This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is ...This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.展开更多
Blast furnace （BF） ironmaking process has complex and nonlinear dynamic characteristics. The molten iron temperature （MIT） as well as Si, P and S contents of molten iron is difficult to be directly measured online...Blast furnace （BF） ironmaking process has complex and nonlinear dynamic characteristics. The molten iron temperature （MIT） as well as Si, P and S contents of molten iron is difficult to be directly measured online, and large-time delay exists in offline analysis through laboratory sampling. A nonlinear multivariate intelligent modeling method was proposed for molten iron quality （MIQ） based on principal component analysis （PCA） and dynamic ge- netic neural network. The modeling method used the practical data processed by PCA dimension reduction as inputs of the dynamic artificial neural network （ANN）. A dynamic feedback link was introduced to produce a dynamic neu- ral network on the basis of traditional back propagation ANN. The proposed model improved the dynamic adaptabili- ty of networks and solved the strong fluctuation and resistance problem in a nonlinear dynamic system. Moreover, a new hybrid training method was presented where adaptive genetic algorithms （AGA） and ANN were integrated, which could improve network convergence speed and avoid network into local minima. The proposed method made it easier for operators to understand the inside status of blast furnace and offered real-time and reliable feedback infor- mation for realizing close-loop control for MIQ. Industrial experiments were made through the proposed model based on data collected from a practical steel company. The accuracy could meet the requirements of actual operation.展开更多
To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are anal...To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.展开更多
A realistic model of neural networks was proposed in this paper.The dynamicprocess of neural impulse discharging was considered.The equations of the model correspondto postsynaptic potentials,receptor potentials,initi...A realistic model of neural networks was proposed in this paper.The dynamicprocess of neural impulse discharging was considered.The equations of the model correspondto postsynaptic potentials,receptor potentials,initial segment graded potentials and the impulsetrain along the axon respectively.To solve the equations numerically,a recurrent algorithm and itscorresponding flow chart was also developed.The simulation results can imitate adaptation,post-excitation inhibition,and phase locking of sensory receptors;they can also imitate the transientresponses of lateral inhibitory network and Mach band phenomenon when they trended to besteady.The simulation results also showed that the lateral inhibitory network was sensitive tomoving objects.展开更多
Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighin...Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighing system for broiler chickens can only weigh the broiler chicken in the monitoring area.Usually,it makes poor weight prediction due to poor segmentation especially when the broiler chicken is flapping its wings.To solve these issues,we developed one simple and low-cost weighing system with high stability and accuracy.A validity value extraction method from dynamic weighing was proposed.Then,an improved amplitude-limiting filtering algorithm and a BP neural networks model were developed to avoid accidental interference.The BP neural networks model used daily weight gain,day-age,average velocity,and the weight data after filtering algorithm as the input layer.The weighing system was tested in a commercial Beijing Fatty Chickens house with Beijing Fatty Chickens.We tested thirteen groups of Beijing Fatty Chickens of different weights,from 500 g to 1800 g in intervals of 100 g,using the three different methods:no filtering algorithm or BP neural networks,only the improved amplitude-limiting filtering algorithm and a hybrid of the improved amplitude-limiting filtering algorithm and BP neural networks.The results showed that the hybrid algorithm had a better performance in minimizing the error,lowering from the original 6%down to 3%.The accurate weight data was transmitted to the remote service platform for further decision-making,such as activity analysis,feeding management,and health alerts.展开更多
With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation method...With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.展开更多
Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resu...Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.展开更多
基金funded by the Joint Funds of the National Natural Science Foundation of China (61079001)
文摘This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.
基金Sponsored by National Natural Science Foundation of China(61290323,61333007,614730646)IAPI Fundamental Research Funds(2013ZCX02-09)+1 种基金Fundamental Research Funds for the Central Universities of China(N130508002,N130108001)National High-tech Research and Development Program of China(2015AA043802)
文摘Blast furnace （BF） ironmaking process has complex and nonlinear dynamic characteristics. The molten iron temperature （MIT） as well as Si, P and S contents of molten iron is difficult to be directly measured online, and large-time delay exists in offline analysis through laboratory sampling. A nonlinear multivariate intelligent modeling method was proposed for molten iron quality （MIQ） based on principal component analysis （PCA） and dynamic ge- netic neural network. The modeling method used the practical data processed by PCA dimension reduction as inputs of the dynamic artificial neural network （ANN）. A dynamic feedback link was introduced to produce a dynamic neu- ral network on the basis of traditional back propagation ANN. The proposed model improved the dynamic adaptabili- ty of networks and solved the strong fluctuation and resistance problem in a nonlinear dynamic system. Moreover, a new hybrid training method was presented where adaptive genetic algorithms （AGA） and ANN were integrated, which could improve network convergence speed and avoid network into local minima. The proposed method made it easier for operators to understand the inside status of blast furnace and offered real-time and reliable feedback infor- mation for realizing close-loop control for MIQ. Industrial experiments were made through the proposed model based on data collected from a practical steel company. The accuracy could meet the requirements of actual operation.
文摘To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.
基金Supported by National Natural Science Foundation of China
文摘A realistic model of neural networks was proposed in this paper.The dynamicprocess of neural impulse discharging was considered.The equations of the model correspondto postsynaptic potentials,receptor potentials,initial segment graded potentials and the impulsetrain along the axon respectively.To solve the equations numerically,a recurrent algorithm and itscorresponding flow chart was also developed.The simulation results can imitate adaptation,post-excitation inhibition,and phase locking of sensory receptors;they can also imitate the transientresponses of lateral inhibitory network and Mach band phenomenon when they trended to besteady.The simulation results also showed that the lateral inhibitory network was sensitive tomoving objects.
基金supported by Key Technologies Research and Development Program(CN),funding number,2018YFE0108500the International Cooperation Fund Project of Beijing Academy of Agriculture and Forestry Sciences,funding number 2019HP002Beijing Science and Technology Planning,funding number Z191100004019007。
文摘Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighing system for broiler chickens can only weigh the broiler chicken in the monitoring area.Usually,it makes poor weight prediction due to poor segmentation especially when the broiler chicken is flapping its wings.To solve these issues,we developed one simple and low-cost weighing system with high stability and accuracy.A validity value extraction method from dynamic weighing was proposed.Then,an improved amplitude-limiting filtering algorithm and a BP neural networks model were developed to avoid accidental interference.The BP neural networks model used daily weight gain,day-age,average velocity,and the weight data after filtering algorithm as the input layer.The weighing system was tested in a commercial Beijing Fatty Chickens house with Beijing Fatty Chickens.We tested thirteen groups of Beijing Fatty Chickens of different weights,from 500 g to 1800 g in intervals of 100 g,using the three different methods:no filtering algorithm or BP neural networks,only the improved amplitude-limiting filtering algorithm and a hybrid of the improved amplitude-limiting filtering algorithm and BP neural networks.The results showed that the hybrid algorithm had a better performance in minimizing the error,lowering from the original 6%down to 3%.The accurate weight data was transmitted to the remote service platform for further decision-making,such as activity analysis,feeding management,and health alerts.
基金supported by the National Nature Science Foundation of China(NSFC 60622110,61471220,91538107,91638205)National Basic Research Project of China(973,2013CB329006),GY22016058
文摘With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.
文摘Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.
