are used to simulate the cascading failure of the power system, and the loss caused by the coordinated attack on the urban integrated electricity-gas system is evaluated according to the physical operation characteristics of IEGS, the IEGS vulnerability branch is evaluated. Finally, the correctness and effectiveness of the model are verified by the integrated electricity-gas system composed of IEEE 30 nodes and Belgium 20 nodes.

International Conference on High Performance Computing and Communication (HPCCE 2021), 2022

With the explosive growth of mobile communication information brought about by the maturity of 5G... more With the explosive growth of mobile communication information brought about by the maturity of 5G commercial use, the energy consumption caused by grid communication is increasing. Promoting the emission reduction and intelligentization of electric energy and communication network is the key to the current grid construction. The impact of dispatched multi-core and multi-chip power terminals is prone to problems such as increased load in local areas and data penetration. Therefore, it is necessary to reduce current energy consumption and optimize power emissions. This paper analyzes the current problems of power grid energy consumption, and puts forward corresponding suggestions on reducing energy consumption, and provides certain ideas for constructing an effective balance between power supply and demand interaction and energy optimization.

Geofluids, 2021

It is an important problem in the mine water disaster prevention and control to control the large... more It is an important problem in the mine water disaster prevention and control to control the large passage moving water. Traditional grouting technology is to put coarse aggregate and fine aggregate downward first and then grouting treatment. But the aggregate and cement flow distance is long, consumption is large, cost is high, and easy to appear secondary water inrush. Centering on the technical difficulties in the rapid construction of the blocking body of the moving water passage, a water-blocking textile bag was invented. The purpose of blocking the tunnel water inrush was achieved by grouting inside the bag body, which fundamentally realized the rapid blocking of the large passage through water under the condition of moving water. However, the mechanism, water plugging law, and design parameters of water blocking roadway with textile bag are still unclear. In this paper, the slip law and stability of the textile bag in the moving water and the deformation characteristics caused...

AIAA Scitech 2019 Forum, 2019

Inorganic Chemistry Communications, 2021

Abstract A series of novel coumarin-based chemosensor azine derivatives L1, L2 and L3 were synthe... more Abstract A series of novel coumarin-based chemosensor azine derivatives L1, L2 and L3 were synthesized and characterized by various spectroscopic methods such as 1H NMR, mass spectrometry, FT-IR and elemental analysis. The sensing property of the sensor L derivatives was confirmed by the UV-Vis absorption, emission spectra and the naked eye sensing. The sensor L derivatives showed the absorption band at 320-330 nm. The fluorescence emission band was observed at 550-560 nm. In the presence of CN– ions, the chemosensor L derivatives show the “turn-on” fluorescence response over the other competing anions such as Br–, I–, HSO4–, ClO4– and PF6–, and the new absorption band appeared at 385 nm and the emission band also shifted to the blue region at 440 nm. The sensor L derivatives bind to cyanide ions in a 1:1 binding stoichiometry calculated from the job’s plot experiments. The detection limit of sensor L1 towards CN– was 5.79 x 10-8 M. Additionally, the binding constant was determined to be 1.0209 x 106 M-1 from the Benesi-Heilbrand equation. The theoretical calculations were performed by Gaussian 9 software. The sensing mechanism of the interaction between the cyanide ion and imine carbon was confirmed by the 1H-NMR titration method and mass spectra.

Building Simulation, 2020

Large metro transfer stations have been widely constructed in China, among which the double-islan... more Large metro transfer stations have been widely constructed in China, among which the double-island station faces the serious fire safety issues owing to its large passenger flow. In this paper, simulation cases were carried out to investigate the effectiveness of different ventilation modes by jointly operating tunnel ventilation fan (TVF) and platform screen doors (PSD) under two typical fire scenarios in the platform. The numerical model was established by Fire Dynamics Simulator software and verified via reduced-scale model experiments. The results indicate that the TVF mode of supplying at the end near fire and exhausting at the other end is superior to that of exhausting at both ends. Besides, activating more PSD and TVF on the both sides of platform will restrict smoke in one end to the greater extent. During a fire in the middle of the platform, opening all PSD near tunnel-2 and TVF in tunnel-2 and tunnel-3 is the most appropriate mode. While during a fire at the left end of the platform, activating all PSD and TVF on both sides is the optimal operation mode. The conclusions can provide guidance for smoke control design and on-site emergency ventilation operation in double-island platform fire.

Physica A: Statistical Mechanics and its Applications, 2021

Abstract Epilepsy is one of the most common brain diseases, and seizures usually occur randomly. ... more Abstract Epilepsy is one of the most common brain diseases, and seizures usually occur randomly. Accurately predicting seizures enable doctors and patients to carry out medical prevention timely. In seizure prediction studies, single-domain information input (time domain or frequency domain, Etc.) neglects some parts’ information from signals. In this paper, we propose a novel deep learning framework named channel attention dual-input convolutional neural network (CADCNN) to obtain the signal’s useful information fully. The spatial–temporal features extracted by short-time Fourier transform (STFT) are fed to the CADCNN, and the raw EEG signals are fed for further feature extraction. With the fusion of two inputs from different domains and the combination of channel attention, CADCNN can learn faithful and distinguishable representations of EEG signals and boost the temporal, spectrum, and spatial information utilization capability. We evaluate the proposed method using the Boston Children’s Hospital-MIT scalp EEG public datasets. Compared with other state-of-the-art methods, the sensitivity, false prediction rate, specificity, and AUC of our proposed method reach 97.1%, 0.029h, 95.6%, and 0.917, respectively, presenting better performance and higher prediction accuracy.

International Journal of Bifurcation and Chaos, 2020

Driver fatigue has caused numerous vehicle crashes and traffic injuries. Exploring the fatigue me... more Driver fatigue has caused numerous vehicle crashes and traffic injuries. Exploring the fatigue mechanism and detecting fatigue state are of great significance for preventing traffic accidents, and further lessening economic and societal loss. Due to the objectivity of EEG signals and the availability of EEG acquisition equipment, EEG-based fatigue detection task has raised great attention in recent years. Although there exist various methods for this task, the study of fatigue mechanism and detection of fatigue state still remain much to be explored. To investigate these problems, a multivariate weighted ordinal pattern transition (MWOPT) network is proposed in this paper. To be specific, a simulated driving experiment was first conducted to obtain the EEG signals of subjects in alert state and fatigue state respectively. Then the MWOPT network is constructed based on a novel Shannon entropy. To probe into the mechanism underlying fatigue behavior, the small-worldness index is extra...

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019

Driver fatigue is an important cause of traffic accidents, which has triggered great concern for ... more Driver fatigue is an important cause of traffic accidents, which has triggered great concern for detecting drivers’ fatigue. Numerous methods have been proposed to fulﬁll this challenging task, including feature methods and machine learning methods. Recently, with the development of deep learning techniques, many studies achieved better results than traditional feature methods, and the combination of traditional methods and deep learning techniques gradually received attention. In this paper, we propose a recurrence network-based convolutional neural network (RN-CNN) method to detect fatigue driving. To be specific, we first conduct a simulated driving experiment to collect electroencephalogram (EEG) signals of subjects under alert state and fatigue state. Then, we construct the multiplex recurrence network (RN) from EEG signals to fuse information from the original time series. Finally, CNN is employed to extract and learn the features of a multiplex RN for realizing a classificati...

IEEE Transactions on Industrial Informatics, 2019

Electroencephalogram (EEG), obtained by wearable devices, can realize effective human health moni... more Electroencephalogram (EEG), obtained by wearable devices, can realize effective human health monitoring. Traditional methods based on artificially-designed features have achieved valid results in EEG-based recognition, and numerous studies start to apply deep learning techniques in this area. In this paper, we propose a coincidence filtering-based method to build a connection between artificial features-based methods and convolutional neural networks (CNNs), and design CNNs through simulating the information extraction pattern of artificial features-based methods. Based on this method, we propose a novel, simple, and effective CNNs structure for EEG-based classification. We implement two experiments to obtain EEG data, and perform experiments based on the two health monitoring tasks. The results illustrate that the proposed network can achieve a prominent average accuracy on the emotion recognition and fatigue driving detection task. Due to its generality, the proposed framework design of CNNs is expected to be useful for broader applications in health monitoring areas.

Neurocomputing, 2019

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Physica A: Statistical Mechanics and its Applications, 2018

Brain-computer interface (BCI) enables users to interact with the environment without relying on ... more Brain-computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human-machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical 'Oddball' paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2018

Smart home has been widely used to improve the living quality of people. Recently, the brain-comp... more Smart home has been widely used to improve the living quality of people. Recently, the brain-computer interface (BCI) contributes greatly to the smart home system. We design a BCI-based smart home system, in which the event-related potentials (ERP) are induced by the image interface based on the oddball paradigm. Then, we investigate the influence of mental fatigue on the ERP classification by the Fisher linear discriminant analysis. The results indicate that the classification accuracy of ERP decreases as the brain evolves from the normal stage to the mental fatigue stage. In order to probe into the difference of the brain, cognitive process between mental fatigue and normal states, we construct multivariate weighted recurrence networks and analyze the variation of the weighted clustering coefficient and weighted global efficiency corresponding to these two brain states. The findings suggest that these two network metrics allow distinguishing normal and mental fatigue states and yi...