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
OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM drugs.METHODS:Generalized Boosted Models ...OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM drugs.METHODS:Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension(RH)patients.Furthermore,we used association analysis to explore the rules of"symptomsyndrome"and"symptom-herb"for the major influencing factors,in order to summarize prescription pattern and applicable patients of TCM.RESULTS:Patients with major adverse cardiac events mostly have complex symptoms of phlegm,stasis,deficiency and fire intermingled with each other,and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical treatment.CONCLUSIONS:Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.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
Today’s product creative design has rendered many fe atures and has brought a great change in our everyday life, there are many new c hallenges in its traditional theory and principle. According to the traditional de...Today’s product creative design has rendered many fe atures and has brought a great change in our everyday life, there are many new c hallenges in its traditional theory and principle. According to the traditional design theory, the FBS design model pays more attention to the function and stru cture of the product. But this model still couldn’t strengthen the relation bet ween product appearance design and human-machine design effectively. This paper adopt converse design thinking and presents an improved design thinking methodo logy based on C: FBS for product appearance design and give a general summarizat ion for the features, methods and technology based on human-machine interaction and interface. Meanwhile it also combines with the behavior design of product r elated IT fields and constructs a new outline to improve the design of product a ppearance supported by the technology of computer aided design. So the new metho d about design thinking for computer aided design, the new abstract product design model and the key problem of design thinking based on human-machine inte raction and interface are addressed in this paper. This kind of creative design theory that is driven by human-machine interaction and interface will help the development of CAD software system and the research of product design and manufa cture. Additionally, this paper gives some beneficial characters to address the theory based on human-machine interaction and interface. Meanwhile, combining with the developing of computer technology, the trends of design thinking based on t he technology of human-machine interaction and interface are also analyzed and discussed at the end of this paper.展开更多
Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study intr...Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.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
Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their mov...Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.展开更多
Virtual human is the simulation of human under the synthesis of virtual reality,artificial intelligence,and other technologies.Modern virtual human technology simulates both the external characteristics and the intern...Virtual human is the simulation of human under the synthesis of virtual reality,artificial intelligence,and other technologies.Modern virtual human technology simulates both the external characteristics and the internal emotions and personality of humans.The relationship between virtual human and human is a concrete expression of the modern symbiotic relationship between human and machine.This human-machine symbiosis can either be a fusion of the virtual human and the human or it can cause a split in the human itself.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
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
Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.How...Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.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
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
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the China Academy of Chinese Medical Sciences,Independent Topic Project:Application Research on Named Entity Recognition and Relationship Extraction of Case Records of Renowned Traditional Chinese Medicine Practitioners(No.Z0643).China Academy of Chinese Medical Sciences,Independent Topic Project:Analysis of Research Directions and Scope in the Discipline of Traditional Chinese Medicine Statistics(No.Z0723)China Academy of Chinese Medical Sciences,Science and Technology Innovation Project:Real-world Effectiveness Evaluation of Traditional Chinese Medicine and Translational Application Research on Causal Inference(No.CI2021A04706).China Academy of Chinese Medical Sciences,Science and Technology Innovation Project:Research on Causal Inference Methodology for Real-world Clinical Evaluation in Traditional Chinese Medicine(No.CI2021B003)National Key Research and Development Program of China:Integrated Evaluation Model and Key Technologies of"Syndrome-Disease-Prescription"for Traditional Chinese Medicine in the Prevention and Treatment of Coronary Heart Disease—Statistical Data Analysis and Data Mining(No.2017YFC1700406-2)。
文摘OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM drugs.METHODS:Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension(RH)patients.Furthermore,we used association analysis to explore the rules of"symptomsyndrome"and"symptom-herb"for the major influencing factors,in order to summarize prescription pattern and applicable patients of TCM.RESULTS:Patients with major adverse cardiac events mostly have complex symptoms of phlegm,stasis,deficiency and fire intermingled with each other,and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical treatment.CONCLUSIONS:Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.
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Today’s product creative design has rendered many fe atures and has brought a great change in our everyday life, there are many new c hallenges in its traditional theory and principle. According to the traditional design theory, the FBS design model pays more attention to the function and stru cture of the product. But this model still couldn’t strengthen the relation bet ween product appearance design and human-machine design effectively. This paper adopt converse design thinking and presents an improved design thinking methodo logy based on C: FBS for product appearance design and give a general summarizat ion for the features, methods and technology based on human-machine interaction and interface. Meanwhile it also combines with the behavior design of product r elated IT fields and constructs a new outline to improve the design of product a ppearance supported by the technology of computer aided design. So the new metho d about design thinking for computer aided design, the new abstract product design model and the key problem of design thinking based on human-machine inte raction and interface are addressed in this paper. This kind of creative design theory that is driven by human-machine interaction and interface will help the development of CAD software system and the research of product design and manufa cture. Additionally, this paper gives some beneficial characters to address the theory based on human-machine interaction and interface. Meanwhile, combining with the developing of computer technology, the trends of design thinking based on t he technology of human-machine interaction and interface are also analyzed and discussed at the end of this paper.
基金funded by the National Science and Technology Council,Taiwan(Grant No.NSTC 112-2121-M-039-001)by China Medical University(Grant No.CMU112-MF-79).
文摘Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.
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 Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2023-00218176)and the Soonchunhyang University Research Fund.
文摘Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.
文摘Virtual human is the simulation of human under the synthesis of virtual reality,artificial intelligence,and other technologies.Modern virtual human technology simulates both the external characteristics and the internal emotions and personality of humans.The relationship between virtual human and human is a concrete expression of the modern symbiotic relationship between human and machine.This human-machine symbiosis can either be a fusion of the virtual human and the human or it can cause a split in the human itself.
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.
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Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.
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.
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.
