In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task i...In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.展开更多
Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fa...Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.展开更多
Two-stage problem of stochastic convex programming with fuzzy probability distribution is studied in this paper. Multicut L-shaped algorithm is proposed to solve the problem based on the fuzzy cutting and the minimax ...Two-stage problem of stochastic convex programming with fuzzy probability distribution is studied in this paper. Multicut L-shaped algorithm is proposed to solve the problem based on the fuzzy cutting and the minimax rule. Theorem of the convergence for the algorithm is proved. Finally, a numerical example about two-stage convex recourse problem shows the essential character and the efficiency.展开更多
This paper analyzes the problems in image encryption and decryption based on chaos theory. This article introduces the application of the two-stage Logistic algorithm in image encryption and decryption, then by inform...This paper analyzes the problems in image encryption and decryption based on chaos theory. This article introduces the application of the two-stage Logistic algorithm in image encryption and decryption, then by information entropy analysis it is concluded that the security of this algorithm is higher compared with the original image;And a new image encryption and decryption algorithm based on the combination of two-stage Logistic mapping and <i>M</i> sequence is proposed. This new algorithm is very sensitive to keys;the key space is large and its security is higher than two-stage Logistic mapping of image encryption and decryption technology.展开更多
针对新能源电力系统中源荷不确定性导致的系统调度灵活性严重不足问题,文中提出了一种考虑源荷不确定性的电力系统两阶段鲁棒优化模型。根据源荷不确定性特征,结合K-means法和鲁棒优化理论,在多时间尺度对电力系统灵活性需求进行量化。...针对新能源电力系统中源荷不确定性导致的系统调度灵活性严重不足问题,文中提出了一种考虑源荷不确定性的电力系统两阶段鲁棒优化模型。根据源荷不确定性特征,结合K-means法和鲁棒优化理论,在多时间尺度对电力系统灵活性需求进行量化。首先,建立日前鲁棒调度模型,充分挖掘火电机组、抽水蓄能等资源的灵活调节潜力,将火电灵活改造及抽水蓄能抽发状态作为模型的第一阶段决策变量,各灵活资源的出力作为第二阶段决策变量,并以灵活改造成本、碳排放成本及运行成本最小为优化目标。其次,在模型求解中,将所建立的两阶段鲁棒模型转化为相对独立的主问题和子问题,并采用列与约束生成(column and constraint generation,C&CG)算法和强对偶理论反复迭代,以逼近最优解。最后,通过算例验证,所提出的优化调度策略在满足灵活性需求的基础上,统筹各类资源,实现了系统中经济性、环保性、灵活性的均衡,并增强了对源荷不确定性风险的抵御能力。展开更多
针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在...针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在目标转弯时的跟踪精度。同时,利用检测算法提供的速度信息,在轨迹更新时对物体速度进行校正,以改善在目标速度突变时的跟踪效果。此外,采用基于置信度的两阶段匹配策略,以降低低置信度检测框对跟踪结果的影响。在nuScenes验证集上对所提出的三维目标检测与跟踪算法进行了性能评估,并通过消融实验验证了算法中各模块的有效性。实验结果表明,基于CTRA模型的点云多目标跟踪算法在跟踪精度上优于基于简单模型的算法,在目标转弯和速度突变场景下的跟踪效果显著提升,且跟踪过程中身份切换次数大幅减少。展开更多
基金supported by National Natural Science Foundation of China (Grant Nos. 60433020, 60175024 and 60773095)European Commission under grant No. TH/Asia Link/010 (111084)the Key Science-Technology Project of the National Education Ministry of China (Grant No. 02090),and the Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, P. R. China
文摘In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.
文摘Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the K-means clustering algorithm, an improved K-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.
文摘Two-stage problem of stochastic convex programming with fuzzy probability distribution is studied in this paper. Multicut L-shaped algorithm is proposed to solve the problem based on the fuzzy cutting and the minimax rule. Theorem of the convergence for the algorithm is proved. Finally, a numerical example about two-stage convex recourse problem shows the essential character and the efficiency.
文摘This paper analyzes the problems in image encryption and decryption based on chaos theory. This article introduces the application of the two-stage Logistic algorithm in image encryption and decryption, then by information entropy analysis it is concluded that the security of this algorithm is higher compared with the original image;And a new image encryption and decryption algorithm based on the combination of two-stage Logistic mapping and <i>M</i> sequence is proposed. This new algorithm is very sensitive to keys;the key space is large and its security is higher than two-stage Logistic mapping of image encryption and decryption technology.
文摘针对新能源电力系统中源荷不确定性导致的系统调度灵活性严重不足问题,文中提出了一种考虑源荷不确定性的电力系统两阶段鲁棒优化模型。根据源荷不确定性特征,结合K-means法和鲁棒优化理论,在多时间尺度对电力系统灵活性需求进行量化。首先,建立日前鲁棒调度模型,充分挖掘火电机组、抽水蓄能等资源的灵活调节潜力,将火电灵活改造及抽水蓄能抽发状态作为模型的第一阶段决策变量,各灵活资源的出力作为第二阶段决策变量,并以灵活改造成本、碳排放成本及运行成本最小为优化目标。其次,在模型求解中,将所建立的两阶段鲁棒模型转化为相对独立的主问题和子问题,并采用列与约束生成(column and constraint generation,C&CG)算法和强对偶理论反复迭代,以逼近最优解。最后,通过算例验证,所提出的优化调度策略在满足灵活性需求的基础上,统筹各类资源,实现了系统中经济性、环保性、灵活性的均衡,并增强了对源荷不确定性风险的抵御能力。
文摘针对简单运动模型在复杂驾驶环境多目标跟踪表现不佳的问题,提出了一种基于恒定转弯率和加速度(constant turn rate and acceleration,CTRA)模型的点云多目标跟踪方法。通过采用包含角速度信息的运动模型来描述目标的运动轨迹,可提高在目标转弯时的跟踪精度。同时,利用检测算法提供的速度信息,在轨迹更新时对物体速度进行校正,以改善在目标速度突变时的跟踪效果。此外,采用基于置信度的两阶段匹配策略,以降低低置信度检测框对跟踪结果的影响。在nuScenes验证集上对所提出的三维目标检测与跟踪算法进行了性能评估,并通过消融实验验证了算法中各模块的有效性。实验结果表明,基于CTRA模型的点云多目标跟踪算法在跟踪精度上优于基于简单模型的算法,在目标转弯和速度突变场景下的跟踪效果显著提升,且跟踪过程中身份切换次数大幅减少。
