Nowadays, due to increasing population and water shortage and competition for its consumption, especially in the agriculture, which is the largest consumer of water, proper and suitable utilization and optimal use of ...Nowadays, due to increasing population and water shortage and competition for its consumption, especially in the agriculture, which is the largest consumer of water, proper and suitable utilization and optimal use of water resources is essential. One of the important parameters in agriculture field is water distribution network. In this research, differential evolution algorithm (DE) was used to optimize Ismail Abad water supply network. This network is pressurized network and includes 19 pipes and 18 nodes. Optimization of the network has been evaluated by developing an optimization model based on DE algorithm in MATLAB and the dynamic connection with EPANET software for network hydraulic calculation. The developing model was run for the scale factor (F), the crossover constant (Cr), initial population (N) and the number of generations (G) and was identified best adeptness for DE algorithm is 0.6, 0.5, 100 and 200 for F and Cr, N and G, respectively. The optimal solution was compared with the classical empirical method and results showed that implementation cost of the network by DE algorithm was 10.66% lower than the classical empirical method.展开更多
Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them...Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems （ANFIS）. To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network （MLP） was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.展开更多
Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the network.Cloud computing services are available through common internet protocols...Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the network.Cloud computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be ignored.There are several techniques for dealing with network attacks.To this end,network anomaly detection systems are widely used as an effective countermeasure against network anomalies.The anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of anomalies.Network anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning methods.This paper presents an efficient model based on autoencoders for anomaly detection in cloud computing networks.The autoencoder learns a basic representation of the normal data and its reconstruction with minimum error.Therefore,the reconstruction error is used as an anomaly or classification metric.In addition,to detecting anomaly data from normal data,the classification of anomaly types has also been investigated.We have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction error.Unlike the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a vector.This enables our model to use the reconstruction error of every input feature as an anomaly or classification metric.We further propose a multi-class classification structure to classify the anomalies.We use the CIDDS-001 dataset as a commonly accepted dataset in the literature.Our evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score metrics.展开更多
文摘Nowadays, due to increasing population and water shortage and competition for its consumption, especially in the agriculture, which is the largest consumer of water, proper and suitable utilization and optimal use of water resources is essential. One of the important parameters in agriculture field is water distribution network. In this research, differential evolution algorithm (DE) was used to optimize Ismail Abad water supply network. This network is pressurized network and includes 19 pipes and 18 nodes. Optimization of the network has been evaluated by developing an optimization model based on DE algorithm in MATLAB and the dynamic connection with EPANET software for network hydraulic calculation. The developing model was run for the scale factor (F), the crossover constant (Cr), initial population (N) and the number of generations (G) and was identified best adeptness for DE algorithm is 0.6, 0.5, 100 and 200 for F and Cr, N and G, respectively. The optimal solution was compared with the classical empirical method and results showed that implementation cost of the network by DE algorithm was 10.66% lower than the classical empirical method.
文摘Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems （ANFIS）. To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network （MLP） was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.
文摘Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the network.Cloud computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be ignored.There are several techniques for dealing with network attacks.To this end,network anomaly detection systems are widely used as an effective countermeasure against network anomalies.The anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of anomalies.Network anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning methods.This paper presents an efficient model based on autoencoders for anomaly detection in cloud computing networks.The autoencoder learns a basic representation of the normal data and its reconstruction with minimum error.Therefore,the reconstruction error is used as an anomaly or classification metric.In addition,to detecting anomaly data from normal data,the classification of anomaly types has also been investigated.We have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction error.Unlike the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a vector.This enables our model to use the reconstruction error of every input feature as an anomaly or classification metric.We further propose a multi-class classification structure to classify the anomalies.We use the CIDDS-001 dataset as a commonly accepted dataset in the literature.Our evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score metrics.
