为解决微电网在传统集中化交易模式下面临的决策耗时长、信任成本高和隐私安全等问题,提出了基于多智能体深度确定性策略梯度(multi-agent deep deterministic policy gradient,MADDPG)算法与智能合约的微电网去中心化市场交易体系。首...为解决微电网在传统集中化交易模式下面临的决策耗时长、信任成本高和隐私安全等问题,提出了基于多智能体深度确定性策略梯度(multi-agent deep deterministic policy gradient,MADDPG)算法与智能合约的微电网去中心化市场交易体系。首先,对微电网市场中多智能体进行划分后设计了适用于各主体参与分布式交易的微电网去中心化交易机制,以保障市场主体利益。其次,为实现交易确认阶段微电网市场主体的交易策略优化,采用MADDPG算法对各主体追求利益最大的竞价模型进行求解。最后,通过算例仿真验证了MADDPG算法在智能合约下微电网市场主体交易策略优化过程中的可行性和经济性。展开更多
In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
Disasters such as conflagration,toxic smoke,harmful gas or chemical leakage,and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent.The calamities are causing...Disasters such as conflagration,toxic smoke,harmful gas or chemical leakage,and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent.The calamities are causing massive fiscal and human life casualties.However,Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco.The authors have proposed an early fire detection system uses machine and/or deep learning algorithms.The article presents an Intelligent Industrial Monitoring System(IIMS)and introduces an Industrial Smart Social Agent(ISSA)in the Industrial SIoT(ISIoT)paradigm.The proffered ISSA empowers smart surveillance objects to communicate autonomously with other devices.Every Industrial IoT(IIoT)entity gets authorization from the ISSA to interact and work together to improve surveillance in any industrial context.The ISSA uses machine and deep learning algorithms for fire-related incident detection in the industrial environment.The authors have modeled a Convolutional Neural Network(CNN)and compared it with the four existing models named,FireNet,Deep FireNet,Deep FireNet V2,and Efficient Net for identifying the fire.To train our model,we used fire images and smoke sensor datasets.The image dataset contains fire,smoke,and no fire images.For evaluation,the proposed and existing models have been tested on the same.According to the comparative analysis,our CNN model outperforms other state-of-the-art models significantly.展开更多
文摘为解决微电网在传统集中化交易模式下面临的决策耗时长、信任成本高和隐私安全等问题,提出了基于多智能体深度确定性策略梯度(multi-agent deep deterministic policy gradient,MADDPG)算法与智能合约的微电网去中心化市场交易体系。首先,对微电网市场中多智能体进行划分后设计了适用于各主体参与分布式交易的微电网去中心化交易机制,以保障市场主体利益。其次,为实现交易确认阶段微电网市场主体的交易策略优化,采用MADDPG算法对各主体追求利益最大的竞价模型进行求解。最后,通过算例仿真验证了MADDPG算法在智能合约下微电网市场主体交易策略优化过程中的可行性和经济性。
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
基金supported by Kyungpook National University Research Fund,2020.
文摘Disasters such as conflagration,toxic smoke,harmful gas or chemical leakage,and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent.The calamities are causing massive fiscal and human life casualties.However,Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco.The authors have proposed an early fire detection system uses machine and/or deep learning algorithms.The article presents an Intelligent Industrial Monitoring System(IIMS)and introduces an Industrial Smart Social Agent(ISSA)in the Industrial SIoT(ISIoT)paradigm.The proffered ISSA empowers smart surveillance objects to communicate autonomously with other devices.Every Industrial IoT(IIoT)entity gets authorization from the ISSA to interact and work together to improve surveillance in any industrial context.The ISSA uses machine and deep learning algorithms for fire-related incident detection in the industrial environment.The authors have modeled a Convolutional Neural Network(CNN)and compared it with the four existing models named,FireNet,Deep FireNet,Deep FireNet V2,and Efficient Net for identifying the fire.To train our model,we used fire images and smoke sensor datasets.The image dataset contains fire,smoke,and no fire images.For evaluation,the proposed and existing models have been tested on the same.According to the comparative analysis,our CNN model outperforms other state-of-the-art models significantly.
