双油罐修理线的天车运送周期性排序问题的求解

作为机车油罐修理中的一个重要资源，天车的排序直接影响系统的、生产率。本文研究了产品在系统的一边装栽、而在另一边卸栽的双油罐修理线的天车周期性排序问题。工件在每个工作台需要加工一定的时间，工作台之间没有缓冲工作台，一台天车用于工作站之间工件的运送，目标是对运送进行排序以极小化生产周期。为了求解这个问题，本文提出了一个混合整数线性规划模型，量化示例表明所提出的方法是有效的。     

基于博弈论的自适应波束成形算法

针对自适应波束成形算法中用户之间的动态相互干扰,提出了一种基于博弈论的自适应波束成形算法,构建了波束成形博弈算法数学模型。该算法将不同用户的发射权值调整描述为一个多用户的博弈过程,通过数学推导论证了基于博弈论的自适应波束成形算法纳什平衡点的存在性和唯一性,设计了收敛的发射权值更新算法。通过仿真实验验证了算法的性能以及讨论了代价因子、可调参数和收敛精度等参数对算法的影响。     

结合非线性CS算法的UWB-SAR运动补偿

载机飞行不稳对SAR成像质量影响很大,对超宽带SAR系统尤其如此,提出了一种结合非线性CS算法的超宽带SAR运动补偿方法。通过采用引入高次相位补偿和非正交旁瓣抑制的改进非线性CS算法,减小了超宽带SAR成像中的相位误差。通过成像前的常规一阶运动补偿和距离徙动校正后的分块二阶运动补偿,有效补偿了超宽带SAR中的方位空变运动误差,提高了飞行不稳时的成像质量。该方法在运动补偿时考虑了方位空变运动误差随距离的变化,针对载机运动误差变化较快的情况也给出了处理办法。仿真和实测数据的处理结果验证了该方法的有效性。     

贝叶斯方法在代码转换器可靠性验证试验中的应用

针对高可靠性产品的可靠性验证试验时间长、费用高等特点,将贝叶斯方法引用到可靠性验证试验中。推导出服从指数分布的贝叶斯可靠性序贯试验方法。将贝叶斯方法与经典序贯试验方法进行比较,贝叶斯方法较大的减少了试验量和试验时间。在某型飞机代码转换器的国产化可靠性验证试验中应用该方法,实际应用结果验证了该方法的有效性。     

属性添加情况下的概念格重构算法

研究在给定的形式背景下,通过逐步添加属性建造概念格的方法.一般地,初始概念格可由部分属性生成.当添加属性时,得到了新节点与它在原概念格中的直接父节点是一一对应的结论.证明了所有新节点在原概念格中的直接父节点,只可能在某个节点和它的子节点中.基于上述性质,给出了添加属性情况下的重建概念格算法.此算法的优点是生成新节点的范围小,构建概念格的速度快.     

Hardware system design for UHF surface velocities radar

UHF surface velocities radar is developed based on the successful ocean state measuring and analyzing radar system. The design method for UHF radar system is presented. It is designed to operate at UHF channel, and the transmit power is under 5W. Maximum range of field test over fresh water can be a kilometer. The field tests at Tangsun River and at Majiatan and Gaohazhou proved that USVR System can be used successfully.     

Ad hoc distributed mutual exclusion algorithm based on token-asking

The solution of distributed mutual exclusion is difficult in Ad hoc networks owing to dynamic topologies and mobility. Based on the analysis of the properties of Ad hoc networks and the disadvantages of the traditional algorithms, an improved Ad hoc system model was given and a novel algorithm was presented as AHDME (Ad Hoc Distributed Mutual Exclusion); it was based on the token-asking algorithms. It utilized broadcast to search for the token and to decrease the message complexity of multi-hop Ad hoc networks. Lamport's timestamp was improved to ensure the time sequence and to prevent nodes from starvation. When compared to traditional algorithms, AHDME does not require the fixed size of request queues and the global system information, which adapts itself to the frequent arrival/departures and the limited computing capability of nodes in Ad hoc networks. Performance analysis and simulation results show that the AHDME algorithm has low message complexity, small space complexity, and short response delay.     

Distributed power control algorithm based on game theory for wireless sensor networks

Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network.Because the character of wireless sensor networks is restrictive energy,this paper proposes a distributed power control algorithm based on game theory for wireless sensor networks which objects of which are reducing power consumption and decreasing overhead and increasing network lifetime.The game theory and OPNET simulation shows that the power control algorithm converges to a Nash Equilibrium when decisions are updated according to a better response dynamic.     

The self-organizing worm algorithm

A new multi-modal optimization algorithm called the self-organizing worm algorithm (SOWA) is presented for optimization of multi-modal functions.The main idea of this algorithm can be described as followsdisperse some worms equably in the domain;the worms exchange the information each other and creep toward the nearest high point;at last they will stop on the nearest high point.All peaks of multi-modal function can be found rapidly through studying and chasing among the worms.In contrast with the classical multi-modal optimization algorithms,SOWA is provided with a simple calculation,strong convergence,high precision,and does not need any prior knowledge.Several simulation experiments for SOWA are performed,and the complexity of SOWA is analyzed amply.The results show that SOWA is very effective in optimization of multi-modal functions.     

Adaptive immune-genetic algorithm for global optimization to multivariable function

An adaptive immune-genetic algorithm(AIGA)is proposed to avoid premature convergence and guarantee the diversity of the population.Rapid immune response (secondary response),adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability,greatly increase the converging speed,and decrease locating the local maxima due to the premature convergence.The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly,guarantees the diversity,stability and good searching ability.