One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monito...One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.展开更多
Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on ...Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.展开更多
In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some appl...In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some applications of sensor networks, sensor nodes sense data from the environment periodically and trans-mit these data to sink node. In order to decrease energy consumption and so, increase network’s lifetime, volume of transmitted data should be decreased. A solution, which is suggested, is aggregation. In aggrega-tion mechanisms, the nodes aggregate received data and send aggregated result instead of raw data to sink, so, the volume of the transmitted data is decreased. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose an automaton based algorithm to con-struct aggregation tree by using energy and distance parameters. Automaton is a decision-making machine that is able-to-learn. Since network’s topology is dynamic, algorithm should construct aggregation tree peri-odically. In order to aware nodes of topology and so, select optimal path, routing packets must be flooded in entire network that led to high energy consumption. By using automaton machine which is in interaction with environment, we solve this problem based on automat learning. By using this strategy, aggregation tree is reconstructed locally, that result in decreasing energy consumption. Simulation results show that the pro-posed algorithm has better performance in terms of energy efficiency which increase the network lifetime and support better coverage.展开更多
Honey Bees Mating Optimization (HBMO) is a novel developed method used in different engineering areas. Optimiza-tion process in this algorithm is inspired of natural mating behavior between bees. In this paper, we hav...Honey Bees Mating Optimization (HBMO) is a novel developed method used in different engineering areas. Optimiza-tion process in this algorithm is inspired of natural mating behavior between bees. In this paper, we have attempted to createa new collaborative learning automata based honey bees mating optimization (C-LA-DHBMO).In previous model presented by very authors, the same learning automata parameters for all drones were used. However in the presented method, learning automatas with different reward and penalty parameters have been used which enhance reliability of algorithm and also has high convergence speed compared to previous proposed algorithm (LA-DHBMO). Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms.展开更多
The paper is related to the error analysis of Multicategory Support Vector Machine （MSVM） classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernel as Mercer kernel and give the error e...The paper is related to the error analysis of Multicategory Support Vector Machine （MSVM） classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernel as Mercer kernel and give the error estimate with De La Vall6e Poussin means. We also introduce the standard estimation of sample error, and derive the explicit learning rate.展开更多
The mobile Ad Hoc network(MANET)is a self-organizing and self-configuring wireless network,consisting of a set of mobile nodes.The design of efficient routing protocols for MANET has always been an active area of rese...The mobile Ad Hoc network(MANET)is a self-organizing and self-configuring wireless network,consisting of a set of mobile nodes.The design of efficient routing protocols for MANET has always been an active area of research.In existing routing algorithms,however,the current work does not scale well enough to ensure route stability when the mobility and distribution of nodes vary with time.In addition,each node in MANET has only limited initial energy,so energy conservation and balance must be taken into account.An efficient routing algorithm should not only be stable but also energy saving and balanced,within the dynamic network environment.To address the above problems,we propose a stable and energy-efficient routing algorithm,based on learning automata(LA)theory for MANET.First,we construct a new node stability measurement model and define an effective energy ratio function.On that basis,we give the node a weighted value,which is used as the iteration parameter for LA.Next,we construct an LA theory-based feedback mechanism for the MANET environment to optimize the selection of available routes and to prove the convergence of our algorithm.The experiments show that our proposed LA-based routing algorithm for MANET achieved the best performance in route survival time,energy consumption,energy balance,and acceptable per-formance in end-to-end delay and packet delivery ratio.展开更多
基金supported by the Islamic Azad University Urmia Brach,Iran
文摘One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.
基金supported by the National Basic Research Program of China(Grant No.2014CB643702)the National Natural Science Foundation of China(Grant No.51590880)+1 种基金the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KJZD-EW-M05)the National Key Research and Development Program of China(Grant No.2016YFB0700903)
文摘Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDA 19060102]supported by the National Natural Science Foundation of China[grant number 42030410]+2 种基金the Laoshan Laboratory [grant number LSL202202402]the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB40000000]the Startup Foundation for Introducing Talent of NUIST
文摘In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some applications of sensor networks, sensor nodes sense data from the environment periodically and trans-mit these data to sink node. In order to decrease energy consumption and so, increase network’s lifetime, volume of transmitted data should be decreased. A solution, which is suggested, is aggregation. In aggrega-tion mechanisms, the nodes aggregate received data and send aggregated result instead of raw data to sink, so, the volume of the transmitted data is decreased. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose an automaton based algorithm to con-struct aggregation tree by using energy and distance parameters. Automaton is a decision-making machine that is able-to-learn. Since network’s topology is dynamic, algorithm should construct aggregation tree peri-odically. In order to aware nodes of topology and so, select optimal path, routing packets must be flooded in entire network that led to high energy consumption. By using automaton machine which is in interaction with environment, we solve this problem based on automat learning. By using this strategy, aggregation tree is reconstructed locally, that result in decreasing energy consumption. Simulation results show that the pro-posed algorithm has better performance in terms of energy efficiency which increase the network lifetime and support better coverage.
文摘Honey Bees Mating Optimization (HBMO) is a novel developed method used in different engineering areas. Optimiza-tion process in this algorithm is inspired of natural mating behavior between bees. In this paper, we have attempted to createa new collaborative learning automata based honey bees mating optimization (C-LA-DHBMO).In previous model presented by very authors, the same learning automata parameters for all drones were used. However in the presented method, learning automatas with different reward and penalty parameters have been used which enhance reliability of algorithm and also has high convergence speed compared to previous proposed algorithm (LA-DHBMO). Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms.
文摘The paper is related to the error analysis of Multicategory Support Vector Machine （MSVM） classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernel as Mercer kernel and give the error estimate with De La Vall6e Poussin means. We also introduce the standard estimation of sample error, and derive the explicit learning rate.
基金The work was supported by the National Natural Science Foundation of China(No.61772386)Guangdong provincial science and technology project(No.2015B010131007)。
文摘The mobile Ad Hoc network(MANET)is a self-organizing and self-configuring wireless network,consisting of a set of mobile nodes.The design of efficient routing protocols for MANET has always been an active area of research.In existing routing algorithms,however,the current work does not scale well enough to ensure route stability when the mobility and distribution of nodes vary with time.In addition,each node in MANET has only limited initial energy,so energy conservation and balance must be taken into account.An efficient routing algorithm should not only be stable but also energy saving and balanced,within the dynamic network environment.To address the above problems,we propose a stable and energy-efficient routing algorithm,based on learning automata(LA)theory for MANET.First,we construct a new node stability measurement model and define an effective energy ratio function.On that basis,we give the node a weighted value,which is used as the iteration parameter for LA.Next,we construct an LA theory-based feedback mechanism for the MANET environment to optimize the selection of available routes and to prove the convergence of our algorithm.The experiments show that our proposed LA-based routing algorithm for MANET achieved the best performance in route survival time,energy consumption,energy balance,and acceptable per-formance in end-to-end delay and packet delivery ratio.
