Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations ...Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system.The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.展开更多
Background:Despite access to university counseling services,many students underutilize them due to cultural stigma,language barriers,and perceived irrelevance.As a result,ChatGPT has emerged as an informal,always-avai...Background:Despite access to university counseling services,many students underutilize them due to cultural stigma,language barriers,and perceived irrelevance.As a result,ChatGPT has emerged as an informal,always-available support system.This study investigates how international PhD students in Malaysia navigate loneliness,mental well-being,and social disconnection through interactions with Generative AI(mainly ChatGPT).Methods:Using a mixed-methods design,the study surveyed 155 international doctoral students and analyzed quantitative responses across four dimensions:loneliness,well-being(WHO-5),perceived social support,and AI-facilitated emotional support.Additionally,open-ended responses were examined using Latent Dirichlet Allocation(LDA)to identify emergent themes.Results:Quantitative findings showed that ChatGPT use was modestly associated with greater loneliness(r=0.17)and lower perceived social support(r=−0.16),with only a weak positive link to well-being(r=0.11).Regression analysis confirmed these small effects,while qualitative themes revealed that students used ChatGPT mainly for emotional venting and productivity,underscoring its value as short-term support but also its potential to displace human interaction.More specifically,thematic analysis revealed two dominant student experiences:(1)emotional venting and calmness,and(2)productivity through non-judgmental dialogue.Conclusions:These findings suggest that while ChatGPT offers emotional reprieve and academic clarity,it may also displace human interaction.This study highlights the promise and pitfalls of AI-driven mental support in higher education.It urges institutions to enhance peer networks,foster culturally responsive mentoring,and develop ethical AI usage guidelines to support international doctoral students holistically.展开更多
考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新...考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新生目标量测集,在两个量测集分别运用PHD组处理更新基础上建立了处理模块的交互与协同机制,力图在保证跟踪精度的同时提高计算效率。该框架由于采用PHD组处理方式而具有状态自动提取功能。进一步给出了该框架的序贯蒙特卡罗算法实现。仿真结果表明,该算法在计算效率以及状态提取精度上具有明显优势。展开更多
基金funded by the National Natural Science Foundation of China(No.12202049)the Beijing Institute of Technology Research Fund Program for Young Scholars,China.
文摘Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system.The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.
文摘Background:Despite access to university counseling services,many students underutilize them due to cultural stigma,language barriers,and perceived irrelevance.As a result,ChatGPT has emerged as an informal,always-available support system.This study investigates how international PhD students in Malaysia navigate loneliness,mental well-being,and social disconnection through interactions with Generative AI(mainly ChatGPT).Methods:Using a mixed-methods design,the study surveyed 155 international doctoral students and analyzed quantitative responses across four dimensions:loneliness,well-being(WHO-5),perceived social support,and AI-facilitated emotional support.Additionally,open-ended responses were examined using Latent Dirichlet Allocation(LDA)to identify emergent themes.Results:Quantitative findings showed that ChatGPT use was modestly associated with greater loneliness(r=0.17)and lower perceived social support(r=−0.16),with only a weak positive link to well-being(r=0.11).Regression analysis confirmed these small effects,while qualitative themes revealed that students used ChatGPT mainly for emotional venting and productivity,underscoring its value as short-term support but also its potential to displace human interaction.More specifically,thematic analysis revealed two dominant student experiences:(1)emotional venting and calmness,and(2)productivity through non-judgmental dialogue.Conclusions:These findings suggest that while ChatGPT offers emotional reprieve and academic clarity,it may also displace human interaction.This study highlights the promise and pitfalls of AI-driven mental support in higher education.It urges institutions to enhance peer networks,foster culturally responsive mentoring,and develop ethical AI usage guidelines to support international doctoral students holistically.
文摘考虑到存活目标与新生目标在动态演化特性上的差异性,提出了面向快速多目标跟踪的协同概率假设密度(collaborative probability hypothesis density,CoPHD)滤波框架。该框架利用存活目标的状态信息,将量测动态划分为存活目标量测集与新生目标量测集,在两个量测集分别运用PHD组处理更新基础上建立了处理模块的交互与协同机制,力图在保证跟踪精度的同时提高计算效率。该框架由于采用PHD组处理方式而具有状态自动提取功能。进一步给出了该框架的序贯蒙特卡罗算法实现。仿真结果表明,该算法在计算效率以及状态提取精度上具有明显优势。
