全自动酶免工作站计算机控制系统设计

介绍了酶免工作站软件控制部分的分析和设计。按软件工程的设计思想,归纳出系统的对象,给出系统实现单文档多视和控制类的详细设计。并将一种改进的自适应遗传算法应用于系统执行效率的优化。此开发系统已投入实际试验,从实际使用的效果看,试验执行效率提高了69.9%,血样检测的准确率也达到了99.6%以上,因此,每台酶免仪每天可以多检测120个左右的血样,如果按照现在医院血液检测的指导价,每台酶免仪每年可为医院多创造150万人民币左右的利润。     

Matlab遗传算法工具箱在非线性优化中的应用

投影寻踪是一种降维处理技术,通过它可以将多维分析问题通过投影方向转化为一维问题分析。应用该法的关键在于寻求最佳投影方向,这可以转化为一个复杂的非线性优化问题来进行解决。选取某地区大气环境质量评价的投影寻踪评价模型,编制相关的目标函数和约束函数,应用基于MATLAB的遗传算法和直接搜索工具箱进行优化求解。结果表明：该工具箱在求解此类非线性优化问题上的有效性和方便性,从而为各领域应用投影寻踪模型提供了强有力的优化工具。     

一种改进的应用于噪声数据中的KNN算法

基于实例的KNN算法不可避免地要依赖于数据的质量,但原始数据含有噪声,因而KNN算法的结果势必会因为数据中的噪声而受到严重的影响。事实上,大多噪声都服从一定的模型,而且模型一般是已知的。充分利用数据中的噪声模型,以减小噪声对KNN算法结果的影响。通过实验结果表明该方法是有效的。     

电-热-氢综合能源系统鲁棒区间优化调度

该文采用鲁棒区间法挖掘电-热综合能源系统协调运行的潜力,以缓解风电功率的不确定性对电力系统的运行稳定性的影响,并构建风力发电与氢储能系统相结合的风-氢混合系统,考虑氢储能系统的热平衡需求,以充分发挥氢储能系统的储能效率,平抑风电的波动性。首先,介绍了考虑氢储能系统接入的电-热综合能源系统结构,并构建其数学模型;然后,以区间形式考虑风电的不确定性,构建含风电的鲁棒区间优化调度模型,使系统在所有风电出力允许区间内,均满足允许约束条件;再次,建立一种含风-氢混合系统的电-热综合能源系统鲁棒区间优化调度模型,采用对偶理论将原模型转化为单层模型进行求解,最坏情况下的风电不平衡功率由可调机组根据时变参与因子进行调整;最后,以PJM-5节点电力系统与6节点热力系统和辽宁省北部太和综合能源系统为例对所提模型进行分析,验证了模型的有效性。     

含多电解槽的新能源制氢能量管理优化

新能源制氢系统是提升风能、太阳能等新能源发电消纳的有效途径。目前国内外关于电解槽能量管理的相关研究以单电解槽为主。单电解槽能量管理未考虑电解槽非线性的工作特性,难以兼顾多个电解槽制氢效率,影响系统经济性。论文针对含有多电解槽新能源制氢系统的能量管理问题进行了研究。以新能源消纳率、经济收益、制氢率为目标,考虑单个电解槽运行特性及生产约束等条件,建立包含风电、光伏、蓄电池、电解槽多个模块的能量管理优化模型,并采用SPEA2算法求解多目标优化问题。仿真研究表明,所提能量管理策略能够实现新能源发电的100%消纳,单位制氢效益可提升5.15%。因此,多电解槽制氢系统进行有效的能量管理有助于提高制氢效率,有效克服单电解槽运行及能量管理的不足。     

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating the pathways using multiple criteria and analytics. We present a Group Adaptive Hybrid Routing Algorithm (GAHRA) for gathering portability, which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations. Based on this aspect, the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach, with the objectives of enhancing the output of MANET routing computation in each hub. Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.     

An Automated Brain Image Analysis System for Brain Cancer using Shearlets

In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of features that includes Gray Level Co-occurrence Matrix (GLCM) based features, Histograms of Positive Shearlet Coefficients (HPSC), and Histograms of Negative Shearlet Coefficients (HNSC) are estimated. The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers; k-Nearest Neighbor (kNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. The output of individual trained classifiers for a testing input is hybridized to take a final decision. The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data (REMBRANDT) database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.     

A modified teaching–learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

In this paper, a modified teaching–learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. Based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly.     

高能超声分散纳米晶须的数值和物理模拟

为研究高能超声处理制备纳米复合材料过程中纳米增强相在熔体中的分散过程,采用甘油为介质分别进行了数值模拟以及物理模拟。数值模拟结果表明,当超声作用于甘油中时,甘油中会形成中心-底面-壁面-中心的环形流动,变幅杆探头端面边缘附近甘油流体存在最大的流动速度,且随着超声功率的增大,流体运动速度增大。物理模拟实验结果显示,高能超声作用下甘油的实际运动行为与数值模拟结果相符合,存在环形流动;此外,高能超声作用下甘油中存在明显的空化效应;纳米晶须在超声作用下于甘油中分散良好,且随着超声功率的增大,达到充分分散所需时间变短。     

Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm

This paper proposes an indirect method for the identification of moving vehicular parameters using the dynamic responses of the vehicle. The moving vehicle is modelled as 2-DOF system with 5 parameters and 4-DOF system with 12 parameters, respectively. Finite element method is used to establish the equation of the coupled bridge–vehicle system. The dynamic responses of the system are calculated by Newmark direct integration method. The parameter identification problem is transformed into an optimization problem by minimizing errors between the calculated dynamic responses of the moving vehicle and those of the simulated measured responses. Glowworm swarm optimization algorithm (GSO) is used to solve the objective function of the optimization problem. A local search method is introduced into the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm. Several test cases are carried out to verify the efficiency of the proposed method and the results show that the vehicular parameters can be identified precisely with the present method and it is not sensitive to artificial measurement noise.