基于基因表达式编程的装备测试需求建立方法

在测试领域,被测对象的测试需求一直是一个比较模糊的概念,本文参考TYX公司对测试需求的定义,针对某型装备的测试需求,开展了基因表达式编程算法的研究.根据制导电子箱的电路特点,设计了基于IEEE 1641标准的运算符集合和变量集合、基于R-square的适应度函数、基于精英主义和轮盘赌算法的选择策略.采用部分映射、启发式变异算子和面向测试应用的插串算子和重组算子.提出了一种基于基因表达式编程算法的装备测试需求建立方法,并进行了系统实现.实验结果表明,该方法可以根据装备的观测数据生成制导电子箱的测试模型,得到装备准确的测试信号需求和具有参考意义的测试方法,此方法具有一定的通用性.     

研发型企业多项目人力资源调度研究——基于蚁群优化的超启发式算法

现阶段,研发型企业的项目处于多项目环境下,为了解决多项目并行时人力资源争夺问题,本文针对该类企业多项目管理中人力资源调度进行优化研究,以考虑项目延期惩罚成本的最小总成本为目标函数,将现实问题抽象建模。基于国内外的研究提出了一种超启发式算法进行求解,该算法将人力资源调度问题分为项目活动分配和人员选择项目活动两个部分,采用蚁群优化作为高层启发式策略搜索低层启发式规则,再进一步根据规则解构造出可行解。最后本研究设计多组仿真实验与启发式规则进行对比,结果表明该算法有较好的搜索性能,为人力资源的调度问题提供了新的解决方案。     

飞机移动生产线作业调度问题的启发式算法

飞机移动生产线作业调度问题是一种具有特殊约束的项目调度问题。综合分析了飞机移动生产线的作业顺序关序、资源约束、空间限制的多重特点,建立了以最小装配完工时间为目标的模型。将问题分为确定作业的优先顺序和选择作业人数的两个阶段进行决策,设计了由不同规则组合成27种启发式方法。通过比较启发式方法在250个测试问题集的表现,确定最有效的解决此问题的方法。     

开设“数字逻辑”综合设计性实验的探索

文章对“数字逻辑”实验教学改革进行了探讨。开设综合设计性实验，采用启发式教学方法，加强对学生实验技能的训练，培养学生的创新能力和综合设计能力，取得了良好的教学效果。还对进一步深化实践教学改革进行了讨论。     

带恶化工件的PFS调度的混合遗传算法北大核心CSSCI

研究了带恶化工件的置换流水车间调度问题,其中工件的加工时间是与开始时间有关的线性函数,考虑不同工件在不同机器上具有不同的恶化率,以最小化最大完工时间为目标,建立数学规划模型,进而提出了一种混合遗传算法来求解。该算法引入一种启发式规则以产生m-1条染色体改进初始种群的40%,结合遗传算法的初始种群产生方法共同生成种群,设计遗传参数自适应调节。仿真实验测试和对比了启发式法、遗传算法和混合遗传算法三种求解方法,实验结果表明所提出的混合遗传算法能更有效地求解这类NP-hard问题。     

带恶化工件的PFS调度的混合遗传算法

研究了带恶化工件的置换流水车间调度问题,其中工件的加工时间是与开始时间有关的线性函数,考虑不同工件在不同机器上具有不同的恶化率,以最小化最大完工时间为目标,建立数学规划模型,进而提出了一种混合遗传算法来求解。该算法引入一种启发式规则以产生m-1条染色体改进初始种群的40%,结合遗传算法的初始种群产生方法共同生成种群,设计遗传参数自适应调节。仿真实验测试和对比了启发式法、遗传算法和混合遗传算法三种求解方法,实验结果表明所提出的混合遗传算法能更有效地求解这类NP-hard问题。     

基于GEP的模拟电路系统辨识技术

根据模拟电路系统的输入输出数据辨识,其数学模型是基于模型的装备快速测试方法中的关键技术之一.针对模拟电路系统的特点,设计了基于R-square的适应度函数、基于精英主义和轮盘赌算法的选择策略,采用部分映射、启发式变异算子,提出了一种基于基因表达式编程算法的模拟电路系统辨识方法并进行了系统实现.与MATLAB系统辨识工具...     

关于药物信息管理类产品的设计检验方法

目的通过检视一款药物信息管理类产品的设计与构建流程,总结其中所采用的设计检验方法,有效地帮助并加速产品的设计改进与迭代过程,满足相关用户的需求。方法对产品的初级交互原型采用启发式评估法进行评估。根据评估结果及需要改进的易用性问题优先级对产品原型进行设计迭代。对迭代后的产品原型进行深入的易用性测试。为评估易用性测试给用户带来的任务工作量,了解产品原型仍然存在的易用性问题以及探索产品细节的用户体验问题,依次采用任务负荷指数评估(NASA-TLX),系统易用性量表法与半结构式访谈来进行产品测试。结论融合启发式评估法与易用性测试,邀请可用性专家与实际用户,分别对初级交互原型与改进后的产品原型进行评估,加速并优化了相应产品的设计流程,提升了用户体验的满意度。     

数字电路可测性设计的一种故障定位方法

在逻辑函数ReedMuller模式的电路可测性设计方面，文章采用AND门阵列和XOR门树结构来设计电路，提出了一种设计方案，可实现任意逻辑函数的功能，而且所得电路具有通用测试集和完全可故障定位的特点。给出了进行故障定位的方法，并可把它应用于其他相关电路的可测性设计。     

新型航政艇舱室的综合减振降噪设计及效果分析

介绍了新型航政艇的综合减振降噪设计技术：推进轴系选择柔性传动方式，主机组整体设计成半弹性照握至统，在船体结构上粘贴阻尼钢板。采用振动舒适性标准评价航政艇的船体振动。实船测试结果表明，综合减振降噪措施显著地降低了船体结构振动和舱室噪声。研究成果对小型船舶的舒适性设计有重要的参考意义。     

Optimization of Hot Rolled Coil Widths Using a Genetic Algorithm

This paper focuses on the assortment problem in the steel industry; with the help of the genetic algorithm, it attempts to determine the optimum width of the parent stock given a set of forecasted customer widths so that the trim loss is minimized. For each given set of forecasted customer widths, an attempt is made to find a single width of the mother coil to be manufactured and kept in stock. In the genetic algorithm, six different selection schemes are considered. A number of test problems are taken up for different selection schemes. Evaluated against the maximum fitness value, the optimal mother coil width, and the generation number at which they are achieved, the elitism selection scheme shows consistently good results in all the test problems compared with the other selection mechanisms.     

基于电压灵敏度的模拟电路节点选择策略

测试节点选择问题是当前模拟电路故障诊断领域的研究热点之一。以节点电压灵敏度定义为基础,对电路中的故障状态进行模糊划分,定义测试节点上的电路故障模糊集,提出故障诊断能力指标的概念,并用其描述节点对故障的区分能力。在此基础上给出一种新的测试节点优选方法,最终实现以最少的节点数目覆盖最多的电路故障的目的,应用实例验证了新算法的有效性。     

An Improved Whale Optimization Algorithm for Feature Selection

Whale optimization algorithm (WOA) is a new population-based metaheuristic algorithm. WOA uses shrinking encircling mechanism, spiral rise, and random learning strategies to update whale’s positions. WOA has merit in terms of simple calculation and high computational accuracy, but its convergence speed is slow and it is easy to fall into the local optimal solution. In order to overcome the shortcomings, this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms, designs the average distance from itself to other whales as an adaptive neighborhood radius, and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies. The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution. A new whale optimization algorithm (HMNWOA) is proposed. The proposed algorithm inherits the global search capability of the original algorithm, enhances the exploitation ability, improves the quality of the population, and thus improves the convergence speed of the algorithm. A feature selection algorithm based on binary HMNWOA is proposed. Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection. The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features, and ensures that HMNWOA has strong search ability in the search feature space.     

Harmony search algorithm: application to the redundancy optimization problem

The redundancy optimization problem is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system performance, given different system-level constraints. This article presents an efficient algorithm based on the harmony search algorithm (HSA) to solve this optimization problem. The HSA is a new nature-inspired algorithm which mimics the improvization process of music players. Two kinds of problems are considered in testing the proposed algorithm, with the first limited to the binary series–parallel system, where the problem consists of a selection of elements and redundancy levels used to maximize the system reliability given various system-level constraints; the second problem for its part concerns the multi-state series–parallel systems with performance levels ranging from perfect operation to complete failure, and in which identical redundant elements are included in order to achieve a desirable level of availability. Numerical results for test problems from previous research are reported and compared. The results of HSA showed that this algorithm could provide very good solutions when compared to those obtained through other approaches.     

A Swarm Metaphor for Multiobjective Design Optimization

This paper presents a new optimization algorithm to solve multiobjective design optimization problems based on behavioral concepts similar to that of a real swarm. The individuals of a swarm update their flying direction through communication with their neighboring leaders with an aim to collectively attain a common goal. The success of the swarm is attributed to three fundamental processes: identification of a set of leaders, selection of a leader for information acquisition, and finally a meaningful information transfer scheme. The proposed algorithm mimics the above behavioral processes of a real swarm. The algorithm employs a multilevel sieve to generate a set of leaders, a probabilistic crowding radius-based strategy for leader selection and a simple generational operator for information transfer. Two test problems, one with a discontinuous Pareto front and the other with a multi-modal Pareto front is solved to illustrate the capabilities of the algorithm in handling mathematically complex problems. Three well-studied engineering design optimization problems (unconstrained and constrained problems with continuous and discrete variables) are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. The results clearly indicate that the swarm algorithm is capable of generating an extended Pareto front, consisting of well spread Pareto points with significantly fewer function evaluations when compared to the nondominated sorting genetic algorithm (NSGA).     

The impact of receiving the same items on consecutive computer adaptive test administrations

This study addresses item exposure in a Computerized Adaptive Test (CAT) when the item selection algorithm is permitted to present examinees with questions that they have already been asked in a previous test administration. The results indicate that the combined use of an adaptive algorithm to select items and latent trait theory to estimate person ability provides substantial protection from score contamination. The implications for constraints that prohibit examinees from seeing an item twice are discussed.     

双离心机法线加速度计动态校准中三参数余弦算法实现及应用

在双离心机的线加速度计动态校准中,三参数余弦算法贯穿在幅频特性、相频特性以及幅值线性度的整个测试中。本文详细描述了三参数余弦算法的概念和推导过程,以及在双离心机线加速度计动态校准数据处理过程中三参数余弦算法的程序实现;介绍了在幅频特性和相频特性中关于采集点的选取方式,降低由于采集点选择方式不同对数据拟合精度造成的影响。选取某型线加速度计为例,利用双离心机进行动态校准,对采集得到的数据点进行曲线拟合并得到测试结果,验证了所编写的三参数余弦算法程序的可行性。     

Efficient algorithms for ordinary differential equation model identification of biological systems

Algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.     

Test Vector Optimization Using Pocofan-Poframe Partitioning

This paper presents an automated POCOFAN-POFRAME algorithm that partitions large combinational digital VLSI circuits for pseudo exhaustive testing. In this paper, a simulation framework and partitioning technique are presented to guide VLSI circuits to work under with fewer test vectors in order to reduce testing time and to develop VLSI circuit designs. This framework utilizes two methods of partitioning Primary Output Cone Fanout Partitioning (POCOFAN) and POFRAME partitioning to determine number of test vectors in the circuit. The key role of partitioning is to identify reconvergent fanout branch pairs and the optimal value of primary input node N and fanout F partitioning using I-PIFAN algorithm. The number of reconvergent fanout and its locations are critical for testing of VLSI circuits and design for testability. Hence, their selection is crucial in order to optimize system performance and reliability. In the present work, the design constraints of the partitioned circuit considered for optimization includes critical path delay and test time. POCOFAN-POFRAME algorithm uses the parameters with optimal values of circuits maximum primary input cone size (N) and minimum fan-out value (F) to determine the number of test vectors, number of partitions and its locations. The ISCAS’85 benchmark circuits have been successfully partitioned, the test results of C499 shows 45% reduction in the test vectors and the experimental results are compared with other partitioning methods, our algorithm makes fewer test vectors.     

A greedy MLPG method for identifying a control parameter in 2D parabolic PDEs

In this paper, we consider coefficient inverse problems, which are associated with the identification of unknown time dependent control parameter and unknown solution of two-dimensional parabolic inverse problem with overspecialization at a point in the spatial domain. After suitable finite difference approximation of time variable, an MLPG method is used for spatial discretization. To improve the efficiency of the MLPG method, a greedy algorithm is used. In fact, using the greedy algorithm, we avoid using more points from the data site than absolutely necessary and therefore, the method becomes more efficient. Comparison of the different kind of point selection and the effect of noisy data are performed for four test problems while our last test problem considers a problem with unknown solution. The results reveal that the method is efficient.