The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced ...The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.展开更多
Rock samples'TOC content is the best indicator of the organic matter in source rocks.The origin rock samples’analysis is used to calculate it manually by specialists.This method requires time and resources becaus...Rock samples'TOC content is the best indicator of the organic matter in source rocks.The origin rock samples’analysis is used to calculate it manually by specialists.This method requires time and resources because it relies on samples from many well intervals in source rocks.Therefore,research has been done to aid this effort.Machine learning algorithms can estimate total organic carbon instead of well logs and stratigraphic studies.In light of these efforts,the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and precision.Genetic algorithms,differential evolution,particle swarm optimization,grey wolf optimization,artificial bee colony,and evolution strategies were used to improve machine learning models to predict TOC.The six metaheuristics were integrated into four machine learning methods:extreme learning machine,elastic net linear model,linear support vector regression,and multivariate adaptive regression splines.Core samples from the YuDong-Nan shale gas field,located in the Sichuan basin,were used to evaluate the hybrid strategy.The findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict TOC.The results show that,independent of the metaheuristic used to guide the model selection,optimized extreme learning machines attained the best performance scores according to six metrics.Such hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.展开更多
To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy sa...To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.展开更多
To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to distribute a given...To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to distribute a given power allocation among the cluster nodes assigned to the application while balancing their performance change. The strategy operates in a timeslice-based manner to estimate the current application performance and power usage per node followed by power redistribution across the nodes. Experiments, performed on four nodes (112 cores) of a modern computing platform interconnected with Infiniband showed that even a significant power budget reduction of 20% may result in a performance degradation of as low as 1% under the proposed strategy compared with the execution in the unlimited power case.展开更多
基金supported by the FundamentalResearch Funds for the Central Universities(2662019QD002)
文摘The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.
基金supported by the Federal University of Juiz de Fora (UFJF).C.S.thanks CAPES (Finance Code 001)L.G.thanks CNPq (401796/2021-3,307688/2022-4,and 409433/2022-5)for their financial support.
文摘Rock samples'TOC content is the best indicator of the organic matter in source rocks.The origin rock samples’analysis is used to calculate it manually by specialists.This method requires time and resources because it relies on samples from many well intervals in source rocks.Therefore,research has been done to aid this effort.Machine learning algorithms can estimate total organic carbon instead of well logs and stratigraphic studies.In light of these efforts,the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and precision.Genetic algorithms,differential evolution,particle swarm optimization,grey wolf optimization,artificial bee colony,and evolution strategies were used to improve machine learning models to predict TOC.The six metaheuristics were integrated into four machine learning methods:extreme learning machine,elastic net linear model,linear support vector regression,and multivariate adaptive regression splines.Core samples from the YuDong-Nan shale gas field,located in the Sichuan basin,were used to evaluate the hybrid strategy.The findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict TOC.The results show that,independent of the metaheuristic used to guide the model selection,optimized extreme learning machines attained the best performance scores according to six metrics.Such hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.
文摘To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.
文摘To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to distribute a given power allocation among the cluster nodes assigned to the application while balancing their performance change. The strategy operates in a timeslice-based manner to estimate the current application performance and power usage per node followed by power redistribution across the nodes. Experiments, performed on four nodes (112 cores) of a modern computing platform interconnected with Infiniband showed that even a significant power budget reduction of 20% may result in a performance degradation of as low as 1% under the proposed strategy compared with the execution in the unlimited power case.
