Real-time decision making reflects the convergence of several digital technologies,including those concerned with the promulgation of artificial intelligence and other advanced technologies that underpin real-time act...Real-time decision making reflects the convergence of several digital technologies,including those concerned with the promulgation of artificial intelligence and other advanced technologies that underpin real-time actions.More specifically,real-time decision making can be depicted in terms of three converging dimensions:Internet of Things,decision making,and real-time.The Internet of Things include tangible goods,intangible services,ServGoods,and connected ServGoods.Decision making includes model-based analytics(since before 1990),information-based Big Data(since 1990),and training-based artificial intelligence(since 2000),and it is bolstered by the evolving real-time technologies of sensing(i.e.,capturing streaming data),processing(i.e.,applying real-time analytics),reacting(i.e.,making decisions in real-time),and learning(i.e.,employing deep neural networks).Real-time includes mobile networks,autonomous vehicles,and artificial general intelligence.Central to decision making,especially real-time decision making,is the ServGood concept,which the author introduced in an earlier paper(2012).It is a physical product or good encased by a services layer that renders the good more adaptable and smarter for a specific purpose or use.Addition of another communication sensors layer could further enhance its smartness and adaptiveness.Such connected ServGoods constitute a solid foundation for the advanced products of tomorrow which can further display their growing intelligence through real-time decisions.展开更多
Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer on...Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.展开更多
The outputs of a national economy can be partitioned into three sets of products:tangible goods(due to manufacturing,construction,extraction and agriculture),intangible services(due to an act of useful effort),and an ...The outputs of a national economy can be partitioned into three sets of products:tangible goods(due to manufacturing,construction,extraction and agriculture),intangible services(due to an act of useful effort),and an integration of services and goods or,as initially defined by Tien(2012),servgoods.Actually,these products can also be considered in terms of their relation to the first three Industrial Revolutions:the First Industrial Revolution(circa 1800)was primarily focused on the production of goods;the Second Industrial Revolution(circa 1900)was primarily focused on the mass production of goods;and the Third Industrial Revolution(circa 2000)has been primarily focused on the mass customization of goods,services or servgoods.In this follow-up paper,the Third Industrial Revolution of mass customization continues to accelerate in its evolution and,in many respects,is subsuming the earlier Industrial Revolutions of production and mass production.More importantly,with the advent of real-time decision making,artificial intelligence,Internet of Things,mobile networks,and other advanced digital technologies,customization has been extensively enabled,thereby advancing mass customization into a Fourth Industrial Revolution of real-time customization.Moreover,the moral,ethical,security and employment problems associated with both mass and real-time customization must be carefully assessed and mitigated,especially in regard to unintended consequences.Looking ahead and with the advance of artificial general intelligence,this Fourth Industrial Revolution could be forthcoming in about the middle of the 21st Century;it would allow for multiple activities to be simultaneously tackled in real-time and in a customized manner.展开更多
Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as elec...Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as electricity for daily needs from an uninterrupted source of energy no matter either renewable or nonrenewable.Causes of resilience issues include power surges,weather,natural disasters,or man-made accidents,and even equipment failure.The human operational error can also be an issue for grid-power supply to go down and should be factored into resilience planning.As the energy landscape undergoes a radical transformation,from a world of large,centralized coal plants to a decentralized energy world made up of small-scale gas-fired production and renewables,the stability of electricity supply will begin to affect energy pricing.Businesses must plan for this change.The challenges that the growth of renewables brings to the grid in terms of intermittency mean that transmission and distribution costs consume an increasing proportion of bills.With progress in the technology of AI(Artificial Intelligence)integration of such progressive technology in recent decades,we are improving our resiliency of energy flow,so we prevent any unexpected interruption of this flow.Ensuring your business is energy resilient helps insulate against price increases or fluctuations in supply,becoming critical to maintaining operations and reducing commercial risk.In the form short TM(Technical Memorandum),this paper covers this issue.展开更多
Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major...Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.展开更多
The services sector employs a large and growing proportion of workers in the industrialized nations, and it is increasingly dependent on information and communication technologies. While the interdependences, similari...The services sector employs a large and growing proportion of workers in the industrialized nations, and it is increasingly dependent on information and communication technologies. While the interdependences, similarities and complementarities of manufacturing and services are significant, there are considerable differences between goods and services, including the shift in focus from mass production to mass customization （whereby a service is produced and delivered in response to a customer＇s stated or imputed needs）. In general, services can be considered to be knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Like manufacturing systems, an efficient service system must be an integrated system of systems, leading to greater connectivity and interdependence. Integration must occur over the physical, temporal, organizational and functional dimensions, and must include methods concerned with the component, the management, and the system. Moreover, an effective service system must also be an adaptable system, leading to greater value and responsiveness. Adaptation must occur over the dimensions of monitoring, feedback, cybernetics and learning, and must include methods concerned with space, time, and system. In sum, service systems are indeed complex, especially due to the uncertainties associated with the human-centered aspects of such systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation. The paper concludes with several insights, including a plea to shift the current misplaced focus on developing a science or discipline for services to further developing a systems engineering approach to services, an approach based on the integration and adaptation of a host of sciences or disciplines （e.g., physics, mathematics, statistics, psychology, sociology, etc.）. In fact, what is required is a services-related transdisciplinary - beyond a single disciplinary - ontology or taxonomy as a basis for disciplinary integration and adaptation.展开更多
Healthcare is indeed a complex service system, one requiring the technobiology approach of systems engineering to underpin its development as an integrated and adaptive system. In general, healthcare services are carr...Healthcare is indeed a complex service system, one requiring the technobiology approach of systems engineering to underpin its development as an integrated and adaptive system. In general, healthcare services are carried out with knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Indeed, the engineering design of a healthcare system must recognize the fact that it is actually a complex integration of human-centered activities that is increasingly dependent on information technology and knowledge. Like any service system, healthcare can be considered to be a combination or recombination of three essential components - people （characterized by behaviors, values, knowledge, etc.）, processes （characterized by collaboration, customization, etc.） and products （characterized by software, hardware, infrastructures, etc.）. Thus, a healthcare system is an integrated and adaptive set of people, processes and products. It is, in essence, a system of systems which objectives are to enhance its efficiency （leading to greater interdependency） and effectiveness （leading to improved health）. Integration occurs over the physical, temporal, organizational and functional dimensions, while adaptation occurs over the monitoring, feedback, cybernetic and learning dimensions. In sum, such service systems as healthcare are indeed complex, especially due to the uncertainties associated with the human-centered aspects of these systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation.展开更多
文摘Real-time decision making reflects the convergence of several digital technologies,including those concerned with the promulgation of artificial intelligence and other advanced technologies that underpin real-time actions.More specifically,real-time decision making can be depicted in terms of three converging dimensions:Internet of Things,decision making,and real-time.The Internet of Things include tangible goods,intangible services,ServGoods,and connected ServGoods.Decision making includes model-based analytics(since before 1990),information-based Big Data(since 1990),and training-based artificial intelligence(since 2000),and it is bolstered by the evolving real-time technologies of sensing(i.e.,capturing streaming data),processing(i.e.,applying real-time analytics),reacting(i.e.,making decisions in real-time),and learning(i.e.,employing deep neural networks).Real-time includes mobile networks,autonomous vehicles,and artificial general intelligence.Central to decision making,especially real-time decision making,is the ServGood concept,which the author introduced in an earlier paper(2012).It is a physical product or good encased by a services layer that renders the good more adaptable and smarter for a specific purpose or use.Addition of another communication sensors layer could further enhance its smartness and adaptiveness.Such connected ServGoods constitute a solid foundation for the advanced products of tomorrow which can further display their growing intelligence through real-time decisions.
基金supported by the Federal Railroad Administration (FRA)the National Academy of Science (NAS) IDEA program
文摘Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.
文摘The outputs of a national economy can be partitioned into three sets of products:tangible goods(due to manufacturing,construction,extraction and agriculture),intangible services(due to an act of useful effort),and an integration of services and goods or,as initially defined by Tien(2012),servgoods.Actually,these products can also be considered in terms of their relation to the first three Industrial Revolutions:the First Industrial Revolution(circa 1800)was primarily focused on the production of goods;the Second Industrial Revolution(circa 1900)was primarily focused on the mass production of goods;and the Third Industrial Revolution(circa 2000)has been primarily focused on the mass customization of goods,services or servgoods.In this follow-up paper,the Third Industrial Revolution of mass customization continues to accelerate in its evolution and,in many respects,is subsuming the earlier Industrial Revolutions of production and mass production.More importantly,with the advent of real-time decision making,artificial intelligence,Internet of Things,mobile networks,and other advanced digital technologies,customization has been extensively enabled,thereby advancing mass customization into a Fourth Industrial Revolution of real-time customization.Moreover,the moral,ethical,security and employment problems associated with both mass and real-time customization must be carefully assessed and mitigated,especially in regard to unintended consequences.Looking ahead and with the advance of artificial general intelligence,this Fourth Industrial Revolution could be forthcoming in about the middle of the 21st Century;it would allow for multiple activities to be simultaneously tackled in real-time and in a customized manner.
文摘Energy resilience is about ensuring a business and end-use consumers have a reliable,regular supply of energy and contingency measures in place in the event of a power failure,generating a source of power such as electricity for daily needs from an uninterrupted source of energy no matter either renewable or nonrenewable.Causes of resilience issues include power surges,weather,natural disasters,or man-made accidents,and even equipment failure.The human operational error can also be an issue for grid-power supply to go down and should be factored into resilience planning.As the energy landscape undergoes a radical transformation,from a world of large,centralized coal plants to a decentralized energy world made up of small-scale gas-fired production and renewables,the stability of electricity supply will begin to affect energy pricing.Businesses must plan for this change.The challenges that the growth of renewables brings to the grid in terms of intermittency mean that transmission and distribution costs consume an increasing proportion of bills.With progress in the technology of AI(Artificial Intelligence)integration of such progressive technology in recent decades,we are improving our resiliency of energy flow,so we prevent any unexpected interruption of this flow.Ensuring your business is energy resilient helps insulate against price increases or fluctuations in supply,becoming critical to maintaining operations and reducing commercial risk.In the form short TM(Technical Memorandum),this paper covers this issue.
文摘Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.
文摘The services sector employs a large and growing proportion of workers in the industrialized nations, and it is increasingly dependent on information and communication technologies. While the interdependences, similarities and complementarities of manufacturing and services are significant, there are considerable differences between goods and services, including the shift in focus from mass production to mass customization （whereby a service is produced and delivered in response to a customer＇s stated or imputed needs）. In general, services can be considered to be knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Like manufacturing systems, an efficient service system must be an integrated system of systems, leading to greater connectivity and interdependence. Integration must occur over the physical, temporal, organizational and functional dimensions, and must include methods concerned with the component, the management, and the system. Moreover, an effective service system must also be an adaptable system, leading to greater value and responsiveness. Adaptation must occur over the dimensions of monitoring, feedback, cybernetics and learning, and must include methods concerned with space, time, and system. In sum, service systems are indeed complex, especially due to the uncertainties associated with the human-centered aspects of such systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation. The paper concludes with several insights, including a plea to shift the current misplaced focus on developing a science or discipline for services to further developing a systems engineering approach to services, an approach based on the integration and adaptation of a host of sciences or disciplines （e.g., physics, mathematics, statistics, psychology, sociology, etc.）. In fact, what is required is a services-related transdisciplinary - beyond a single disciplinary - ontology or taxonomy as a basis for disciplinary integration and adaptation.
文摘Healthcare is indeed a complex service system, one requiring the technobiology approach of systems engineering to underpin its development as an integrated and adaptive system. In general, healthcare services are carried out with knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Indeed, the engineering design of a healthcare system must recognize the fact that it is actually a complex integration of human-centered activities that is increasingly dependent on information technology and knowledge. Like any service system, healthcare can be considered to be a combination or recombination of three essential components - people （characterized by behaviors, values, knowledge, etc.）, processes （characterized by collaboration, customization, etc.） and products （characterized by software, hardware, infrastructures, etc.）. Thus, a healthcare system is an integrated and adaptive set of people, processes and products. It is, in essence, a system of systems which objectives are to enhance its efficiency （leading to greater interdependency） and effectiveness （leading to improved health）. Integration occurs over the physical, temporal, organizational and functional dimensions, while adaptation occurs over the monitoring, feedback, cybernetic and learning dimensions. In sum, such service systems as healthcare are indeed complex, especially due to the uncertainties associated with the human-centered aspects of these systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation.
