指定逻辑的电路最小项扰动算法

针对现有的基于最小项的伪装算法不能稳定地生成最小项扰动电路,而且不能控制指定逻辑,使得该技术难以在实际电路中使用的问题,提出了一种最小项扰动算法,通过引入原始输入信号并缩小受逻辑门影响的最小范围,快速找到单个最小项扰动.同时,可以任意设计特定逻辑:特定输出、特定门和特定最小项.首先,将多输出电路分成多个单输出电路并指定任何输出;然后可以指定电路的任何门,并使用敏化和FAN技术获得所有扰动的最小项;最后,导入原始信号确定伪装电路中的唯一扰动最小项.该算法可用于设计只能被特定电路使用的电路模块.在ISCAS’89基准电路和OpenSPARC微处理器控制器进行测试,该算法可以在2 s内确定特定电路的最小项,正确率超过80%.证明文中最小项扰动算法可以灵活、高效地选择最小项进行修改,并且保证修改成功.     

用于二级优化的有限搜索算法

在计算机辅助逻辑设计理论中,最小项(零多维体)的相邻度是一个基本且十分有用的概念,一系列重要的二级优化算法都是以最小项相邻度概念为基础的.本文把最小项相邻度的概念推广到任意维多维体,定义了多维体方向集概念.并利用方向集概念成功地提出了优于张弛算法的二级优化算法:有限搜索法,即有限搜索法在造价优于张弛法的前提下,求取质蕴涵项的速度比张弛法快一到几个数量级,且有限搜索法扩展了张弛法的适用范围,可以综合输入变量大于20的布尔函数.     

基于近似计算与多数覆盖技术的电路面积优化

针对基于最小项的近似计算技术不适合解决大规模电路面积优化问题,提出一种采用乘积项和逻辑覆盖的电路面积近似计算技术优化算法.利用基于乘积项的多数覆盖技术实现近似逻辑函数搜索,用逻辑覆盖不相交运算实现近似函数错误率计算,可以有效地避免因输入变量增加和最小项数量激增导致算法效率低下甚至无法工作的问题.文中算法用C编程并经MC...     

从三值K图到三值b_i图的图块转换算法

1.引言 逻辑函数有两种基本的规范展开式,一是以Post代数为基础的最小项展开式。n变量、三值逻辑函数的最小项展开式可写成:     

一种逻辑优化的增强Quine-McCluskey算法

传统的Quine-McCluskey算法第一步多从只拥有最小项的真值表开始,而真值表中的最小项数目是算法时间复杂度的最重要因素,数目越大,算法完成需要的时间越多。本文采用增强Quine-McCluskey算法,对真值表大小进行控制,利用更多归约规则,从而减少算法执行时间。     

量化规则格及其渐进式构造

提取最小无冗余规则的关键是获取频繁封闭项集所对应的同交易项集集合中的最小项集.为了便于利用概念格提取这类规则,本文提出量化规则格,重点讨论在渐进构造格的过程中生成节点所对应的同交易项集中的最小项集的问题,并给出相应的算法.由于量化规则格和格节点对应的具有相同交易集的最小项集是渐进生成的,因此,它适合于从动态数据库中提取最小无冗余的关联规则并且可方便地实现规则的渐增更新.     

基于粒计算的逻辑函数快速粒约简算法

逻辑函数是描述数字电路中输入变量与输出变量之间逻辑因果关系的重要工具,研究逻辑函数的约简具有重要的理论和实际意义.针对计算机化简逻辑函数普遍存在的算法复杂度高、运算速度慢的问题,将粒计算思想与启发式搜索相结合来约简逻辑函数.首先将逻辑函数转化为最小项之和的表达形式,按照粒度由粗到细的次序,在不同粒度下的知识空间中利用吸收律和最小项之间的统计信息求取信息粒,当所有信息粒对应的最小项覆盖论域时,算法结束.算法由MATLAB编程实现.通过计算实例和算法复杂度分析证明了算法的快速性和有效性.     

关于多变量开关函数化简的问题

本文提出n维立方体及顶点编号方法的模型,应用它可以解决多变量开关函数化简问题。该模型能全面准确地揭示出n维空间中2~n个多变量开关函数每个最小项之间相邻的数学关系,相邻单元的组合关系,并给出合并最小项的差值合并定理及系统化简方法,方法简单直观,可获得最简表达式。这对大规模集成电路设计和计算机辅助开关电路设计具有十分重要的意义。     

基于人工选择的组合电路优化算法

针对一般组合电路的优化算法复杂、优化过程时间长、优化效率偏低等问题,提出一种人工选择方式下的组合电路优化算法。该算法模拟物种进化时的家养模式,将最小项作为基因,函数表达式作为染色体,把逻辑电路的优化过程演变为遵循电路定律的基因变异、重组、寻优的过程。算法通过有利的变异条件,提高了算法的收敛速度和效率。通过与简单免疫、多目标遗传、自适应免疫算法的实验比较,证明了该算法的有效性和优越性。     

改进的Q-M逻辑函数化简方法

为进一步提高逻辑函数的化简速度,提出一种改进的Q-M逻辑函数化简方法。在迭代比较过程中设置2个权值以缩减可合并蕴涵项集合的大小,只对满足条件的蕴涵项进行合并处理,得到全部质蕴涵项。构造质蕴涵项与最小项关联图,利用启发式规则得到能蕴涵全部最小项的最少质蕴涵项集合,从而得到逻辑函数的最小覆盖,完成逻辑函数化简。实验结果表明,该算法能降低迭代次数,减少逻辑函数的化简时间。     

商用电热开水器内胆结构优化

为提高商用电热开水器的使用寿命,将开水器水箱壁的变形量作为优化对象,通过变动水箱壁加强筋的位置来获得水箱壁的最小变形量;用ANSYS Workbench建立有限元模型并施加载荷和边界条件,进行结果分析和结构优化;根据最高水位和最低水位情况下变形量的大小与加强筋位置的分析比较,确定加强筋的最佳位置．结果表明经优化后,目标面（有加强筋的正面）的变形量降低29％．     

考虑多重覆盖的应急设施多级协同布局鲁棒优化

应急设施的合理布局是灾后实现物资高效、公平和稳定供应的重要保障.针对突发自然灾害的不确定性,研究基于多重覆盖的应急设施多级协同布局鲁棒优化问题.首先,提出多级设施选址下的多重覆盖水平函数,以最小覆盖水平和期望总成本最优为目标,建立应急设施多级协同选址双目标优化模型;其次,应用基数不确定集和p-鲁棒方法构建两类鲁棒优化模型,分别研究场景内不确定需求和随机场景对设施布局的影响;最后,以湖南省救灾备荒种子储备库选址为例进行实证分析,验证所提出优化模型的有效性.研究结果表明:多级协同布局相比传统布局方案更具优势;鲁棒优化模型能够有效应对不确定情形和随机场景下的物资需求;决策者的风险偏好程度和预算水平对设施协同布局有重要影响,需对二者进行综合权衡.     

混沌粒子群鸡群融合优化的RSSI质心定位算法

针对传统接收信号强度指示（RSSI）定位算法定位精度低及粒子群优化（PSO）算法容易陷入早熟和局部最优解的问题,提出一种RSSI质心定位算法。通过RSSI测距技术计算各传感节点之间的距离,选择离未定位节点最近的3个锚节点和已定位节点,建立以未知节点坐标为参数的数学模型。在PSO算法的基础上运用混沌优化思想避免搜索过程陷入局部极小,并利用鸡群算法进行优化从而得到质量更好的解。实验结果表明,与原始质心定位算法、加权RSSI质心定位算法和PSO-RSSI质心定位算法相比,该算法具有较快的收敛速度和较高的定位精度。     

遗传算法支持下的孔组复合位置度误差评判

在孔组复合位置度误差评判过程中,用传统的数值优化方法一般难以找到符合最小条件判别准则的几何图框理想位置,尤其当位置度公差遵守最大实体要求且被测要素尚未达到最小实体尺寸时,被判为不合格的零件还有被修复的可能。针对上述问题,可利用自适应遗传算法全局搜索几何图框最佳位置,并在搜索过程中同时考虑遵守最大实体要求时的误差补偿因素的影响。此外,针对不合格零件进行的可修复性判别和修正量的计算,进一步降低了零件误废率。     

A technique for locating function roots and for satisfying equality constraints in optimization

A new technique for locating simultaneous roots of a set of functions is described. The technique is based on the property of the Kreisselmeier-Steinhauser function which descends to a minimum at each root location. It is shown that the ensuing algorithm may be merged into any nonlinear programming method for solving optimization problems with equality constraints.     

DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization

Incorporation of distributed generation (DG) in distribution network may reduce the network loss if DG of appropriate size is placed at proper strategic location. The current article presents determination of optimal size and location of DG in radial distribution network (RDN) for the reduction of network loss considering deterministic load demand and DG generation using symbiotic organisms search (SOS) algorithm. SOS algorithm is a meta-heuristic technique, inspired by the symbiotic relationship between different biological species. In this paper, optimal size and location of DG are obtained for two different RDNs (such as, 33-bus and 69-bus distribution networks). The obtained results, using the proposed SOS, are compared to the results offered by some other optimization algorithms like particle swarm optimization, teaching-learning based optimization, cuckoo search, artificial bee colony, gravitational search algorithm and stochastic fractal search. The comparison is done based on minimum loss of the distribution network as well as based on the convergence mobility of the fitness function offered by each of the comparative algorithms for both the networks under consideration. It is established that the proposed SOS algorithm offers better result as compared to other optimization algorithms under consideration. The results are also compared to the existing solution available in the literature.     

Joint location and dispatching decisions for Emergency Medical Services

The main purpose of Emergency Medical Service systems is to save lives by providing quick response to emergencies. The performance of these systems is affected by the location of the ambulances and their allocation to the customers. Previous literature has suggested that simultaneously making location and dispatching decisions could potentially improve some performance measures, such as response times. We developed a mathematical formulation that combines an integer programming model representing location and dispatching decisions, with a hypercube model representing the queuing elements and congestion phenomena. Dispatching decisions are modeled as a fixed priority list for each customer. Due to the model’s complexity, we developed an optimization framework based on Genetic Algorithms. Our results show that minimization of response time and maximization of coverage can be achieved by the commonly used closest dispatching rule. In addition, solutions with minimum response time also yield good values of expected coverage. The optimization framework was able to consistently obtain the best solutions (compared to enumeration procedures), making it suitable to attempt the optimization of alternative optimization criteria. We illustrate the potential benefit of the joint approach by using a fairness performance indicator. We conclude that the joint approach can give insights of the implicit trade-offs between several conflicting optimization criteria.     

回溯搜索优化改进矩阵填充的高效位置指纹库构建

针对基于信号强度指示（RSSI）的位置指纹定位过程中用于其离线位置指纹库构建的全采法采集工作量较大、位置指纹库构建效率较低、而插值法通常精度有限等问题,提出一种基于回溯搜索优化算法改进奇异值阈值（SVT）矩阵填充（MC）算法的离线位置指纹库高效构建方法。首先,利用定位区域内采集到的部分参考点的位置指纹数据建立低秩矩阵填充模型;然后通过基于奇异值阈值的低秩矩阵填充算法来求解该模型,进而快速准确重构出完整的位置指纹数据库;同时,针对传统矩阵填充算法最优解模糊及平滑性欠佳的问题,引入回溯搜索优化算法,以核范数最小建立适应度函数,对矩阵填充算法的寻优过程进行改进,进一步提高了求解精度。实验结果表明,利用所提方法构建的位置指纹库与实际采集的位置指纹库之间的平均误差仅为2.7054 dB,平均定位误差仅相差0.0863 m,但却节约了近50%的离线采集工作量。上述结果表明所提算法用于离线位置指纹库构建可以在保证精度的基础上,有效降低离线采集阶段的工作量,显著提高位置指纹库构建效率,在一定程度上提高位置指纹定位方法的实用性。     

Piezoresistive Microphone Design Pareto Optimization: Tradeoff Between Sensitivity and Noise Floor

This paper addresses tradeoffs between pressure sensitivity and electronic noise floor in optimizing the performance of a piezoresistive microphone. A design optimization problem is formulated to find the optimum dimensions of the diaphragm, the piezoresistor geometry and location for two objective functions: maximum pressure sensitivity and minimum electronic noise floor. The Pareto curve of optimum designs for both objectives is generated. The minimum detectable pressure (MDP) was also employed as an objective function, generating a point on the Pareto curve that may be the best compromise between the original two objectives. The results indicated that this application the critical constraints are the linearity and power consumption. The minimized MDP design was less sensitive to uncertainty in design variables. The optimization methodology presented in this paper as well as some of the conclusions are applicable to other types of piezoresistive sensors     

Topology optimization of beam cross-section considering warping deformation

The beam cross-section optimization problems have been very important as beams are widely used as efficient load-carrying structural components. Most of the earlier investigations focus on the dimension and shape optimization or on the topology optimization along the axial direction. An important problem in beam section design is to find the location and direction of stiffeners, for the introduction of a stiffener in a closed beam section may result in a topologically different configuration from the original; the existing section shape optimization theory cannot be used. The purpose of this paper is to formulate a section topology optimization technique based on an anisotropic beam theory considering warping of sections and coupling among deformations. The formulation and corresponding solving method for the topology optimization of beam cross-sections are proposed. In formulating the topology optimization problem, the minimum averaged compliance of the beam is taken as objective, and the material density of every element is used as design variable. The schemes to determine the rigidity matrix of the cross-sections and the sensitivity analysis are presented. Several kinds of topologies of the cross-section under different load conditions are given, and the effect of load condition on the optimum topology is analyzed.