Internet of things(IOT)possess cultural,commercial and social effect in life in the future.The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets.Attack and identification...Internet of things(IOT)possess cultural,commercial and social effect in life in the future.The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets.Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain.Machine Learning Based Ensemble Intrusion Detection(MLEID)method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport(MQTT)and Hyper-Text Transfer Proto-col(HTTP)protocols.The proposed work has two significant contributions which are a selection of features and detection of attacks.New features are chosen from Improved Ant Colony Optimization(IACO)in the feature selection,and then the detection of attacks is carried out based on a combination of their possible proper-ties.The IACO approach is focused on defining the attacker’s important features against HTTP and MQTT.In the IACO algorithm,the constant factor is calculated against HTTP and MQTT based on the mean function for each element.Attack detection,the performance of several machine learning models are Distance Deci-sion Tree(DDT),Adaptive Neuro-Fuzzy Inference System(ANFIS)and Mahala-nobis Distance Support Vector Machine(MDSVM)were compared with predicting accurate attacks on the IoT network.The outcomes of these classifiers are combined into the ensemble model.The proposed MLEID strategy has effec-tively established malicious incidents.The UNSW-NB15 dataset is used to test the MLEID technique using data from simulated IoT sensors.Besides,the pro-posed MLEID technique has a greater detection rate and an inferior rate of false-positive compared to other conventional techniques.展开更多
Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an ...Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.展开更多
由于机型体积小,安装、操作方便和分散控制等特点,风机盘管(Fan Coil Unit,FCU)空调系统在办公楼、宾馆和公寓等建筑场所得到了广泛应用。然而,其主要设备-FCU具有惯性和较大时间滞后等动态特性,传统控制方式,如整数阶PID方式会导致室...由于机型体积小,安装、操作方便和分散控制等特点,风机盘管(Fan Coil Unit,FCU)空调系统在办公楼、宾馆和公寓等建筑场所得到了广泛应用。然而,其主要设备-FCU具有惯性和较大时间滞后等动态特性,传统控制方式,如整数阶PID方式会导致室温稳态误差和超调量较大,调节时间长等问题。鉴于此,提出FCU的室温分数阶PID(PIλDμ)控制器参数整定新算法及其控制系统构建的设计思路。首先,结合空调工艺的相关要求和分数阶控制技术,分别对FCU作用下的室内温度对象、室温测量变送器、FCU的送风单元、冷却/加热单元和室温PIλDμ控制器(Indoor Temperature Fractional Order Proportional Integral Derivative Controller,IT-FOPIDC)进行建模。其次,基于改进的蚁群优化算法(Improved Ant Colony Optimization Algorithm,IACOA)对该IT-FOPIDC的5个控制参数进行整定,获取其最佳值。最后,借助Matlab工具,对FCU作用下的空调房间室温PIλDμ调节系统进行组态和数值模拟其控制效果。结果表明,该室温PIλDμ调节系统在理论上是可行的,且室温控制效果明显优于Ziegler-Nichols(Z-N)整定法和ACOA算法的室温整数阶PID控制系统。展开更多
This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell...This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.展开更多
文摘Internet of things(IOT)possess cultural,commercial and social effect in life in the future.The nodes which are participating in IOT network are basi-cally attracted by the cyber-attack targets.Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain.Machine Learning Based Ensemble Intrusion Detection(MLEID)method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport(MQTT)and Hyper-Text Transfer Proto-col(HTTP)protocols.The proposed work has two significant contributions which are a selection of features and detection of attacks.New features are chosen from Improved Ant Colony Optimization(IACO)in the feature selection,and then the detection of attacks is carried out based on a combination of their possible proper-ties.The IACO approach is focused on defining the attacker’s important features against HTTP and MQTT.In the IACO algorithm,the constant factor is calculated against HTTP and MQTT based on the mean function for each element.Attack detection,the performance of several machine learning models are Distance Deci-sion Tree(DDT),Adaptive Neuro-Fuzzy Inference System(ANFIS)and Mahala-nobis Distance Support Vector Machine(MDSVM)were compared with predicting accurate attacks on the IoT network.The outcomes of these classifiers are combined into the ensemble model.The proposed MLEID strategy has effec-tively established malicious incidents.The UNSW-NB15 dataset is used to test the MLEID technique using data from simulated IoT sensors.Besides,the pro-posed MLEID technique has a greater detection rate and an inferior rate of false-positive compared to other conventional techniques.
基金Supported by the National Natural Science Foundation of China(No.61273035,71471135)
文摘Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical.
文摘由于机型体积小,安装、操作方便和分散控制等特点,风机盘管(Fan Coil Unit,FCU)空调系统在办公楼、宾馆和公寓等建筑场所得到了广泛应用。然而,其主要设备-FCU具有惯性和较大时间滞后等动态特性,传统控制方式,如整数阶PID方式会导致室温稳态误差和超调量较大,调节时间长等问题。鉴于此,提出FCU的室温分数阶PID(PIλDμ)控制器参数整定新算法及其控制系统构建的设计思路。首先,结合空调工艺的相关要求和分数阶控制技术,分别对FCU作用下的室内温度对象、室温测量变送器、FCU的送风单元、冷却/加热单元和室温PIλDμ控制器(Indoor Temperature Fractional Order Proportional Integral Derivative Controller,IT-FOPIDC)进行建模。其次,基于改进的蚁群优化算法(Improved Ant Colony Optimization Algorithm,IACOA)对该IT-FOPIDC的5个控制参数进行整定,获取其最佳值。最后,借助Matlab工具,对FCU作用下的空调房间室温PIλDμ调节系统进行组态和数值模拟其控制效果。结果表明,该室温PIλDμ调节系统在理论上是可行的,且室温控制效果明显优于Ziegler-Nichols(Z-N)整定法和ACOA算法的室温整数阶PID控制系统。
基金supported by the National Natural Science Foundation of China(7127106671171065+1 种基金71202168)the Natural Science Foundation of Heilongjiang Province(GC13D506)
文摘This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.
