The scheme for probabilistic teleportation of an arbitrary three-particle state is proposed. By using single qubit gate and three two-qubit gates, efficient quantum logic networks for probabilistic teleportation of an...The scheme for probabilistic teleportation of an arbitrary three-particle state is proposed. By using single qubit gate and three two-qubit gates, efficient quantum logic networks for probabilistic teleportation of an arbitrary three-particle state are constructed.展开更多
We construct efficient quantum logic network for probabilistic cloning the quantum states used in imple mented tasks for which cloning provides some enhancement in performance.
Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily life.HAR uses sensor devices to collect user behavior data,obtain human activity information and identify them.Markov...Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily life.HAR uses sensor devices to collect user behavior data,obtain human activity information and identify them.Markov Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and data.MLN can solve the problems of complexity and uncertainty,and has good knowledge expression ability.However,MLN structure learning is relatively weak and requires a lot of computing and storage resources.Essentially,the MLN structure is derived from sensor data in the current scene.Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be obtained.To this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning.Such a rulebase can reduce the time required for MLN structure learning.We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time.In addition,we evaluate the parameters of the rulebase building scheme to check its stability.展开更多
This study investigates the robust feedback set stabilization of switched logic control networks(SLCNs)with state-dependent uncertain switching and control constraints.First,based on the properties of the semi-tensor ...This study investigates the robust feedback set stabilization of switched logic control networks(SLCNs)with state-dependent uncertain switching and control constraints.First,based on the properties of the semi-tensor product of matrices and the vector representation of logic,an SLCN with state-dependent uncertain switching and control constraints is expressed in algebraic form.Second,an input transformation and a switching model are constructed to transfer the original SLCN into one with a free control input and arbitrary switching.The equivalence between the set stabilizability of the original SLCN and that of the resulting SLCN is established.Based on such equivalence,the authors propose a necessary and sufficient condition for robust feedback set stabilizability.Finally,an example is presented to demonstrate the application of the results obtained.展开更多
An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and c...An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.展开更多
According to the features of Intelligent Network(IN)service logic,a method based ondata table to implement IN Service Logic is proposed.The method supports dynamic additionof IN service logic.
Development of energy-resources-poor remote rural areas of the world has been discussed by many in the past. Harnessing locally available renewable energy resources as an environmentally friendly option is gaining mom...Development of energy-resources-poor remote rural areas of the world has been discussed by many in the past. Harnessing locally available renewable energy resources as an environmentally friendly option is gaining momentum. Smart Integrated Renewable Energy Systems (SIRES) offer a resilient and economic path to “energize” the area and reach this goal. This paper discusses its intelligent control using neural networks and fuzzy logic.展开更多
In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties ...In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).展开更多
In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural n...In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural network and adaptive neurofuzzy inference system(ANFIS)controller with safe boundary algorithm.In this method of target seeking behaviour,the obstacle avoidance at every instant improves the performance of robot in navigation approach.The inputs to the controller are the signals from various sensors fixed at front face,left and right face of the AMR.The output signal from controller regulates the angular velocity of both front power wheels of the AMR.The shortest path is identified using fuzzy,neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation.The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation,in particular with curved/irregular obstacles.展开更多
In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neu...In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.展开更多
Device to device(D2 D) multi-hop communication in multicast networks solves the contradiction between high speed requirements and limited bandwidth in regional data sharing communication services. However, most networ...Device to device(D2 D) multi-hop communication in multicast networks solves the contradiction between high speed requirements and limited bandwidth in regional data sharing communication services. However, most networking models demand a large control overhead in eNodeB. Moreover, the topology should be calculated again due to the mobility of terminals, which causes the long delay. In this work, we model multicast network construction in D2 D communication through a fuzzy mathematics and game theory based algorithm. In resource allocation, we assume that user equipment(UE) can detect the available frequency and the fuzzy mathematics is introduced to describe an uncertain relationship between the resource and UE distributedly, which diminishes the time delay. For forming structure, a distributed myopic best response dynamics formation algorithm derived from a novel concept from the coalitional game theory is proposed, in which every UE can self-organize into stable structure without the control from eNodeB to improve its utilities in terms of rate and bit error rate(BER) while accounting for a link maintenance cost, and adapt this topology to environmental changes such as mobility while converging to a Nash equilibrium fast. Simulation results show that the proposed architecture converges to a tree network quickly and presents significant gains in terms of average rate utility reaching up to 50% compared to the star topology where all of the UE is directly connected to eNodeB.展开更多
A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simul...A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simula-tion results show that the controller has not only simple nonlinear control of tfuzzy control, but also the learning capabil-ity and adaptability of neural netwrk.展开更多
Analyzing gene network structure is an important way to discover and understand some unknown relevant functions and regulatory mechanisms of organism at the molecular level. In this work, mutual information networks a...Analyzing gene network structure is an important way to discover and understand some unknown relevant functions and regulatory mechanisms of organism at the molecular level. In this work, mutual information networks and Boolean logic networks are constructed using the methods of reverse modeling based on gene expression profiles in lung tissues with and without cancer. The comparison of these network structures shows that average degree, the proportion of non-isolated nodes, average betweenness and average coreness can distinguish the networks corresponding to the lung tissues with and without cancer. According to the difference of degree, betweenness and coreness of each gene in these networks, nine structural key genes are obtained. Seven of them which are related to lung cancer are supported by literatures. The remaining two genes AKT1 and RBL may have important roles in the formation, development and metastasis of lung cancer. Furthermore, the contrast of these logic networks suggests that the distributions of logic types are obviously different. The structural differences can help us to understand the mechanism of formation and development of lung cancer.展开更多
文摘The scheme for probabilistic teleportation of an arbitrary three-particle state is proposed. By using single qubit gate and three two-qubit gates, efficient quantum logic networks for probabilistic teleportation of an arbitrary three-particle state are constructed.
文摘We construct efficient quantum logic network for probabilistic cloning the quantum states used in imple mented tasks for which cloning provides some enhancement in performance.
基金supported by the National Natural Science Foundation of China(No.61872038).
文摘Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily life.HAR uses sensor devices to collect user behavior data,obtain human activity information and identify them.Markov Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and data.MLN can solve the problems of complexity and uncertainty,and has good knowledge expression ability.However,MLN structure learning is relatively weak and requires a lot of computing and storage resources.Essentially,the MLN structure is derived from sensor data in the current scene.Assuming that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be obtained.To this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN learning.Such a rulebase can reduce the time required for MLN structure learning.We apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning time.In addition,we evaluate the parameters of the rulebase building scheme to check its stability.
基金supported by the National Natural Science Foundation of China under Grant Nos.61873284,61321003,and 62373374.
文摘This study investigates the robust feedback set stabilization of switched logic control networks(SLCNs)with state-dependent uncertain switching and control constraints.First,based on the properties of the semi-tensor product of matrices and the vector representation of logic,an SLCN with state-dependent uncertain switching and control constraints is expressed in algebraic form.Second,an input transformation and a switching model are constructed to transfer the original SLCN into one with a free control input and arbitrary switching.The equivalence between the set stabilizability of the original SLCN and that of the resulting SLCN is established.Based on such equivalence,the authors propose a necessary and sufficient condition for robust feedback set stabilizability.Finally,an example is presented to demonstrate the application of the results obtained.
基金National Natural Science Foundation of China and Provincial Natural Science Foundafion of Guangdong, China.
文摘An artificial neural network(ANN) and a self-adjusting fuzzy logiccontroller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented.The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and theintelligent control for weld seam tracking with FLC. The proposed neural network can produce highlycomplex nonlinear multi-variable model of the GTAW process that offers the accurate prediction ofwelding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts thecontrol parameters on-line automatically according to the tracking errors so that the torch positioncan be controlled accurately.
基金Doctoral Research Fund of State Educational Commission of China.
文摘According to the features of Intelligent Network(IN)service logic,a method based ondata table to implement IN Service Logic is proposed.The method supports dynamic additionof IN service logic.
文摘Development of energy-resources-poor remote rural areas of the world has been discussed by many in the past. Harnessing locally available renewable energy resources as an environmentally friendly option is gaining momentum. Smart Integrated Renewable Energy Systems (SIRES) offer a resilient and economic path to “energize” the area and reach this goal. This paper discusses its intelligent control using neural networks and fuzzy logic.
文摘In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).
文摘In the present work,autonomous mobile robot(AMR)system is intended with basic behaviour,one is obstacle avoidance and the other is target seeking in various environments.The AMR is navigated using fuzzy logic,neural network and adaptive neurofuzzy inference system(ANFIS)controller with safe boundary algorithm.In this method of target seeking behaviour,the obstacle avoidance at every instant improves the performance of robot in navigation approach.The inputs to the controller are the signals from various sensors fixed at front face,left and right face of the AMR.The output signal from controller regulates the angular velocity of both front power wheels of the AMR.The shortest path is identified using fuzzy,neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation.The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation,in particular with curved/irregular obstacles.
基金China Postdoctoral Science Foundation and Natural Science of Heibei Province!698004
文摘In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.
基金supported by the National Science and Technology Major Project of China(2013ZX03005007-004)the National Natural Science Foundation of China(6120101361671179)
文摘Device to device(D2 D) multi-hop communication in multicast networks solves the contradiction between high speed requirements and limited bandwidth in regional data sharing communication services. However, most networking models demand a large control overhead in eNodeB. Moreover, the topology should be calculated again due to the mobility of terminals, which causes the long delay. In this work, we model multicast network construction in D2 D communication through a fuzzy mathematics and game theory based algorithm. In resource allocation, we assume that user equipment(UE) can detect the available frequency and the fuzzy mathematics is introduced to describe an uncertain relationship between the resource and UE distributedly, which diminishes the time delay. For forming structure, a distributed myopic best response dynamics formation algorithm derived from a novel concept from the coalitional game theory is proposed, in which every UE can self-organize into stable structure without the control from eNodeB to improve its utilities in terms of rate and bit error rate(BER) while accounting for a link maintenance cost, and adapt this topology to environmental changes such as mobility while converging to a Nash equilibrium fast. Simulation results show that the proposed architecture converges to a tree network quickly and presents significant gains in terms of average rate utility reaching up to 50% compared to the star topology where all of the UE is directly connected to eNodeB.
文摘A netal network-based fuzzy self-tuning PID controller theh is prope to control the dynamic process ofpulse TIG welding uses fuzzy logic and neural network to adjust the parameters of PID controller on line, and simula-tion results show that the controller has not only simple nonlinear control of tfuzzy control, but also the learning capabil-ity and adaptability of neural netwrk.
文摘Analyzing gene network structure is an important way to discover and understand some unknown relevant functions and regulatory mechanisms of organism at the molecular level. In this work, mutual information networks and Boolean logic networks are constructed using the methods of reverse modeling based on gene expression profiles in lung tissues with and without cancer. The comparison of these network structures shows that average degree, the proportion of non-isolated nodes, average betweenness and average coreness can distinguish the networks corresponding to the lung tissues with and without cancer. According to the difference of degree, betweenness and coreness of each gene in these networks, nine structural key genes are obtained. Seven of them which are related to lung cancer are supported by literatures. The remaining two genes AKT1 and RBL may have important roles in the formation, development and metastasis of lung cancer. Furthermore, the contrast of these logic networks suggests that the distributions of logic types are obviously different. The structural differences can help us to understand the mechanism of formation and development of lung cancer.
