GCC编译器中间代码层控制流扩充研究

本文首先对CFCSS(控制流检错算法)进行了介绍,对GCC编译器的运行流程进行了简要分析,再次给出了在GCC编译器中扩充CFCSS算法的具体方法,最后通过故障注入实验对扩充后的GCC进行了有效性验证。实验表明,扩充了CFCSS算法的GCC编译器所编译的程序在运行过程中具有控制流检错能力。这为我们下一步的故障定位和故障恢复提供了有力的支持,为解决星载计算机的运行故障奠定了基础。     

基于种群多样性的自适应PSO算法求解VRPSPD问题

为有效求解逆向物流车辆路径(VRPSPD)模型,本文提出一种基于种群多样性的自适应PSO算法(SDAPSO)。在SDAPSO运行时,根据种群多样性,自适应地对种群中运行较差的粒子进行扰动操作,提升这些粒子向最优解收敛的能力;同时,对全局最优粒子进行概率扰动,以增加种群的多样性。标准检测函数的仿真结果表明SDAPSO算法是对基本PSO算法的有效改进。在对VRPSPD模型求解中,通过与其它粒子群算法相比,表明SDAPSO是求解该类问题的一种有效方法。     

火车车轮成形新工艺的开发研究

在比较分析技术先进国家的火车车轮成形工艺流程后，采用试验研究和数值模拟相结合方法，研究开发出优化的新工艺。钢坯先在一台水压机上预成形制坯，再在另一台压机上模锻，经轧制扩径后冲孔压弯。这样的生产线效益最好     

A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs

The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.     

Optimization algorithm for ultra-sparse arrays in regional energy focusing

The setting of ultra-sparse arrays is one of key factors for the performance of regional energy focusing.This paper focuses on one of its typical applications:Precision Electronic Warfare by UAV,and proposes an optimization algorithm for ultra-sparse arrays to maximize the performance of regional energy focusing.First,we establish the joint optimization model of the interference waveform and the positions of elements.Second,particle swarm optimization (PSO) is adopted to iterate the positions of elements.Then,the target value of the subproblem is solved for the fitness criterion,as the current array is known in each step of the iteration.Finally,we get the optimal solution of the interference waveform and the ultra-sparse array by this method.Numerical results indicate that the algorithm can optimize the array effectively,and improve the indicators of regional energy focusing obviously.Whether the localization errors of elements exist or not,the algorithm provides a better performance than existing algorithms which only design the interference waveform.     

Adaptive simulated annealing particle swarm optimization algorithm

Particle swarm optimization is widely used in various fields because of the few parameters to be set and the simple calculation structure.In order to improve the optimization speed and accuracy of the PSO,and to avoid falling into the local optimal solution,an adaptive simulated annealing PSO is proposed,which uses the hyperbolic tangent function to control the inertia weight factor for nonlinear adaptive changes,uses linear change strategies to control 2 learning factors,introduces the simulation annealing operation,set a temperature according to the initial state of the population,guide the population to accept the difference solution with a certain probability according to the Metropolis criterion,and ensure the ability to jump out of the local optimal solution.To verify the effect of the algorithm proposed in this paper,7 typical test functions and 5 algorithms proposed in the literature are selected for comparison and testing.According to the average value,standard deviation and number of iterations of the optimization results,the algorithm proposed in this paper has greatly improved the iteration accuracy,convergence speed and stability so as to overcome the shortcomings of particle swarm optimization.     

基于GWO-SVM的下肢假肢穿戴者骑行相位识别

针对下肢假肢穿戴者骑行相位识别的问题,提出基于灰狼算法优化的支持向量机(GWO-SVM)分类模型. 建立下肢多源信息系统,采集膝关节、踝关节的加速度信号以及膝关节角度信号. 应用奇异值分解,对采集到的信号进行降噪处理. 在对信号进行降噪处理之后,为了避免单一信号不确定的影响,从数据冗余角度,选取各信号的特征点,开展归一化处理,组成多维特征向量,作为SVM分类模型的输入. 为了能够进一步提高分类精度,加强全局优化能力,利用GWO算法对核参数进行优化. 通过与PSO-SVM分类模型、GA-SVM分类模型对比表明,基于GWO优化的SVM分类模型对骑行相位的识别率为94%,高于其他方法优化的SVM分类模型.     

面向对象轮廓约束GGVF Snake的建筑物边界优化方法

分析高分辨率遥感影像中建筑物的特征和常用方法提取建筑物存在边界漏检误检导致的边界不规则等问题,提出面向对象轮廓约束广义梯度矢量流（GGVF）Snake模型的建筑物边界优化方法. 在利用分类法获取建筑物轮廓的初始结果基础上,自动提取每个建筑物轮廓线作为GGVF Snake的初始轮廓线,获取各轮廓外接矩形进行对象裁剪,提取每个建筑物的子图对象. 对每个子图对象进行Canny边缘检测,结合Hough变换提取直线特征,输入到广义梯度矢量流模型的迭代求解中快速最小化能量函数,实现对象级建筑物轮廓的精确优化. 实验结果表明,利用提出的方法能够自动获取初始建筑物的轮廓信息,提高优化的自动化程度;基于对象级的边缘检测与直线特征的加入,有助于GGVF Snake快速拟合,准确地平滑建筑物边缘且准确度高. 相对于其他对比方法,本文方法的轮廓优化总体精度和综合值均有提升,可以作为有效提升分类原理获取的建筑物轮廓的优化后处理手段,提高了建筑物提取的精度.     

多能互补系统综合效益最大化控制

针对光伏发电出力随机性大,波动较强等问题,提出了一种多能互补系统综合效益最大化控制策略,控制双向DC-DC逆变器实现蓄电池、超级电容器和氢燃料电池充放电,保证直流母线电压的稳定。然后利用蓄电池和超级电容器所具有的能量互补特点,实现对储能系统的优化管理。采用光伏与混合储能协调互补的方法,可以减少储能系统的容量、降低蓄电池循环充放电的次数、延长储能装置的使用寿命,并获得较好的光伏波动平抑效果,相比于常规的控制方法,减少了运行投资成本,具有显著的经济效益。     

超超临界锅炉喷氨优化与调整

某厂超超临界燃煤火电机组已完成超低排放改造,实现烟尘、SO₂和NO_x排放浓度远低于10 mg/Nm³、35 mg/Nm³和50 mg/Nm³。但锅炉中脱硝系统氨逃逸过大,导致脱硝系统后面的烟道内空预器及低温省煤器堵塞严重,严重影响了机组运行安全。通过测量该锅炉SCR反应器出口NOx浓度及氨逃逸量等参数,基于实测数据分析对锅炉喷氨量进行优化调整,降低SCR反应器出口氨逃逸量,从而有效缓解空预器及低温省煤器的堵塞状况,保证机组的高效安全运行。