By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s...By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.展开更多
Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this uniq...Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.展开更多
The local visual motion detection mechanism used in the visual systems of primatescan only sense the motion component oriented perpendicularly to the contrast gradient of thebrightness pattern.But the visual system of...The local visual motion detection mechanism used in the visual systems of primatescan only sense the motion component oriented perpendicularly to the contrast gradient of thebrightness pattern.But the visual system of higher animals can adaptively determine the actualdirection of motion through a learning process.In this paper a multilayered feedforward neuralnetwork model for perception of visual motion is presented.This model employs W.Reichardt’selementary motion detectors array and T.Kohonen’s self-organizing feature map.We explored theself-organizing principles for perception of visual motion.The computer simulations show thatthis neural network is able to recognize the true direction of motion through an unsupervisedlearning process.In addition,the neurons with the same or similar motion direction selectivitytend to appear in“functional columns”which seem to be qualitatively similar to the corticalmotion columns observed by electrophysiological and cytohistochemical studies in certain higherareas such as MT.It proves that motion-detection by spatio-temporal coherences,mapping,co-operation,competition,and Hebb rule may be the basic principles for the self-organization ofvisual motion perception networks.展开更多
Average(mean)voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a...Average(mean)voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a new generation of average voter based on parallel algorithms and parallel random access machine(PRAM)structure are proposed.The analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.展开更多
This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm...This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.展开更多
Measurement-while-drilling(MWD)and guidance technologies have been extensively deployed in the exploitation of oil,natural gas,and other energy resources.Conventional control approaches are plagued by challenges,inclu...Measurement-while-drilling(MWD)and guidance technologies have been extensively deployed in the exploitation of oil,natural gas,and other energy resources.Conventional control approaches are plagued by challenges,including limited anti-interference capabilities and the insufficient generalization of decision-making experience.To address the intricate problem of directional well trajectory control,an intelligent algorithm design framework grounded in the high-level interaction mechanism between geology and engineering is put forward.This framework aims to facilitate the rapid batch migration and update of drilling strategies.The proposed directional well trajectory control method comprehensively considers the multi-source heterogeneous attributes of drilling experience data,leverages the generative simulation of the geological drilling environment,and promptly constructs a directional well trajectory control model with self-adaptive capabilities to environmental variations.This construction is carried out based on three hierarchical levels:“offline pre-drilling learning,online during-drilling interaction,and post-drilling model transfer”.Simulation results indicate that the guidance model derived from this method demonstrates remarkable generalization performance and accuracy.It can significantly boost the adaptability of the control algorithm to diverse environments and enhance the penetration rate of the target reservoir during drilling operations.展开更多
语义同步定位与地图构建(simultaneous localization and mapping,SLAM)能够帮助移动机器人实现对未知环境更高层次的语义信息感知,已经成为解决移动机器人适应未来发展的关键技术之一。针对移动机器人在弱纹理、存在动态物体的未知境...语义同步定位与地图构建(simultaneous localization and mapping,SLAM)能够帮助移动机器人实现对未知环境更高层次的语义信息感知,已经成为解决移动机器人适应未来发展的关键技术之一。针对移动机器人在弱纹理、存在动态物体的未知境下环境感知能力弱的问题,提出了一种基于稀疏邻接注意力的图像分割算法(SNA-Seg)。首先,设计了基于稀疏邻接窗口自注意机制的全景分割网络结构,在不增加计算复杂度的前提下,充分挖掘全局信息,增强了网络对边缘细节的分割效果。其次,选取Cityscapes数据集作为训练与测试数据集,采用全景质量(panoptic quality,PQ)、分割质量(segmentation quality,SQ)和平均交并比(mIoU)等指标对算法性能进行了评估,并且采集本地视觉图像数据验证了算法的实际有效性。实验结果表明,SNA-Seg算法与基于滑窗注意力(Swin)和邻接注意力(NA)图像分割算法比较,各项评价指标均有不同程度的提升,其中mIoU指标提升幅度达到了11.17%,反映了掩膜分类准确性提升最为显著;在实例分割任务中,SNA-Seg算法展现出更高的分割精度,其输出掩膜与原始图像在边缘细节和语义类别上一致性较强,分割结果更加符合真实场景的语义结构。本文方法为语义SLAM中的全景图像分割任务提供了新的技术思路。展开更多
文摘By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.
基金the National Natural Science Foundation of China(Grant No.52072041)the Beijing Natural Science Foundation(Grant No.JQ21007)+2 种基金the University of Chinese Academy of Sciences(Grant No.Y8540XX2D2)the Robotics Rhino-Bird Focused Research Project(No.2020-01-002)the Tencent Robotics X Laboratory.
文摘Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.
基金Supported in part by the National Natural Science Foundation of China National Laboratory of Pattern Recognition,Institute of Automation,Academia Sinica.
文摘The local visual motion detection mechanism used in the visual systems of primatescan only sense the motion component oriented perpendicularly to the contrast gradient of thebrightness pattern.But the visual system of higher animals can adaptively determine the actualdirection of motion through a learning process.In this paper a multilayered feedforward neuralnetwork model for perception of visual motion is presented.This model employs W.Reichardt’selementary motion detectors array and T.Kohonen’s self-organizing feature map.We explored theself-organizing principles for perception of visual motion.The computer simulations show thatthis neural network is able to recognize the true direction of motion through an unsupervisedlearning process.In addition,the neurons with the same or similar motion direction selectivitytend to appear in“functional columns”which seem to be qualitatively similar to the corticalmotion columns observed by electrophysiological and cytohistochemical studies in certain higherareas such as MT.It proves that motion-detection by spatio-temporal coherences,mapping,co-operation,competition,and Hebb rule may be the basic principles for the self-organization ofvisual motion perception networks.
文摘Average(mean)voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a new generation of average voter based on parallel algorithms and parallel random access machine(PRAM)structure are proposed.The analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.
文摘This investigation deals with the intelligent system for parallel fault-tolerant diagnostic tests construction. A modified parallel algorithm for fault-tolerant diagnostic tests construction is proposed. The algorithm is allowed to optimize processing time on tests construction. A matrix model of data and knowledge representation, as well as various kinds of regularities in data and knowledge are presented. Applied intelligent system for diagnostic of mental health of population which is developed with the use of intelligent system for parallel fault-tolerant DTs construction is suggested.
基金supported by the National Key R&D Program of China(No.2019YFA0708304)the CNPC Innovation Fund(No.2022DQ02-0609)the Scientific research and technology development Project of CNPC(No.2022DJ4507).
文摘Measurement-while-drilling(MWD)and guidance technologies have been extensively deployed in the exploitation of oil,natural gas,and other energy resources.Conventional control approaches are plagued by challenges,including limited anti-interference capabilities and the insufficient generalization of decision-making experience.To address the intricate problem of directional well trajectory control,an intelligent algorithm design framework grounded in the high-level interaction mechanism between geology and engineering is put forward.This framework aims to facilitate the rapid batch migration and update of drilling strategies.The proposed directional well trajectory control method comprehensively considers the multi-source heterogeneous attributes of drilling experience data,leverages the generative simulation of the geological drilling environment,and promptly constructs a directional well trajectory control model with self-adaptive capabilities to environmental variations.This construction is carried out based on three hierarchical levels:“offline pre-drilling learning,online during-drilling interaction,and post-drilling model transfer”.Simulation results indicate that the guidance model derived from this method demonstrates remarkable generalization performance and accuracy.It can significantly boost the adaptability of the control algorithm to diverse environments and enhance the penetration rate of the target reservoir during drilling operations.
