The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
A theory of （4＋1）-dimensional gravity is developed on the basis of the teleparallel theory equivalent to general relativity. The fundamental gravitational field variables are the five-dimensional vector fields （pe...A theory of （4＋1）-dimensional gravity is developed on the basis of the teleparallel theory equivalent to general relativity. The fundamental gravitational field variables are the five-dimensional vector fields （pentad）, defined globally on a manifold M, and gravity is attributed to the torsion. The Lagrangian density is quadratic in the torsion tensor. We then give the exact five-dimensional solution. The solution is a generalization of the familiar Schwarzschild and Kerr solutions of the four-dimensional teleparallel equivalent of general relativity. We also use the definition of the gravitational energy to calculate the energy and the spatial momentum.展开更多
Sr, Nd and Pb isotopic characteristics of granulite and pyroxenite xenoliths from Hannuoba Basalts in five-dimensional space are studied. Combined with the distribution of xenoliths, it is suggested that the isotopic ...Sr, Nd and Pb isotopic characteristics of granulite and pyroxenite xenoliths from Hannuoba Basalts in five-dimensional space are studied. Combined with the distribution of xenoliths, it is suggested that the isotopic relationship between various xenoliths can be well explained by the processes of delamination.展开更多
Owing to the increasing worldwide demand for natural gas,the development of a large submerged combustion vaporizer is required.Its burner is equipped with a water spray nozzle to reduce nitrogen oxides,and a practi-ca...Owing to the increasing worldwide demand for natural gas,the development of a large submerged combustion vaporizer is required.Its burner is equipped with a water spray nozzle to reduce nitrogen oxides,and a practi-cal simulation method is required for the optimal design.The non-adiabatic flamelet approach can predict the combustion emissions and is useful for reducing simulation costs.However,as the number of control variables increases,the database requires larger memory and cannot be dealt with by general computers.In this study,an artificial neural network(ANN)model based on a five-dimensional flamelet database,which includes the effects of heat loss and vapor concentration by sprayed water evaporation,is developed.Furthermore,large eddy sim-ulations(LESs)for turbulent combustion fields with and without water spray are conducted employing flamelet generated manifold(FGM)approach with this ANN model,and the validity is investigated.For comparison,a lab-scale burner equipped with a water spray nozzle is manufactured,and combustion experiments with and without water spray are conducted.The results show that CO,NO,temperature,and reaction rate of progress variable predicted by the present ANN model are in good agreement with those of a five-dimensional flamelet database.In the condition without water spray,the flame behavior predicted by the LES employing the FGM/ANN ap-proach is in good agreement with that employing the conventional FGM approach,while indicating much lower memory,although there appeared some quantitative discrepancies in the temperature against the experiment probably partially because of the insufficiency of the FGM approach for the present complex flame structure.In the condition with water spray,the LES employing the FGM/ANN approach is able to capture the effect of the water spray on the flame behavior in the experiment,such that the water spray decreases the temperature,which causes the decrease in NO but increase in CO.展开更多
世界卫生组织(World Health Organization, WHO)强调通过健康促进、疾病预防和早诊早治以提升健康水平。由于疾病种类繁多、观点错杂,病机往往呈现为“多元化”的势态,尤其重大慢病、疑难重症、复合共病更是与复合体质状态密切关联,故...世界卫生组织(World Health Organization, WHO)强调通过健康促进、疾病预防和早诊早治以提升健康水平。由于疾病种类繁多、观点错杂,病机往往呈现为“多元化”的势态,尤其重大慢病、疑难重症、复合共病更是与复合体质状态密切关联,故而从“体病相关”切入探讨是突破疾病预测、预防以及治疗瓶颈的关键。基于多年的研究提出“五行十态体质”创新观点;遵循“天人相应”规律,将传统六十甲子周期理论运用于体质状态研究,综合考虑五运六气交互承制、天干地支本属系统、正化对化、天地二甲子、运气的平气状态等关键因素对人体五脏六腑功能的影响,运用传统“医算法”,依据“生克制化”“胜复郁发”“淫胜郁复”之气机升降特点,详细分析了基础体质与后天体质状态的关联;重视始生之时、疾病始发时、传变时、欲解时以及就诊时等不同节点的运气格局对人体的影响,以揭示体质特异性与相应系统疾病的易感性以及脏腑复合病机主、次标本的关系,预判健康状态与疾病不同阶段的病机、病势演变,为临床疾病的预测、预防、个体化干预以及群体化防治提供指导思路。展开更多
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
文摘A theory of （4＋1）-dimensional gravity is developed on the basis of the teleparallel theory equivalent to general relativity. The fundamental gravitational field variables are the five-dimensional vector fields （pentad）, defined globally on a manifold M, and gravity is attributed to the torsion. The Lagrangian density is quadratic in the torsion tensor. We then give the exact five-dimensional solution. The solution is a generalization of the familiar Schwarzschild and Kerr solutions of the four-dimensional teleparallel equivalent of general relativity. We also use the definition of the gravitational energy to calculate the energy and the spatial momentum.
文摘Sr, Nd and Pb isotopic characteristics of granulite and pyroxenite xenoliths from Hannuoba Basalts in five-dimensional space are studied. Combined with the distribution of xenoliths, it is suggested that the isotopic relationship between various xenoliths can be well explained by the processes of delamination.
基金The temperature measurements and PIA were supported by Prof.M.Nishioka of University of Tsukuba and Prof.K.Nishino of Yokohama National University,respectively.This work was partially supported by MEXT as"Program for Promoting Researches on the Supercomputer Fu-gaku"(Digital Twins of Real World’s Clean Energy Systems with Inte-grated Utilization of Super-simulation and AI).
文摘Owing to the increasing worldwide demand for natural gas,the development of a large submerged combustion vaporizer is required.Its burner is equipped with a water spray nozzle to reduce nitrogen oxides,and a practi-cal simulation method is required for the optimal design.The non-adiabatic flamelet approach can predict the combustion emissions and is useful for reducing simulation costs.However,as the number of control variables increases,the database requires larger memory and cannot be dealt with by general computers.In this study,an artificial neural network(ANN)model based on a five-dimensional flamelet database,which includes the effects of heat loss and vapor concentration by sprayed water evaporation,is developed.Furthermore,large eddy sim-ulations(LESs)for turbulent combustion fields with and without water spray are conducted employing flamelet generated manifold(FGM)approach with this ANN model,and the validity is investigated.For comparison,a lab-scale burner equipped with a water spray nozzle is manufactured,and combustion experiments with and without water spray are conducted.The results show that CO,NO,temperature,and reaction rate of progress variable predicted by the present ANN model are in good agreement with those of a five-dimensional flamelet database.In the condition without water spray,the flame behavior predicted by the LES employing the FGM/ANN ap-proach is in good agreement with that employing the conventional FGM approach,while indicating much lower memory,although there appeared some quantitative discrepancies in the temperature against the experiment probably partially because of the insufficiency of the FGM approach for the present complex flame structure.In the condition with water spray,the LES employing the FGM/ANN approach is able to capture the effect of the water spray on the flame behavior in the experiment,such that the water spray decreases the temperature,which causes the decrease in NO but increase in CO.
文摘世界卫生组织(World Health Organization, WHO)强调通过健康促进、疾病预防和早诊早治以提升健康水平。由于疾病种类繁多、观点错杂,病机往往呈现为“多元化”的势态,尤其重大慢病、疑难重症、复合共病更是与复合体质状态密切关联,故而从“体病相关”切入探讨是突破疾病预测、预防以及治疗瓶颈的关键。基于多年的研究提出“五行十态体质”创新观点;遵循“天人相应”规律,将传统六十甲子周期理论运用于体质状态研究,综合考虑五运六气交互承制、天干地支本属系统、正化对化、天地二甲子、运气的平气状态等关键因素对人体五脏六腑功能的影响,运用传统“医算法”,依据“生克制化”“胜复郁发”“淫胜郁复”之气机升降特点,详细分析了基础体质与后天体质状态的关联;重视始生之时、疾病始发时、传变时、欲解时以及就诊时等不同节点的运气格局对人体的影响,以揭示体质特异性与相应系统疾病的易感性以及脏腑复合病机主、次标本的关系,预判健康状态与疾病不同阶段的病机、病势演变,为临床疾病的预测、预防、个体化干预以及群体化防治提供指导思路。
