The article synthesizes and presents the results regarding the stability of positive homogeneous systems that have been researched and published in recent years. Next, we provide a sufficient condition for global expo...The article synthesizes and presents the results regarding the stability of positive homogeneous systems that have been researched and published in recent years. Next, we provide a sufficient condition for global exponential stability in the case of discrete-time positive homogeneous systems with an order less than one with time-varying delays.展开更多
A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies t...A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.展开更多
Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the la...Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the last few decades, and the difficulties of sustainably providing social services that meet the required standards. The development of refugee settlements assumed that forcibly displaced people would return to their places or countries of origin. Unfortunately, displacement situations are prolonged indefinitely, forcing these people to spend most of their lives in conditions that are often deplorable and substandard, and therefore unsustainable. In most cases, the establishment of refugee settlements is triggered by an emergency caused by an influx of forcibly displaced people, who need to be accommodated urgently and provided with some form of international assistance and protection. This leaves little or no time for proper planning for long-term development as required. In addition, the current approach to temporary settlement harms the environment and can strain limited resources with ad hoc development models that have exacerbated difficulties. As a result, living conditions in refugee settlements have deteriorated over the last few decades and continue to pose challenges as to how best to design, plan, and sustain settlements over time. To contribute to addressing these challenges, this study proposes a new methodology supported by Model-Based Systems Engineering (MBSE) and a Systems Modeling Language (SysML) to develop a typical sustainable human settlement system model, which has functionally and operationally executed using a Systems Engineering (SE) approach. To assess the sustainability capacity of the proposed system, this work applies a matrix of crossed impact multiplication through a case study by conducting a system capacity interdependence analysis (SCIA) using the MICMAC methodology (Cross-impact matrix multiplication applied to classification) to assess the interdependency that exist between the sub-systems categories to deliver services at the system level. The sustainability analysis results based on capacity variables influence and dependency models shows that development activities in the settlement are unstable and, therefore, unsustainable since there is no apparent difference between the influential and dependent data used for the assessment. These results illustrate that an integrated system could improve human settlements’ sustainability and that capacity building in service delivery is beneficial and necessary.展开更多
The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearabl...The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.展开更多
A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there ...A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.展开更多
Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a nove...Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.展开更多
文摘The article synthesizes and presents the results regarding the stability of positive homogeneous systems that have been researched and published in recent years. Next, we provide a sufficient condition for global exponential stability in the case of discrete-time positive homogeneous systems with an order less than one with time-varying delays.
文摘A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.
文摘Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the last few decades, and the difficulties of sustainably providing social services that meet the required standards. The development of refugee settlements assumed that forcibly displaced people would return to their places or countries of origin. Unfortunately, displacement situations are prolonged indefinitely, forcing these people to spend most of their lives in conditions that are often deplorable and substandard, and therefore unsustainable. In most cases, the establishment of refugee settlements is triggered by an emergency caused by an influx of forcibly displaced people, who need to be accommodated urgently and provided with some form of international assistance and protection. This leaves little or no time for proper planning for long-term development as required. In addition, the current approach to temporary settlement harms the environment and can strain limited resources with ad hoc development models that have exacerbated difficulties. As a result, living conditions in refugee settlements have deteriorated over the last few decades and continue to pose challenges as to how best to design, plan, and sustain settlements over time. To contribute to addressing these challenges, this study proposes a new methodology supported by Model-Based Systems Engineering (MBSE) and a Systems Modeling Language (SysML) to develop a typical sustainable human settlement system model, which has functionally and operationally executed using a Systems Engineering (SE) approach. To assess the sustainability capacity of the proposed system, this work applies a matrix of crossed impact multiplication through a case study by conducting a system capacity interdependence analysis (SCIA) using the MICMAC methodology (Cross-impact matrix multiplication applied to classification) to assess the interdependency that exist between the sub-systems categories to deliver services at the system level. The sustainability analysis results based on capacity variables influence and dependency models shows that development activities in the settlement are unstable and, therefore, unsustainable since there is no apparent difference between the influential and dependent data used for the assessment. These results illustrate that an integrated system could improve human settlements’ sustainability and that capacity building in service delivery is beneficial and necessary.
文摘The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.
文摘A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.
基金the National Key Research and Development Program of China(2021YFF0900800)the National Natural Science Foundation of China(61972276,62206116,62032016)+2 种基金the New Liberal Arts Reform and Practice Project of National Ministry of Education(2021170002)the Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems(20210101)Tianjin University Talent Innovation Reward Program for Literature and Science Graduate Student(C1-2022-010)。
文摘Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.
