Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this proc...Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this process,molecular dynamics(MD)simulations can reveal the microscopic mechanisms of battery processes,thereby boosting the design of batteries.Compared to other MD simulation techniques,the machine learning force field(MLFF)holds the advantages of first-principles accuracy along with large spatial and temporal scale,offering opportunities to uncover new mechanisms in battery systems.This review presents a detailed overview of the fundamental principles and model types of MLFFs,as well as their applications in simulating the structure,transport properties,and chemical reaction properties of bulk battery materials and interfaces.Notably,we emphasize the long-range interaction corrections and constant-potential methods in the model design of MLFFs.Finally,we discuss the challenges and prospects of applying MLFF models in the research of batteries.展开更多
The formation of interphase layers,including the cathode-electrolyte interphase(CEI)and solidelectrolyte interphase(SEI),exhibits significant chemical complexity and plays a pivotal role in determining the performance...The formation of interphase layers,including the cathode-electrolyte interphase(CEI)and solidelectrolyte interphase(SEI),exhibits significant chemical complexity and plays a pivotal role in determining the performance of lithium batteries.Despite considerable advances in simulating the bulk phase properties of battery materials,the understanding of interfaces,including crystalline interfaces that represent the simplest case,remains limited.This is primarily due to challenges in performing ground-state searches for interface microstructures and the high computational costs associated with first-principles methods.Herein,we introduce InterOptimus,an automated workflow designed to efficiently search for ground-state heterogeneous interfaces.InterOptimus incorporates a rigorous,symmetry-aware equivalence analysis for lattice matching and termination scanning.Additionally,it introduces stereographic projection as an intuitive and comprehensive framework for visualizing and classifying interface structures.By integrating universal machine learning interatomic potentials(MLIPs),InterOptimus enables rapid predictions of interface energy and stability,significantly reducing the necessary computational cost in density functional theory(DFT)by over 90%.We benchmarked several MLIPs at three critical lithium battery interfaces,Li_(2)S|Ni_(3)S_(2),LiF|NCM,and Li_(3)PS_(4)|Li,and demonstrated that the MLIPs achieve accuracy comparable to DFT in modeling potential energy surfaces and ranking interface stabilities.Thus,InterOptimus facilitates the efficient determination of ground-state heterogeneous interface structures and subsequent studies of structure-property relationships,accelerating the interface engineering of novel battery materials.展开更多
Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an...Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an early detection approach to mitigate such threats by identifying ransomware activity before the encryption process begins.The approach employs a two-tiered approach:a signature-based method using hashing techniques to match known threats and a dynamic behavior-based analysis leveraging Cuckoo Sandbox and machine learning algorithms.A critical feature is the integration of the most effective Application Programming Interface call monitoring,which analyzes system-level interactions such as file encryption,key generation,and registry modifications.This enables the detection of both known and zero-day ransomware variants,overcoming limitations of traditional methods.The proposed technique was evaluated using classifiers such as Random Forest,Support Vector Machine,and K-Nearest Neighbors,achieving a detection accuracy of 98%based on 26 key ransomware attributes with an 80:20 training-to-testing ratio and 10-fold cross-validation.By combining minimal feature sets with robust behavioral analysis,the proposed method outperforms existing solutions and addresses current challenges in ransomware detection,thereby enhancing cybersecurity resilience.展开更多
Interface engineering is proved to be the most important strategy to push the device performance of the perovskite solar cell(PSC) to its limit, and numerous works have been conducted to screen efficient materials. He...Interface engineering is proved to be the most important strategy to push the device performance of the perovskite solar cell(PSC) to its limit, and numerous works have been conducted to screen efficient materials. Here, on the basis of the previous studies, we employ machine learning to map the relationship between the interface material and the device performance, leading to intelligently screening interface materials towards minimizing voltage losses in p-i-n type PSCs. To enhance the explainability of the machine learning models, molecular descriptors are used to represent the materials. Furthermore,experimental analysis with different characterization methods and device simulation based on the drift-diffusion physical model are conducted to get physical insights and validate the machine learning models. Accordingly, 3-thiophene ethylamine hydrochloride(Th EACl) is screened as an example, which enables remarkable improvements in VOCand PCE of the PSCs. Our work reveals the critical role of datadriven analysis in the high throughput screening of interface materials, which will significantly accelerate the exploration of new materials for high-efficiency PSCs.展开更多
The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-c...The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-computer interface(BCI)to awareness detection with a passive auditory stimulation paradigm.12 subjects with normal hearing were invited to collect electroencephalogram(EEG)based on a BCI communication system,in which EEG signals are transmitted wirelessly.After necessary preprocessing,RBF-SVM and EEGNet were used for algorithm realization and analysis.For a single subject,RBF-SVM can distinguish his(her)name stimuli awareness with classification accuracies ranging from 60-95%.EEGNet was used to learn all subjects'data and improved accuracy to 78.04%for characteristics finding and model generalization.Moreover,we completed the supplementary analysis work from the time domain and time-frequency domain.This study applied BCI communication to human awareness detection,proposed a passive auditory paradigm,and proved the effectiveness,which could be an inspiration for brain,mental or physical diseases diagnosis and detection.展开更多
GaP has been shown to be a promising photoelectrocatalyst for selective CO_(2)reduction to methanol.Due to the relevance of the interface structure to important processes such as electron/proton transfer,a detailed un...GaP has been shown to be a promising photoelectrocatalyst for selective CO_(2)reduction to methanol.Due to the relevance of the interface structure to important processes such as electron/proton transfer,a detailed understanding of the GaP(110)-water interfacial structure is of great importance.Ab initio molecular dynamics(AIMD)can be used for obtaining the microscopic information of the interfacial structure.However,the GaP(110)-water interface cannot converge to an equilibrated structure at the time scale of the AIMD simulation.In this work,we perform the machine learning accelerated molecular dynamics(MLMD)to overcome the difficulty of insufficient sampling by AIMD.With the help of MLMD,we unravel the microscopic information of the structure of the GaP(110)-water interface,and obtain a deeper understanding of the mechanisms of proton transfer at the GaP(110)-water interface,which will pave the way for gaining valuable insights into photoelectrocatalytic mechanisms and improving the performance of photoelectrochemical cells.展开更多
This paper describes the innovation schemes of the interface of a CNC machine which cannot communicate with a computer by a Direct Numerical Control(DNC)interface and the functions of a DNC interface system.One archit...This paper describes the innovation schemes of the interface of a CNC machine which cannot communicate with a computer by a Direct Numerical Control(DNC)interface and the functions of a DNC interface system.One architecture of hardware and software of a practi- cal single-chip computer based on DNC interface system developed by the authors is given. Without any change of the original hardware and software,this DNC interface system has been used to innovate the CNC machine's interface to implement the direct communication between a computer and a CNC machine and to achieve no tape transmission of a part program and ma- chine parameters.It has been demonstrated that this DNC interface system has certain practical values in improving the reliability,efficiency and production management of CNC/NC machine.展开更多
We outline problems and potential solutions for feasible human-machine interfaces using cable-based parallel manipulators for physiotherapy applications.From an engineering perspective,we discuss the design constraint...We outline problems and potential solutions for feasible human-machine interfaces using cable-based parallel manipulators for physiotherapy applications.From an engineering perspective,we discuss the design constraints related to acceptance by patients and physiotherapist users.To date,most designs have focused on mobile platforms that are designed to be operated as an end-effector connected to human limbs for direct patient interaction.Some specific examples are illustrated from the authors' experience with prototypes available at Laboratory of Robotics and Mechatronics (LARM),Italy.展开更多
With the introduction of high-speed trains into chinese railway system, closeattention should be paid to the aspects of safety in hish-speed railways. Thereare many interfaces which are very important and directly rel...With the introduction of high-speed trains into chinese railway system, closeattention should be paid to the aspects of safety in hish-speed railways. Thereare many interfaces which are very important and directly related to drivmgsafety. This paper focuses on features of design and analyses the principles ofsafety.展开更多
As agricultural machines become more complex, it is increasingly critical that special attention be directed to the design of the user interface to ensure that the operator will have an adequate understanding of the s...As agricultural machines become more complex, it is increasingly critical that special attention be directed to the design of the user interface to ensure that the operator will have an adequate understanding of the status of the machine at all times. A user-centred design focus was employed to develop two conceptual designs (UCD1 & UCD2) for a user interface for an agricultural air seeder. The two concepts were compared against an existing user interface (baseline condition) using the metrics of situation awareness (Situation Awareness Global Assessment Technique), mental workload (Integrated Workload Scale), reaction time, and subjective feedback. There were no statistically significant differences among the three user interfaces based on the metric of situation awareness;however, UCD2 was deemed to be significantly better than either UCD1 or the baseline interface on the basis of mental workload, reaction time and subjective feedback. The research has demonstrated that a user-centred design focus will generate a better user interface for an agricultural machine.展开更多
The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are res...The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the issue.We classified these algorithms into two categories,namely,and enhancing the representation and expanding the training set.Specifically,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so on.The methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG data.The result of these techniques showed the resolution of the challenges to some extent.As a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.展开更多
The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corros...The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.展开更多
The bioinspired nacre or bone structure represents a remarkable example of tough,strong,lightweight,and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performa...The bioinspired nacre or bone structure represents a remarkable example of tough,strong,lightweight,and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials.The bioinspired structure consists of hard grains and soft material interfaces.While the material interface has a very low volume percentage,its property has the ability to determine the bulk material response.Machine learning technology nowadays is widely used in material science.A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite.This model was trained on a comprehensive dataset of material response and interface properties,allowing it to make accurate predictions.The results of this study demonstrate the efficiency and high accuracy of the machine learning model.The successful application of machine learning into the material property prediction process has the potential to greatly enhance both the efficiency and accuracy of the material design process.展开更多
Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model t...Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training data.In recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model.This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI.In addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer learning.Furthermore,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,etc.At the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.展开更多
In this paper, a vibration motion control is proposed and implemented on a foamed polystyrene machining robot to suppress the generation of undesirable cusp marks, and the basic performance of the controller is verifi...In this paper, a vibration motion control is proposed and implemented on a foamed polystyrene machining robot to suppress the generation of undesirable cusp marks, and the basic performance of the controller is verified through machining experiments of foamed polystyrene. Then, a 3 dimensional （3D） printer-like data interface is proposed for the machining robot. The 3D data inter- face enables to control the machining robot directly using stereolithography （STL） data without conducting any computer-aided man- ufacturing （CAM） process. This is done by developing a robotic preprocessor that helps to remove the need for the conventional CAM process by directly converting the STL data into cutter location source data called cutter location （CL） or cutter location source （CLS） data. The STL is a file format proposed by 3D systems, and recently is supported by many computer aided design （CAD）/CAM soft- waxes. The STL is widely used for rapid prototyping with a 3D printer which is a typical additive manufacturing system. The STL deals with a triangular representation of a curved surface geometry. The developed 3D printer-like data interface allows to directly control the machining robot through a zigzag path, rectangular spiral path and circular spiral path generated according to the information included in STL data. The effectiveness and usefulness of the developed system are demonstrated through actual machining experiments.展开更多
In order to study the variation of machine tools’dynamic characteristics in the manufacturing space,a Kriging approximate model is proposed.Finite element method(FEM)is employed on the platform of ANSYS to establish ...In order to study the variation of machine tools’dynamic characteristics in the manufacturing space,a Kriging approximate model is proposed.Finite element method(FEM)is employed on the platform of ANSYS to establish finite element(FE)model with the dynamic characteristic of combined interface for a milling machine,which is newly designed for producing aero engine blades by a certain enterprise group in China.The stiffness and damping of combined interfaces are adjusted by using adaptive simulated annealing algorithm with the optimizing software of iSIGHT in the process of FE model update according to experimental modal analysis(EMA)results.The Kriging approximate model is established according to the finite element analysis results utilizing orthogonal design samples by taking into account of the range of configuration parameters.On the basis of the Kriging approximate model,the response surfaces between key response parameter and configuration parameters are obtained.The results indicate that configuration parameters have great effects on dynamic characteristics of machine tools,and the Kriging approximate model is an effective and rapid method for estimating dynamic characteristics of machine tools in the manufacturing space.展开更多
Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common d...Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.展开更多
Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which ...Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.展开更多
Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discr...Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.展开更多
基金funding support from the National Natural Science Foundation of China(92472109,T2325012)the Program for HUST Academic Frontier Youth Team+1 种基金support from the Fundamental Research Funds for the Central Universities(HUST,5003120083)supported by the Postdoctoral Fellowship Program of CPSF(GZC20240532)。
文摘Batteries play a critical role in electric vehicles and distributed energy generation.With the growing demand for energy storage solutions,new battery materials and systems are continually being developed.In this process,molecular dynamics(MD)simulations can reveal the microscopic mechanisms of battery processes,thereby boosting the design of batteries.Compared to other MD simulation techniques,the machine learning force field(MLFF)holds the advantages of first-principles accuracy along with large spatial and temporal scale,offering opportunities to uncover new mechanisms in battery systems.This review presents a detailed overview of the fundamental principles and model types of MLFFs,as well as their applications in simulating the structure,transport properties,and chemical reaction properties of bulk battery materials and interfaces.Notably,we emphasize the long-range interaction corrections and constant-potential methods in the model design of MLFFs.Finally,we discuss the challenges and prospects of applying MLFF models in the research of batteries.
基金supported by the National Natural Science Foundation of China(92470110)the Special Funds for the Development of Strategic Emerging Industries in Shenzhen(XMHT20240108008)the Shenzhen Stable Support Program for Higher Education Institutions(WDZC20231126215806001)。
文摘The formation of interphase layers,including the cathode-electrolyte interphase(CEI)and solidelectrolyte interphase(SEI),exhibits significant chemical complexity and plays a pivotal role in determining the performance of lithium batteries.Despite considerable advances in simulating the bulk phase properties of battery materials,the understanding of interfaces,including crystalline interfaces that represent the simplest case,remains limited.This is primarily due to challenges in performing ground-state searches for interface microstructures and the high computational costs associated with first-principles methods.Herein,we introduce InterOptimus,an automated workflow designed to efficiently search for ground-state heterogeneous interfaces.InterOptimus incorporates a rigorous,symmetry-aware equivalence analysis for lattice matching and termination scanning.Additionally,it introduces stereographic projection as an intuitive and comprehensive framework for visualizing and classifying interface structures.By integrating universal machine learning interatomic potentials(MLIPs),InterOptimus enables rapid predictions of interface energy and stability,significantly reducing the necessary computational cost in density functional theory(DFT)by over 90%.We benchmarked several MLIPs at three critical lithium battery interfaces,Li_(2)S|Ni_(3)S_(2),LiF|NCM,and Li_(3)PS_(4)|Li,and demonstrated that the MLIPs achieve accuracy comparable to DFT in modeling potential energy surfaces and ranking interface stabilities.Thus,InterOptimus facilitates the efficient determination of ground-state heterogeneous interface structures and subsequent studies of structure-property relationships,accelerating the interface engineering of novel battery materials.
基金funded by the National University of Sciences and Technology(NUST)supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(2021R1IIA3049788).
文摘Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an early detection approach to mitigate such threats by identifying ransomware activity before the encryption process begins.The approach employs a two-tiered approach:a signature-based method using hashing techniques to match known threats and a dynamic behavior-based analysis leveraging Cuckoo Sandbox and machine learning algorithms.A critical feature is the integration of the most effective Application Programming Interface call monitoring,which analyzes system-level interactions such as file encryption,key generation,and registry modifications.This enables the detection of both known and zero-day ransomware variants,overcoming limitations of traditional methods.The proposed technique was evaluated using classifiers such as Random Forest,Support Vector Machine,and K-Nearest Neighbors,achieving a detection accuracy of 98%based on 26 key ransomware attributes with an 80:20 training-to-testing ratio and 10-fold cross-validation.By combining minimal feature sets with robust behavioral analysis,the proposed method outperforms existing solutions and addresses current challenges in ransomware detection,thereby enhancing cybersecurity resilience.
基金supported by the National Natural Science Foundation of China (62075006)the National Key R&D Program of China (2018YFB1500200)。
文摘Interface engineering is proved to be the most important strategy to push the device performance of the perovskite solar cell(PSC) to its limit, and numerous works have been conducted to screen efficient materials. Here, on the basis of the previous studies, we employ machine learning to map the relationship between the interface material and the device performance, leading to intelligently screening interface materials towards minimizing voltage losses in p-i-n type PSCs. To enhance the explainability of the machine learning models, molecular descriptors are used to represent the materials. Furthermore,experimental analysis with different characterization methods and device simulation based on the drift-diffusion physical model are conducted to get physical insights and validate the machine learning models. Accordingly, 3-thiophene ethylamine hydrochloride(Th EACl) is screened as an example, which enables remarkable improvements in VOCand PCE of the PSCs. Our work reveals the critical role of datadriven analysis in the high throughput screening of interface materials, which will significantly accelerate the exploration of new materials for high-efficiency PSCs.
基金supported by the Science and Technology Commission of Shanghai Municipality(STCSM)Research Fund(21JC1405300)to Fan Minthe National Key Research and Development Program of China(2018YFC0831102)sponsored by the Shanghai Key Research Laboratory of NSAI。
文摘The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-computer interface(BCI)to awareness detection with a passive auditory stimulation paradigm.12 subjects with normal hearing were invited to collect electroencephalogram(EEG)based on a BCI communication system,in which EEG signals are transmitted wirelessly.After necessary preprocessing,RBF-SVM and EEGNet were used for algorithm realization and analysis.For a single subject,RBF-SVM can distinguish his(her)name stimuli awareness with classification accuracies ranging from 60-95%.EEGNet was used to learn all subjects'data and improved accuracy to 78.04%for characteristics finding and model generalization.Moreover,we completed the supplementary analysis work from the time domain and time-frequency domain.This study applied BCI communication to human awareness detection,proposed a passive auditory paradigm,and proved the effectiveness,which could be an inspiration for brain,mental or physical diseases diagnosis and detection.
基金the National Natural Science Foundation of China(22225302,21991151,21991150,22021001,92161113,91945301)the Fundamental Research Funds for the Central Universities(20720220009)+1 种基金the China Postdoctoral Science Foundation(2020 M682079)the Guangdong Basic and Applied Basic Research Foundation(2020A1515110539)。
文摘GaP has been shown to be a promising photoelectrocatalyst for selective CO_(2)reduction to methanol.Due to the relevance of the interface structure to important processes such as electron/proton transfer,a detailed understanding of the GaP(110)-water interfacial structure is of great importance.Ab initio molecular dynamics(AIMD)can be used for obtaining the microscopic information of the interfacial structure.However,the GaP(110)-water interface cannot converge to an equilibrated structure at the time scale of the AIMD simulation.In this work,we perform the machine learning accelerated molecular dynamics(MLMD)to overcome the difficulty of insufficient sampling by AIMD.With the help of MLMD,we unravel the microscopic information of the structure of the GaP(110)-water interface,and obtain a deeper understanding of the mechanisms of proton transfer at the GaP(110)-water interface,which will pave the way for gaining valuable insights into photoelectrocatalytic mechanisms and improving the performance of photoelectrochemical cells.
文摘This paper describes the innovation schemes of the interface of a CNC machine which cannot communicate with a computer by a Direct Numerical Control(DNC)interface and the functions of a DNC interface system.One architecture of hardware and software of a practi- cal single-chip computer based on DNC interface system developed by the authors is given. Without any change of the original hardware and software,this DNC interface system has been used to innovate the CNC machine's interface to implement the direct communication between a computer and a CNC machine and to achieve no tape transmission of a part program and ma- chine parameters.It has been demonstrated that this DNC interface system has certain practical values in improving the reliability,efficiency and production management of CNC/NC machine.
基金supported by the research project RORAS 2 of the Mediterranean Program funded by INRIA,France
文摘We outline problems and potential solutions for feasible human-machine interfaces using cable-based parallel manipulators for physiotherapy applications.From an engineering perspective,we discuss the design constraints related to acceptance by patients and physiotherapist users.To date,most designs have focused on mobile platforms that are designed to be operated as an end-effector connected to human limbs for direct patient interaction.Some specific examples are illustrated from the authors' experience with prototypes available at Laboratory of Robotics and Mechatronics (LARM),Italy.
文摘With the introduction of high-speed trains into chinese railway system, closeattention should be paid to the aspects of safety in hish-speed railways. Thereare many interfaces which are very important and directly related to drivmgsafety. This paper focuses on features of design and analyses the principles ofsafety.
文摘As agricultural machines become more complex, it is increasingly critical that special attention be directed to the design of the user interface to ensure that the operator will have an adequate understanding of the status of the machine at all times. A user-centred design focus was employed to develop two conceptual designs (UCD1 & UCD2) for a user interface for an agricultural air seeder. The two concepts were compared against an existing user interface (baseline condition) using the metrics of situation awareness (Situation Awareness Global Assessment Technique), mental workload (Integrated Workload Scale), reaction time, and subjective feedback. There were no statistically significant differences among the three user interfaces based on the metric of situation awareness;however, UCD2 was deemed to be significantly better than either UCD1 or the baseline interface on the basis of mental workload, reaction time and subjective feedback. The research has demonstrated that a user-centred design focus will generate a better user interface for an agricultural machine.
文摘The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the issue.We classified these algorithms into two categories,namely,and enhancing the representation and expanding the training set.Specifically,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so on.The methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG data.The result of these techniques showed the resolution of the challenges to some extent.As a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.
文摘The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.
文摘The bioinspired nacre or bone structure represents a remarkable example of tough,strong,lightweight,and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials.The bioinspired structure consists of hard grains and soft material interfaces.While the material interface has a very low volume percentage,its property has the ability to determine the bulk material response.Machine learning technology nowadays is widely used in material science.A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite.This model was trained on a comprehensive dataset of material response and interface properties,allowing it to make accurate predictions.The results of this study demonstrate the efficiency and high accuracy of the machine learning model.The successful application of machine learning into the material property prediction process has the potential to greatly enhance both the efficiency and accuracy of the material design process.
基金the National Natural Science Foundation of China(Nos.11832003 and 81471770)the Natural Science Foundation of Beijing(No.4182009)。
文摘Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training data.In recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model.This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI.In addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer learning.Furthermore,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,etc.At the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.
基金supported by the Japam Society for the Promotion of Science(JSPS)KAKENHI(Nos.25420232 and 16K06203)
文摘In this paper, a vibration motion control is proposed and implemented on a foamed polystyrene machining robot to suppress the generation of undesirable cusp marks, and the basic performance of the controller is verified through machining experiments of foamed polystyrene. Then, a 3 dimensional （3D） printer-like data interface is proposed for the machining robot. The 3D data inter- face enables to control the machining robot directly using stereolithography （STL） data without conducting any computer-aided man- ufacturing （CAM） process. This is done by developing a robotic preprocessor that helps to remove the need for the conventional CAM process by directly converting the STL data into cutter location source data called cutter location （CL） or cutter location source （CLS） data. The STL is a file format proposed by 3D systems, and recently is supported by many computer aided design （CAD）/CAM soft- waxes. The STL is widely used for rapid prototyping with a 3D printer which is a typical additive manufacturing system. The STL deals with a triangular representation of a curved surface geometry. The developed 3D printer-like data interface allows to directly control the machining robot through a zigzag path, rectangular spiral path and circular spiral path generated according to the information included in STL data. The effectiveness and usefulness of the developed system are demonstrated through actual machining experiments.
基金Project(2009ZX04001-073)supported by the Important National Science&Technology Specific Projects of ChinaProject(51105025)supported by the National Natural Science Foundation of China
文摘In order to study the variation of machine tools’dynamic characteristics in the manufacturing space,a Kriging approximate model is proposed.Finite element method(FEM)is employed on the platform of ANSYS to establish finite element(FE)model with the dynamic characteristic of combined interface for a milling machine,which is newly designed for producing aero engine blades by a certain enterprise group in China.The stiffness and damping of combined interfaces are adjusted by using adaptive simulated annealing algorithm with the optimizing software of iSIGHT in the process of FE model update according to experimental modal analysis(EMA)results.The Kriging approximate model is established according to the finite element analysis results utilizing orthogonal design samples by taking into account of the range of configuration parameters.On the basis of the Kriging approximate model,the response surfaces between key response parameter and configuration parameters are obtained.The results indicate that configuration parameters have great effects on dynamic characteristics of machine tools,and the Kriging approximate model is an effective and rapid method for estimating dynamic characteristics of machine tools in the manufacturing space.
基金supported by National Natural Science Foundation of China(52202117,52232006,52072029,and 12102256)Collaborative Innovation Platform Project of Fu-Xia-Quan National Independent Innovation Demonstration Zone(3502ZCQXT2022005)+3 种基金Natural Science Foundation of Fujian Province of China(2022J01065)State Key Lab of Advanced Metals and Materials(2022-Z09)Fundamental Research Funds for the Central Universities(20720220075)the Ministry of Education,Singapore,under its MOE ARF Tier 2(MOE2019-T2-2-179).
文摘Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.
文摘Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.
基金supported by the National Natural Science Foundation of China under Grant No. 30525030, 60701015, and 60736029.
文摘Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.
