Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability o...Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability of these robots.Therefore,this study proposes a variable stiffness humancomputer interaction contact unit module(VSHCUM)based on the granular jamming mechanism.It is characterized by a double-layer chamber structure:the inner layer is a granular chamber,and the outer layer is an air chamber.The interaction force is transmitted by embedding a rigid support in the inner layer.Unlike the common flexible-belt interactive contact unit,when the exoskeleton is bound to the patient's limb,vSHCUM can realize adaptive fitting of the patient's limb shape using the pressure change in the double-chamber structure.Simultaneously,by adjusting the vacuum level of the granular chamber,the stiffness of the interactive contact unit can be adjusted by a factor of more than five,and the internal work loss caused by self-pulling deformation during the auxiliary force transfer process can be reduced.展开更多
In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operati...In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.展开更多
With the rapid development of artificial intelligence technology,AIGC(Artificial Intelligence-Generated Content)has triggered profound changes in the field of high-level language programming courses.This paper deeply ...With the rapid development of artificial intelligence technology,AIGC(Artificial Intelligence-Generated Content)has triggered profound changes in the field of high-level language programming courses.This paper deeply explored the application principles,advantages,and limitations of AIGC in intelligent code generation,analyzed the new mode of human-computer collaboration in high-level language programming courses driven by AIGC,discussed the impact of human-computer collaboration on programming efficiency and code quality through practical case studies,and looks forward to future development trends.This research aims to provide theoretical and practical guidance for high-level language programming courses and promote innovative development of high-level language programming courses under the human-computer collaboration paradigm.展开更多
The shop floor dynamic scheduling system based on human-computer interaction is the use of computer-aided decision-making and human-computer interaction to solve the dynamic scheduling problem.A human-computer interac...The shop floor dynamic scheduling system based on human-computer interaction is the use of computer-aided decision-making and human-computer interaction to solve the dynamic scheduling problem.A human-computer interaction interface based on Gantt chart is designed,which can not only comprehensively and quantitatively represent the scheduling process and scheduling scheme,but also have friendly human-computer interaction performance.The data transmission and interaction architecture is constructed to realize the rapid response to shop floor disturbance events.A priority calculation algorithm integrating priority rules and dispatcher preference is proposed,which realizes the automatic calculation of priority for the dispatcher's reference and reduces theirburden.A man-machine interactive shop floor dynamic scheduling strategy is proposed.When solving the dynamic flexible job shop scheduling problem caused by machine tool breakdown and urgent order,the origin moments obtained by using this strategy are 0.4190 and 0.3703 respectively.As can be seen from the origin moment indicator,the dynamic shop floor scheduling system based on the human-computer interaction is efficient and reliable in solving dynamic scheduling problems,and related strategies of this system are also feasible and stable.展开更多
Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing de...Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.展开更多
With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supporte...The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supported Africa in addressing historical injustices at diplomatic and political levels and firmly backed South Africa in hosting the G20 Leaders’Summit,further deepening China-Africa strategic mutual trust.展开更多
With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT termi...With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.展开更多
This mixed-methods study investigated how AI-enhanced English as a Foreign Language(EFL)learning environments influence students’psychological well-being through the mediating roles of motivation and language learnin...This mixed-methods study investigated how AI-enhanced English as a Foreign Language(EFL)learning environments influence students’psychological well-being through the mediating roles of motivation and language learning anxiety and the moderating role of trust.Participants were Chinese university students(N=310,62%female,mean age=18.9,SD=0.8),of whom 15 completed interviews to both add to and to clarify the evidence from the surveys.Structural equation modeling results revealed that AI use had significant indirect effects on well-being through increased motivation and reduced language learning anxiety.Trust in AI significantly moderated both paths,amplifying the motivational benefits and anxiety reduction associated with AI use.Thematic analysis supported these results,identifying three experiential themes:(1)motivational empowerment through personalization,(2)anxiety regulation through safe practice and feedback,and(3)trust as the emotional bridge between AI and well-being.The study extends AI psychology applications by empirically linking technology engagement with affective outcomes and underscores the need for human-centered and trust-enhancing design in AI-supported education.From these findings,we conclude that adaptive,transparent,and autonomy-supportive AI systems promote self-determined motivation,emotional safety,and overall psychological health among EFL learners.展开更多
Optimization design of hydraulic manifold blocks (HMB) is studied as acomplex solid spatial layout problem. Based on comprehensive research into structure features anddesign rules of HMB, an optimal mathematical model...Optimization design of hydraulic manifold blocks (HMB) is studied as acomplex solid spatial layout problem. Based on comprehensive research into structure features anddesign rules of HMB, an optimal mathematical model for this problem is presented. Usinghuman-computer cooperative genetic algorithm (GA) and its hybrid optitation strategies, integratedlayout and connection design schemes of HMB can be automatically optimized. An example is given totestify it.展开更多
Recently,vision-based gesture recognition(VGR)has become a hot research spot in human-computer interaction(HCI).Unlike other gesture recognition methods with data gloves or other wearable sensors,vision-based gesture ...Recently,vision-based gesture recognition(VGR)has become a hot research spot in human-computer interaction(HCI).Unlike other gesture recognition methods with data gloves or other wearable sensors,vision-based gesture recognition could lead to more natural and intuitive HCI interactions.This paper reviews the state-of-the-art vision-based gestures recognition methods,from different stages of gesture recognition process,i.e.,(1)image acquisition and pre-processing,(2)gesture segmentation,(3)gesture tracking,(4)feature extraction,and(5)gesture classification.This paper also analyzes the advantages and disadvantages of these various methods in detail.Finally,the challenges of vision-based gesture recognition in haptic rendering and future research directions are discussed.展开更多
To solve the problem of risk identification and quantitative assessment for human-computer interaction(HCI)in complex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA...To solve the problem of risk identification and quantitative assessment for human-computer interaction(HCI)in complex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA)and cognitive reliability and error analysis method(CREAM)is proposed.STPACREAM can identify unsafe control actions and find the causal path during the interaction of avionics systems and pilot with the help of formal verification tools automatically.The common performance conditions(CPC)of avionics systems in the aviation environment is established and a quantitative analysis of human failure is carried out.Taking the head-up display(HUD)system interaction process as an example,a case analysis is carried out,the layered safety control structure and formal model of the HUD interaction process are established.For the interactive behavior“Pilots approaching with HUD”,four unsafe control actions and35 causal scenarios are identified and the impact of common performance conditions at different levels on the pilot decision model are analyzed.The results show that HUD's HCI level gradually improves as the scores of CPC increase,and the quality of crew member cooperation and time sufficiency of the task is the key to its HCI.Through case analysis,it is shown that STPACREAM can quantitatively assess the hazards in HCI and identify the key factors that impact safety.展开更多
The cooperative work between human being and computer based on virtual reality (VR) is investigated to plan the disassembly sequences more efficiently. A three-layer model of human-computer cooperative virtual disasse...The cooperative work between human being and computer based on virtual reality (VR) is investigated to plan the disassembly sequences more efficiently. A three-layer model of human-computer cooperative virtual disassembly is built, and the corresponding human-computer system for stable virtual disassembly is developed. In this system, an immersive and interactive virtual disassembly environment has been created to provide planners with a more visual working scene. For cooperative disassembly, an intelligent module of stability analysis of disassembly operations is embedded into the human-computer system to assist planners to implement disassembly tasks better. The supporting matrix for stability analysis of disassembly operations is defined and the method of stability analysis is detailed. Based on the approach, the stability of any disassembly operation can be analyzed to instruct the manual virtual disassembly. At last, a disassembly case in the virtual environment is given to prove the validity of above ideas.展开更多
Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates...Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.展开更多
Human-Computer dialogue systems provide a natural language based interface between human and computers. They are widely demanded in network information services, intelligent accompanying robots, and so on. A Human-Com...Human-Computer dialogue systems provide a natural language based interface between human and computers. They are widely demanded in network information services, intelligent accompanying robots, and so on. A Human-Computer dialogue system typically consists of three parts, namely Natural Language Understanding （NLU）, Dialogue Management （DM） and Natural Language Generation （NLG）. Each part has several different subtasks. Each subtask has been received lots of attentions, many improvements have been achieved on each subtask, respectively. But systems built in traditional pipeline way, where different subtasks are assembled sequently, suffered from some problems such as error accu- mulation and expanding, domain transferring. Therefore, researches on jointly modeling several subtasks in one part or cross different parts have been prompted greatly in recent years, especially the rapid developments on deep neural networks based joint models. There is even a few work aiming to integrate all subtasks of a dialogue system in a single model, namely end-to-end models. This paper introduces two basic frames of current dialogue systems and gives a brief survey on recent advances on variety subtasks at first, and then focuses on joint models for multiple subtasks of dialogues. We review several different joint models including integration of several subtasks inside NLU or NLG, jointly modeling cross NLG and DM, and jointly modeling through NLU, DM and NLG. Both advantages and problems of those joint models are discussed. We consider that the joint models, or end-to-end models, will be one important trend for developing Human-Computer dialogue systems.展开更多
In this paper, a new control system based on forearm electromyogram （EMG） is proposed for computer peripheral control and artificial prosthesis control. This control system intends to realize the commands of six pre...In this paper, a new control system based on forearm electromyogram （EMG） is proposed for computer peripheral control and artificial prosthesis control. This control system intends to realize the commands of six pre-defined hand poses： up, down, left, right, yes, and no. In order to research the possibility of using a unified amplifier for both electroencephalogram （EEG） and EMG, the surface forearm EMG data is acquired by a 4-channel EEG measurement system. The Bayesian classifier is used to classify the power spectral density （PSD） of the signal. The experiment result verifies that this control system can supply a high command recognition rate （average 48%） even the EMG data is collected with an EEG system just with single electrode measurement.展开更多
We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic informa...We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic information for applications. Facing this challenge, a new tactic, Human-Computer Collaborative (HCC) tactic, and a corresponding new method, Operator-Object Directed (OOD) method, are proposed for the design of a system for feature extraction from large scale aerial images. We hold that in almost all technical complex systems, full automation will be neither technically feasible nor socially acceptable. The system should be designed to optimize through the cooperative operation with two agents in the system: the hurtan and the computer.展开更多
Based on the traditional Human-Computer Interaction method which is mainly touch input system, the way of capturing the movement of people by using cameras is proposed. This is a convenient technique which can provide...Based on the traditional Human-Computer Interaction method which is mainly touch input system, the way of capturing the movement of people by using cameras is proposed. This is a convenient technique which can provide users more experience. In the article, a new way of detecting moving things is given on the basis of development of the image processing technique. The system architecture decides that the communication should be used between two different applications. After considered, named pipe is selected from many ways of communication to make sure that video is keeping in step with the movement from the analysis of the people moving. According to a large amount of data and principal knowledge, thinking of the need of actual project, a detailed system design and realization is finished. The system consists of three important modules: detecting of the people's movement, information transition between applications and video showing in step with people's movement. The article introduces the idea of each module and technique.展开更多
基金Supported by National Key R&D Program of China(Grant Nos.2022YFC3601704,2023YFB4706100)National Natural Science Foundation of China(Grant Nos.U23A20338,62203149).
文摘Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability of these robots.Therefore,this study proposes a variable stiffness humancomputer interaction contact unit module(VSHCUM)based on the granular jamming mechanism.It is characterized by a double-layer chamber structure:the inner layer is a granular chamber,and the outer layer is an air chamber.The interaction force is transmitted by embedding a rigid support in the inner layer.Unlike the common flexible-belt interactive contact unit,when the exoskeleton is bound to the patient's limb,vSHCUM can realize adaptive fitting of the patient's limb shape using the pressure change in the double-chamber structure.Simultaneously,by adjusting the vacuum level of the granular chamber,the stiffness of the interactive contact unit can be adjusted by a factor of more than five,and the internal work loss caused by self-pulling deformation during the auxiliary force transfer process can be reduced.
基金the Talent Fund of Beijing Jiaotong University(Grant No.2024XKRC055).
文摘In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.
基金Education and Teaching Research Project of Beijing University of Technology(ER2024KCB08)。
文摘With the rapid development of artificial intelligence technology,AIGC(Artificial Intelligence-Generated Content)has triggered profound changes in the field of high-level language programming courses.This paper deeply explored the application principles,advantages,and limitations of AIGC in intelligent code generation,analyzed the new mode of human-computer collaboration in high-level language programming courses driven by AIGC,discussed the impact of human-computer collaboration on programming efficiency and code quality through practical case studies,and looks forward to future development trends.This research aims to provide theoretical and practical guidance for high-level language programming courses and promote innovative development of high-level language programming courses under the human-computer collaboration paradigm.
基金supported by the Tianjin Enterprise Science and Technology Commissioner Project(Grant No.23YDTPJC00740,Grant No.24YDTPJC00610)the Tianjin Tiankai Higher Education Science and Technology Innovation Park Enterprise R&D Special Project(Grant No.23YFZXYC00027).
文摘The shop floor dynamic scheduling system based on human-computer interaction is the use of computer-aided decision-making and human-computer interaction to solve the dynamic scheduling problem.A human-computer interaction interface based on Gantt chart is designed,which can not only comprehensively and quantitatively represent the scheduling process and scheduling scheme,but also have friendly human-computer interaction performance.The data transmission and interaction architecture is constructed to realize the rapid response to shop floor disturbance events.A priority calculation algorithm integrating priority rules and dispatcher preference is proposed,which realizes the automatic calculation of priority for the dispatcher's reference and reduces theirburden.A man-machine interactive shop floor dynamic scheduling strategy is proposed.When solving the dynamic flexible job shop scheduling problem caused by machine tool breakdown and urgent order,the origin moments obtained by using this strategy are 0.4190 and 0.3703 respectively.As can be seen from the origin moment indicator,the dynamic shop floor scheduling system based on the human-computer interaction is efficient and reliable in solving dynamic scheduling problems,and related strategies of this system are also feasible and stable.
基金The author Dr.Arshiya S.Ansari extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1538).
文摘Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
文摘The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supported Africa in addressing historical injustices at diplomatic and political levels and firmly backed South Africa in hosting the G20 Leaders’Summit,further deepening China-Africa strategic mutual trust.
基金supported by National Key R&D Program of China(No.2022YFB3105101).
文摘With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.
文摘This mixed-methods study investigated how AI-enhanced English as a Foreign Language(EFL)learning environments influence students’psychological well-being through the mediating roles of motivation and language learning anxiety and the moderating role of trust.Participants were Chinese university students(N=310,62%female,mean age=18.9,SD=0.8),of whom 15 completed interviews to both add to and to clarify the evidence from the surveys.Structural equation modeling results revealed that AI use had significant indirect effects on well-being through increased motivation and reduced language learning anxiety.Trust in AI significantly moderated both paths,amplifying the motivational benefits and anxiety reduction associated with AI use.Thematic analysis supported these results,identifying three experiential themes:(1)motivational empowerment through personalization,(2)anxiety regulation through safe practice and feedback,and(3)trust as the emotional bridge between AI and well-being.The study extends AI psychology applications by empirically linking technology engagement with affective outcomes and underscores the need for human-centered and trust-enhancing design in AI-supported education.From these findings,we conclude that adaptive,transparent,and autonomy-supportive AI systems promote self-determined motivation,emotional safety,and overall psychological health among EFL learners.
基金This project is supported by Provincial ScienceTechnology Foundation of Liaoning (No. 20022132)
文摘Optimization design of hydraulic manifold blocks (HMB) is studied as acomplex solid spatial layout problem. Based on comprehensive research into structure features anddesign rules of HMB, an optimal mathematical model for this problem is presented. Usinghuman-computer cooperative genetic algorithm (GA) and its hybrid optitation strategies, integratedlayout and connection design schemes of HMB can be automatically optimized. An example is given totestify it.
基金Supported by the National Natural Science Foundation of China(61773205,61773219)the Fundamental Research Funds for the Central Universities(NS2016032,NS2019018,Nanjing University of Aeronautics and Astronautics)+1 种基金the Scholarship from China Scholarship Council(201906835020)the Fundamental Research Funds for the Central Universities(the Graduate Student Innovation Base Open Fund Project of NUAA,kfjj20190307)。
文摘Recently,vision-based gesture recognition(VGR)has become a hot research spot in human-computer interaction(HCI).Unlike other gesture recognition methods with data gloves or other wearable sensors,vision-based gesture recognition could lead to more natural and intuitive HCI interactions.This paper reviews the state-of-the-art vision-based gestures recognition methods,from different stages of gesture recognition process,i.e.,(1)image acquisition and pre-processing,(2)gesture segmentation,(3)gesture tracking,(4)feature extraction,and(5)gesture classification.This paper also analyzes the advantages and disadvantages of these various methods in detail.Finally,the challenges of vision-based gesture recognition in haptic rendering and future research directions are discussed.
基金supported by the National Key Research and Development Program of China(2021YFB1600601)the Joint Funds of the National Natural Science Foundation of China and the Civil Aviation Administration of China(U1933106)+2 种基金the Scientific Research Project of Tianjin Educational Committee(2019KJ134)the Natural Science Foundation of TianjinIntelligent Civil Aviation Program(21JCQNJ C00900)。
文摘To solve the problem of risk identification and quantitative assessment for human-computer interaction(HCI)in complex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA)and cognitive reliability and error analysis method(CREAM)is proposed.STPACREAM can identify unsafe control actions and find the causal path during the interaction of avionics systems and pilot with the help of formal verification tools automatically.The common performance conditions(CPC)of avionics systems in the aviation environment is established and a quantitative analysis of human failure is carried out.Taking the head-up display(HUD)system interaction process as an example,a case analysis is carried out,the layered safety control structure and formal model of the HUD interaction process are established.For the interactive behavior“Pilots approaching with HUD”,four unsafe control actions and35 causal scenarios are identified and the impact of common performance conditions at different levels on the pilot decision model are analyzed.The results show that HUD's HCI level gradually improves as the scores of CPC increase,and the quality of crew member cooperation and time sufficiency of the task is the key to its HCI.Through case analysis,it is shown that STPACREAM can quantitatively assess the hazards in HCI and identify the key factors that impact safety.
基金This project is supported by National Natural Science Foundation of China (No.59990470-2).
文摘The cooperative work between human being and computer based on virtual reality (VR) is investigated to plan the disassembly sequences more efficiently. A three-layer model of human-computer cooperative virtual disassembly is built, and the corresponding human-computer system for stable virtual disassembly is developed. In this system, an immersive and interactive virtual disassembly environment has been created to provide planners with a more visual working scene. For cooperative disassembly, an intelligent module of stability analysis of disassembly operations is embedded into the human-computer system to assist planners to implement disassembly tasks better. The supporting matrix for stability analysis of disassembly operations is defined and the method of stability analysis is detailed. Based on the approach, the stability of any disassembly operation can be analyzed to instruct the manual virtual disassembly. At last, a disassembly case in the virtual environment is given to prove the validity of above ideas.
基金supported by the China Fundamental Research Funds for the Central Universities(2022JBQY006)。
文摘Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.
文摘Human-Computer dialogue systems provide a natural language based interface between human and computers. They are widely demanded in network information services, intelligent accompanying robots, and so on. A Human-Computer dialogue system typically consists of three parts, namely Natural Language Understanding （NLU）, Dialogue Management （DM） and Natural Language Generation （NLG）. Each part has several different subtasks. Each subtask has been received lots of attentions, many improvements have been achieved on each subtask, respectively. But systems built in traditional pipeline way, where different subtasks are assembled sequently, suffered from some problems such as error accu- mulation and expanding, domain transferring. Therefore, researches on jointly modeling several subtasks in one part or cross different parts have been prompted greatly in recent years, especially the rapid developments on deep neural networks based joint models. There is even a few work aiming to integrate all subtasks of a dialogue system in a single model, namely end-to-end models. This paper introduces two basic frames of current dialogue systems and gives a brief survey on recent advances on variety subtasks at first, and then focuses on joint models for multiple subtasks of dialogues. We review several different joint models including integration of several subtasks inside NLU or NLG, jointly modeling cross NLG and DM, and jointly modeling through NLU, DM and NLG. Both advantages and problems of those joint models are discussed. We consider that the joint models, or end-to-end models, will be one important trend for developing Human-Computer dialogue systems.
基金supported by the National Natural Science Foundation of China under Grant No. 60736029 and 30525030UESTC Youth Foundation under Grant No. L08010901JX0772 for support.
文摘In this paper, a new control system based on forearm electromyogram （EMG） is proposed for computer peripheral control and artificial prosthesis control. This control system intends to realize the commands of six pre-defined hand poses： up, down, left, right, yes, and no. In order to research the possibility of using a unified amplifier for both electroencephalogram （EEG） and EMG, the surface forearm EMG data is acquired by a 4-channel EEG measurement system. The Bayesian classifier is used to classify the power spectral density （PSD） of the signal. The experiment result verifies that this control system can supply a high command recognition rate （average 48%） even the EMG data is collected with an EEG system just with single electrode measurement.
文摘We are involved in an embarrassing situation that the limited capability of automated feature extraction in digital photogrammetric systems cannot satisfy the increasing needs for rapid acquisition of semantic information for applications. Facing this challenge, a new tactic, Human-Computer Collaborative (HCC) tactic, and a corresponding new method, Operator-Object Directed (OOD) method, are proposed for the design of a system for feature extraction from large scale aerial images. We hold that in almost all technical complex systems, full automation will be neither technically feasible nor socially acceptable. The system should be designed to optimize through the cooperative operation with two agents in the system: the hurtan and the computer.
文摘Based on the traditional Human-Computer Interaction method which is mainly touch input system, the way of capturing the movement of people by using cameras is proposed. This is a convenient technique which can provide users more experience. In the article, a new way of detecting moving things is given on the basis of development of the image processing technique. The system architecture decides that the communication should be used between two different applications. After considered, named pipe is selected from many ways of communication to make sure that video is keeping in step with the movement from the analysis of the people moving. According to a large amount of data and principal knowledge, thinking of the need of actual project, a detailed system design and realization is finished. The system consists of three important modules: detecting of the people's movement, information transition between applications and video showing in step with people's movement. The article introduces the idea of each module and technique.
