Application of Projection Pursuit Evaluation Model Based on Real-Coded Accelerating Genetic Algorithm in Evaluating Wetland Soil Quality Variations in the Sanjiang Plain, China

A new technique of dimension reduction named projection pursuit is applied to model and evaluatewetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the im-proved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimized and multi-dimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluationof wetland soil quality variations are realized by pursuing optimum projection direction and projection func-tion value. Therefore, by adopting this new method, any possible human interference can be avoided andsound results can be achieved in researching quality changes and classification of wetland soils.

Inverse procedure for determining model parameter of soils using real-coded genetic algorithm

The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.

Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology

In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.

Real-Code Genetic Algorithm for Ground State Energies of Hydrogenic Donors in GaAs-（Ga,Al）As Quantum Dots

We present a global optimization method, called the real-code genetic algorithm (RGA), to the ground state energies. The proposed method does not require partial derivatives with respect to each variational parameter or solving an eigenequation, so the present method overcomes the major difficulties of the variational method. RGAs also do not require coding and encoding procedures, so the computation time and complexity are reduced. The ground state energies of hydrogenic donors in GaAs-(Ga,Al)As quantum dots have been calculated for a range of the radius of the quantum dot radii of practical interest. They are compared with those obtained by the variational method. The results obtained demonstrate the proposed method is simple, accurate, and easy implement.

Adaptive Real-Coded Genetic Algorithm for Identifying Motor Systems

In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.

Real-coded genetic algorithm for optimal vibration controlof flexible structure

Presents the study on the optimum location of actuators/sensors for active vibration control in aerospace flexible structures with the performance function first built by maximization of dissipation energy due to control action and a real coded genetic algorithm then proposed to produce a global optimum solution, and proves the feasibility and advantages of this algorithm with the example of a standard test function and a two collocated actuators/sensors cantilever, and comparing the results with those given in the literatures.

A Real-coded Genetic Algorithm Applied to Optimum Design of a Low Solidity Vaned Diffuser for Diffuser Pinup

A numerical procedure for hydrodynamic redesign of the conventional vaned diffuser into the low solidity vaned diffuser by means of a real-coded genetic algorithm with Boltzmann, Tournament and Roulette Wheel selection is presented. In the first part, an investigation on the relative efficiency of the different real-coded genetic algorithm is carried out on a typical mathematical test function. The real-coded genetic algorithm with Boltzmann selection shows the best optimization performance compared to the Tournament and Roulette Wheel selection. In the second part, an approach to redesign the vaned diffuser profile is introduced. Goal of the optimum design is to search the highest static pressure recovery coefficient and low solidity vaned diffuser. The result of the low solidity vaned diffuser optimum design confirms that the efficiency and optimization performance of the real-coded Boltzmann selection genetic algorithm outperforms the other selection methods. A comparison between the designed low solidity vaned diffuser and original vaned diffuser shows that the diffuser pump with the redesigned low solidity vaned diffuser has the higher static pressure recovery and improved total hydrodynamic performance. In addition, the smaller outlet diameter of designed vaned diffuser tends to a more compact size of diffuser pump compared to the original diffuser pump. The obtained results also demonstrate the real-coded Boltzmann selection genetic algorithm is a promising optimization algorithm for centrifugal pumps design.

基于改进实数编码遗传算法的神经网络超参数优化

针对神经网络超参数优化效果差、容易陷入次优解和优化效率低的问题,提出一种基于改进实数编码遗传算法(IRCGA)的深度神经网络超参数优化算法——IRCGA-DNN(IRCGA for Deep Neural Network)。首先,采用实数编码方式表示超参数的取值,使超参数的搜索空间更灵活;然后,引入分层比例选择算子增加解集多样性;最后,分别设计了改进的单点交叉和变异算子,以更全面地探索超参数空间,提高优化算法的效率和质量。基于两个仿真数据集,验证IRCGA-DNN的毁伤效果预测性能和收敛效率。实验结果表明,在两个数据集上,与GA-DNN(Genetic Algorithm for Deep Neural Network)相比,所提算法的收敛迭代次数分别减少了8.7%和13.6%,均方误差(MSE)相差不大;与IGA-DNN(Improved GA-DNN)相比,IRCGA-DNN的收敛迭代次数分别减少了22.2%和13.6%。实验结果表明,所提算法收敛速度和预测性能均更优,能有效处理神经网络超参数优化问题。

基于改进实数遗传算法的桑叶采摘机结构参数优化

针对遗传算法在求解桑叶采摘机结构优化问题时容易陷入局部最优和求解精度低等问题,提出一种改进实数遗传算法。首先是给出一种基于序的组合适应度函数的轮盘赌选择算子,该算子在轮盘赌的基础上,通过一个自适应变化的参数在两种适应度函数中选择一个,再去计算适应度值;然后设计一种基于方向的改进启发式交叉算子,该算子既保留两个父代个体中较优个体对子代个体的影响,又增加种群中最优个体对子代个体的影响,提高交叉产生有潜力子代的可能性。接着将改进算法应用于摇杆式桑叶采摘机的优化参数设计中,通过与其他算法作仿真对比试验验证算法的优越性,获得采摘机最优参数组合:行走结构速度为24 mm/s、拨动结构角速度为1.2 rad/s、采摘结构速度为440 mm/s,并由运行结果可知整机性能与优化前相比提高13%。最后用优化得到的参数组合进行实地试验,结果显示桑叶采摘机性能提升10.9%,误差较小为2.1%。可见,所提改进实数遗传算法是优化采摘机参数的一种有效算法。

“亮码办”在不动产登记平台中的设计与实现

身份证明是不动产登记受理的必要材料。为深化落实广州市优化营商环境5.0的要求,解决企业或个人未携带实体身份证明造成的困扰和不便等问题,广州市不动产登记率先提出应用电子身份证“亮码办”。平台采用人脸识别和电子身份二维码二次加解密技术,实现了申请人持手机出具身份证明二维码以代替实体身份证明并办理不动产登记业务,达到了政务服务减材料的成效,为企业和群众带来了良好的体验感,同时也为其他地区不动产统一登记平台建设以及推广电子身份证应用提供借鉴。

利用FME构建不动产地理实体数据

针对目前三维不动产管理中缺乏有效空间数据支撑的问题,提出了一种基于FME与“多测合一”测绘成果的不动产地理实体数据自动构建方法。采用FME数据处理技术对不动产测绘、楼盘表和多源地理信息数据进行数据融合和转换,创建具有实体化、三维化、语义化特征的分层分户地理实体空间数据。实验结果表明,该方法执行效率高、构建的实体数据产品具有“一码多态”特征和较高的实用价值,可为不动产三维确权登记等管理工作提供技术和数据支持。

登记对抗主义下相对性物权说之批判

登记对抗主义物权变动规则由设立要件与对抗要件构成,其适用难点在于如何解释对抗要件所产生的登记对抗效力。相对性物权说在承认未登记物权仍为物权属性可对抗一般债权的前提下,主张未登记物权不能对抗特殊债权乃对抗要件发挥其登记对抗效力的必然结果,进而认为,在登记对抗主义下,依据合同设立的未登记物权具有相对性。然而,该学说偏离了物权界定规则与登记对抗规则的位阶关系,对域外法的借鉴未能作出契合本土的诠释,导致设立要件与对抗要件的功能错位。实质上,相对性物权说是以物权的相对性替换债权物权化。特殊债权优先受偿与登记对抗效力不存在必然联系,因而不应纳入登记对抗效力的调整范围。

论高职院校房产管理系统设计实现

以包头职业技术学院为例,重点分析了房产系统的业务需求,设计系统总体功能模块及数据库表单,明确数据表逻辑关系,提出采用先进的前后端分离的体系架构模式,给出了系统主要功能的现实代码,总结了系统设计的特色。本文将为高职类院校房产管理实现信息化建设提供参考。

基于模糊认知图的智能汽车驾驶权交互决策建模方法

针对人机共驾中的由自动驾驶接管到驾驶人接管切换的动态问题,提出一种基于模糊认知图(fuzzy cognitive map,FCM)的驾驶权交互决策模型。首先分别分析影响驾驶风险的车辆和人的动态风险因素,并选取车距、加速度、方向盘转向熵、驾驶员眼动信息、心脑电波信息等相关影响指标构建基于FCM的车辆动态风险模型与人的动态风险模型,再结合环境因素,车辆状况以及驾驶员水平构建基于FCM的驾驶员交互决策模型。为降低对专家知识的依赖,运用基于实数编码的遗传算法(real coded genetic algorithm,RCGA)对模型进行优化,进一步提高模型决策的准确性。仿真结果表明,经RCGA优化后模型适应度值达到0.979,可见该模型能够较好地应对动态、复杂的驾驶环境。

血清lncRNA T342620联合AFP对肝癌的诊断价值

目的探究肝癌患者血清长链非编码RNA(lncRNA)T342620的表达水平,及其单独或联合甲胎蛋白(AFP)诊断肝癌的临床应用价值。方法采用病例对照研究,收集2021年4月至2023年5月在中国人民解放军南部战区总医院治疗的69例原发性肝癌患者(肝癌组)、32例乙型肝炎患者(乙肝组)、20例肝硬化患者(肝硬化组)、30例原发性肝癌经导管肝动脉化疗栓塞术(TACE)术后患者(肝癌术后组)和同期进行体检的50例健康者(健康体检组)的血清,提取血清总RNA,采用实时荧光定量PCR技术,检测血清中lncRNA T342620的相对表达量;结合患者临床诊疗资料,分析其表达水平与病理特征和血清学相关指标的相关性;运用受试者工作特征(ROC)曲线分析lncRNA T342620单独及联合AFP对肝癌诊断的特异度和灵敏度。根据ROC曲线下面积(AUC)判断诊断效能,评估其在肝癌诊断中的应用价值。各组间比较采用χ^(2)检验,相关性分析采用Spearman法。结果lncRNA T342620在肝癌组和肝癌术后组血清表达水平较健康体检组、乙肝组、肝硬化组高,差异均具有统计学意义(P<0.001);临床病理和血清学相关指标分析显示:肿瘤越大,血清lncRNAT342620表达水平越高,肝癌组血清lncRNA T342620表达水平与白蛋白(ALB)和白球比(A/G)呈负相关(P<0.05),与α-L-岩藻糖苷酶(AFU)和HBV-DNA呈正相关(P<0.05),肝癌术后组患者血清lncRNA T342620表达水平与总胆汁酸(TBA)呈正相关(P<0.05);ROC曲线分析显示:血清lncRNA T342620用于区别肝癌患者与健康者、乙肝和肝硬化患者时,其灵敏度和特异度分别为55.1%和94.1%,具有较好的诊断价值;和AFP联合检测时,灵敏度和特异度分别为91.3%和91.2%,其灵敏度高于各单项指标诊断的灵敏度,且联合检测诊断效能最高,AUC为0.954,与AFP和lncRNA-T342620单独检测的AUC(0.906、0.758)比较,差异有统计学意义(P<0.05)。结论血清lncRNA T342620有可能成为肝癌辅助诊断的新型血清分子标志物。

SAR实时快速处理抗主瓣欺骗式干扰方法

主瓣欺骗干扰与真实目标场景具有相同的角度,二者在多个表征域具有高度相似性,严重降低了合成孔径雷达系统对真实目标的检测和判断能力。针对该问题,为保证有效抑制主瓣欺骗干扰,文中提出了一种实时快速处理抗主瓣欺骗干扰算法。首先,提出了一种基于方位相位编码的回波恢复框架,并且该框架能够对原始回波数据进行分组处理;其次,提出了一种模块化处理的快速算法来加快所提方法的处理效率;最后,通过点目标和分布式目标仿真实验验证了所提方法的有效性和可行性。通过与传统主瓣欺骗干扰方法进行对比,文中所提方法不仅能够降低计算复杂度,还能减少对系统资源的占用。

结合遗传算法的水库汛限水位动态控制模型研究

针对水库汛限水位动态控制问题,提出一种新的自适应交叉变异遗传算法,解决了水库防洪效益与发电效益之间的矛盾问题,实现了水库水位的调整,缓解了防洪效益与发电效益的冲突。遗传算法使原始汛期水位提升了0.7m,蓄水量提升了2947m^(3),同时增加了发电量233.33×10^(4) kW·h。基于峡山水库实例试验的研究结果表明,结合遗传算法的水库汛限水位动态控制模型,不仅可对水位进行实时性调节,还能提升水库效益,研究结果可应用于各水库防洪系统。

不动产单元代码“一码关联”新建项目全生命周期管理与应用研究

针对用地建设项目各环节业务不协同、全周期信息关联弱、审批智能化支撑不足等问题,基于济南市实践做法,本文系统总结了利用“不动产单元代码”作为关联互认的关键字段,“一码关联”工程建设项目用地审批、土地供应、规划建设、竣工验收、确权登记、产权交易等全生命周期管理的实施路径,阐述其工作机制建设及实现方式,并结合工作实际,提出了深化不动产代码社会应用的对策建议。

基于数据融合的高校化学品实时管理体系构建

高校化学品使用具有种类多、单品存量小、专业覆盖面广、存储区域分散等特征,实时记账、科学分类存储难以落实,给高校实验室带来重大安全隐患。某高校将数据融合技术引入化学品管理体系,实现从购置源头到使用过程、处置末端的全数据管理,完成了“一体两翼多支点”式化学品管理软、硬件平台建设,包括化学品申购云商城及全过程管理云平台、院级试剂库及校级危废暂存库、智能化学品存储柜。在抽取数据特征、实现全数据融合的基础上,以“二维码”为核心,建成了“扫码开门、扫码存储、及时称量、自动记账”的化学品实时管理体系。

智能仓储多机器人动态任务分配方法

针对智能仓储系统中多机器人任务分配方法进行研究,根据智能仓储中物流任务数量庞大且实时新增的动态特点,创新地提出了一种总体分配并根据当前工况实时插入的动态任务分配方法,将一定时间阈值内的物流任务集合利用综合时间代价、路径代价、空载代价和任务均衡度代价的自适应多层编码遗传算法进行任务整体规划,为智能仓储系统中每个机器人分配需执行的物流任务序列及执行顺序。任务执行过程中,各机器人通过对当前自身剩余任务量成本及插入实时任务后路径成本的判断,在不影响当前任务完成总时间的情况下,对下一时间阈值内产生的实时物流任务是否插入当前执行任务序列展开竞争,实现任务的实时插入,未被实时插入的物流任务则等待下一次总体分配。实验结果表明,该方法能在缩短系统运行总时间和总路程的基础上,极大的降低机器人无任务空载等待的时间,提高系统运行效率。