MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times,the images and videos have emerged as one of the most important information source depicting the real time scenarios.Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane.The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition.One of the application fields pertains to detection of diseases occurring in the plants,which are destroying the widespread fields.Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests.This is a tedious and time consuming process and does not suffice the accuracy levels.This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading.The digital images captured from the field’s forms the dataset which trains the machine learning models to predict the nature of the disease.The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images,appropriate segmentation methodology,feature vector development and the choice of machine learning algorithm.To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages.Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection.The training vector thus developed is capable of presenting the relationship between the feature values and the target class.In this article,a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed.The overall improvement in terms of accuracy is measured and depicted.

Reliability-based multidisciplinary design optimization using incremental shifting vector strategy and its application in electronic product design

Use of multidisciplinary analysis in reliabilitybased design optimization(RBDO) results in the emergence of the important method of reliability-based multidisciplinary design optimization(RBMDO). To enhance the efficiency and convergence of the overall solution process,a decoupling algorithm for RBMDO is proposed herein.Firstly, to decouple the multidisciplinary analysis using the individual disciplinary feasible(IDF) approach, the RBMDO is converted into a conventional form of RBDO. Secondly,the incremental shifting vector(ISV) strategy is adopted to decouple the nested optimization of RBDO into a sequential iteration process composed of design optimization and reliability analysis, thereby improving the efficiency significantly. Finally, the proposed RBMDO method is applied to the design of two actual electronic products: an aerial camera and a car pad. For these two applications, two RBMDO models are created, each containing several finite element models(FEMs) and relatively strong coupling between the involved disciplines. The computational results demonstrate the effectiveness of the proposed method.

基于高维混合模型的离心泵叶轮子午面优化设计

为提高离心泵在设计工况下的运行效率和扬程,提出一种基于高维混合模型的离心泵叶轮优化设计方法.选取一台比转数为157的单级离心泵作为研究对象,通过CFturbo软件对优化变量进行参数化,然后结合数值模拟获得高维混合模型的训练集.在此基础上采用获取的训练集通过MATLAB机器学习得出效率、扬程与优化参数之间关于支持向量回归的高维模型,并采用遗传算法寻优.在设计工况下,所拟合的高维混合模型预测的效率和扬程值比原模型分别高1.5%和3.2 m,数值模拟验证优化方案的效率和扬程分别比原模型高0.9%和2.1 m.算例研究表明,将高维混合模型应用于离心泵叶轮的优化设计中可以实现快速寻优并提高离心泵水力性能.

A C-Based Variable Length and Vector Pipeline Architecture Design Methodology and Its Application

The size and performance of a System LSI depend heavily on the architecture which is chosen. As a result, the architecture design phase is one of the most important steps in the System LSI development process and is critical to the commercial success of a device. In this paper, we propose a C-based variable length and vector pipeline (VVP) architecture design methodology and apply it to the design of the output probability computation circuit for a speech recognition system. VVP processing accelerated by loop optimization, memory access methods, and application-specific cir- cuit design was implemented to calculate the Hidden Markov Model (HMM) output probability at high speed and its performance is evaluated. It is shown that designers can explore a wide range of design choices and generate complex circuits in a short time by using a C-based pipeline architecture design method.

Monotonic Vector Space Model(II): Application in System Capability Indicator Analysis and Design

The application of some MVS operations (each dimension of which corresponds to a capability indicator) in system capability analysis and design, including capability indicator requirement analysis, effectiveness analysis, sensibility analysis, fuzzy analysis, stability analysis, capability optimization design, etc., is discussed in the second paper of this series of papers. And some MVS-based models and algorithms for capability analysis and design are put forward. Finally, an example of capability analysis and optimization design is given for explaining the usages of related models and algorithms.

永磁同步直线电机的矢量控制

由于旋转电机加滚珠丝杠的传统机械结构加入了中间传动环节,无法进一步提升使用性能,研究了永磁同步直线电动机矢量控制算法.针对永磁同步直线电动机开展矢量控制算法分析以及验证工作,在矢量控制系统框架下进行了硬件搭建与软件设计,搭建了永磁同步直线电机矢量控制验证平台,测试核心算法,经过试验,设计的软/硬件能够对永磁同步直线电机进行有效控制,有较好的鲁棒性.

结合词向量和自注意力机制的设计素材推荐系统

当设计师使用文本在设计库中检索素材时,基于词匹配的搜索无法有效地识别文本中的设计意图并推荐合适的素材.对此,提出一个结合词向量和自注意力机制的设计素材推荐系统.首先从预训练词向量模型中获取文本的词向量表征;然后利用自注意力机制学习文本中不同词的权重以及文本与设计语义间的映射关系;最后基于模型输出与向量相似,检索并重排序设计素材形成推荐.所提系统还能检索带有不同语义标签的素材数据,通过计算其语义与设计语义的词相似作为权重,转换其数据维度与模型输出维度一致.基于1300个电商设计案例和200种字体进行实验的结果表明,用户评估推荐结果的有效率分别为55%和57.3%,处于同类工作中上水平;验证了该系统的可行性及有效性.

新能源汽车的驱动电机系统研究和设计

通过改变新能源汽车的能源消耗方式,能提高节能减排工作效果,促进新能源开发利用工作顺利进行,保证社会经济实现可持续发展。科学技术的发展决定着新能源汽车的研发进程,驱动电机系统作为新能源汽车的关键点,涉及新能源汽车研发的核心技术,相关人员要注重新能源汽车的驱动电机系统研究和设计,提高设计的合理性。

基于NEON的VSIPL加速技术研究

针对航空及通信领域嵌入式信息处理软件的可移植性和实时性需求,提出了ARM平台下基于NEON的矢量信号图像处理函数库(VSIPL)中间件算法加速优化的方法。VSIPL中间件能够解决信号处理软件在不同平台的复用性问题,通过对VSIPL的数据特性进行分析,然后将NEON技术应用于VSIPL加速设计中,实现数据的并行处理以提高运算速率。最后,对VSIPL中的典型向量算法优化前后进行性能对比测试,并分析了不同因素对加速效果的影响,测试结果验证了加速方法的可行性。提出的基于NEON的VSIPL加速设计方法能有效提高信号处理软件的性能,具有一定的指导意义和工程应用价值。

高精度矢量数据的公路快速逆向建模方法

数字交通的快速发展对道路基础设施三维数字化提出了新的要求。针对人工建立公路三维模型费时费力等问题,研究了一种基于高精度矢量数据的快速逆向建模方法。以三维激光扫描点云为基础数据源,首先提取高速公路中各目标高精度矢量数据,并存储为空间矢量地图。然后针对道路场景各目标模型结构,提出自适应节点重建、参照线放样重建及实例化重建等多种关键建模方法;最后基于MAXscript三维建模语言进行开发,建立精细化的高速公路三维模型。以某段高速公路实际数据为例进行实验,验证该方法的可行性。

基于计算几何的多波束测线模型

多波束具有测点密度大、精度高、可视化等优点,被广泛应用于海洋测量。根据数学建模赛题给出的案例,在不同的海域条件下,借助计算几何和三角函数定理,建立并推导多波束测深过程中表征覆盖宽度和相邻条带重叠率关系的数学模型。同时,基于向量法提出了一种多波束测线设计优化方法,详细给出了MATLAB求解结果。模型的合理性通过模拟仿真进行检验说明。

矢量喷管作动器热设计技术研究

随着航空发动机性能需求的不断提升,对发动机作动产品的环境温度要求也不断提高,文章以航空发动机矢量喷管典型作动器产品为研究对象,开展了矢量喷管作动器热设计技术研究。根据作动器所处的外部热环境开展相应的主动冷却散热设计,并对矢量喷管作动器进行热场仿真分析;在研究了冷却结构的散热效果及冷却流量的大小对作动器温度分布影响规律的同时开展了作动器的热环境试验研究,对比分析了仿真和试验研究的结果,验证了主动冷却结构的合理性,为类似作动器冷却结构和系统设计提供一定的参考和指导。

3D矢量空间和最优最劣法在绿色产品设计决策方法中的应用

为了解决目前绿色产品设计方案中产品基础属性考虑欠缺,以及缺乏可视化比较方法的问题,提出一种基于3D矢量空间和最优最劣法(Best worst method,BWM)的绿色产品设计多属性决策方法。通过功能、外观和可持续3个维度为绿色产品设计的基础属性构建3D矢量空间,并对每个维度扩充其包含的下级属性;利用间隔方法优化的BWM获得三个维度及各维度下级属性的权重分配;通过3D矢量空间求得各备选方案的总体评价得分,实现客观科学的绿色产品设计多属性决策。以电动汽车的绿色产品设计方案为例,验证了所提方法的可行性和有效性。

APPLICATIONS OF THE PACK & UNPACK TECHNIQUE IN VECTOR COMPUTATION

This article describes the pack & unpack performance of the vector using YH-1 supercomputer as its platform, with its emphasis on the pack & unpack technique's unique application to vector computation, in the light of vector computer's characteristics and some practical cases. The practical numerical experiment proves that the pack & unpack techniques is a key method to the depth parallel development and vectorization research of large scale scientific computation project, and which is a highly effective method to vectorizing application programs.

Codebook design using improved particle swarm optimization based on selection probability of artificial bee colony algorithm

In the paper, a new selection probability inspired by artificial bee colony algorithm is introduced into standard particle swarm optimization by improving the global extremum updating condition to enhance the capability of its overall situation search. The experiment result shows that the new scheme is more valuable and effective than other schemes in the convergence of codebook design and the performance of codebook, and it can avoid the premature phenomenon of the particles.

OPTIMUM DESIGN BASED ON RELIABILITY IN STOCHASTIC STRUCTURE SYSTEMS

The optimum design method based on the reliability is presented to the stochastic structure systems （i. e., the sectional area, length, elastic module and strength of the structural member are random variables ） under the random loads. The sensitivity expression of system reliability index and the safety margins were presented in the stochastic structure systems. The optimum vector method was given. First, the expressions of the reliability index of the safety margins with the improved first-order second-moment and the stochastic finite element method were deduced, and then the expressions of the systemic failure probability by probabilistic network evaluation technique（PNET） method were obtained. After derivation calculus ,the expressions of the sensitivity analysis for the system reliability were obtained. Moreover, the optimum design with the optimum vector algorithm was undertaken. In the optimum iterative procedure, the gradient step and the optimum vector step were adopted to calculate. At the last, a numerical example was provided to illustrate that the method is efficient in the calculation, stably converges and fits the application in engineering.

基于大数据分析的配电网低压故障定位研究

为了维护配电网正常运行,以提升配电网低压故障定位能力为目标,提出基于大数据分析的配电网低压故障定位方法。通过采集配电网低压故障定位数据,利用大数据分析算法中的支持向量机算法进行分类,从而实现配电网低压故障定位。使用改进粒子群算法改进支持向量机算法,优化配电网低压故障定位结果,并进行配电网低压故障定位的仿真实验。结果表明,所提方法解决了配电网低压故障定位方法存在的弊端,获得了理想的配电网低压故障定位效果,能够满足配电网低压故障定位实际要求。

基于灰色支持向量回归的起伏管线临界携液流速预测模型

延安气田起伏的地理形貌特征,致使其内部采气管线在低点及上倾段容易产生积液问题,制约气田正常生产,开展起伏管线临界携液流速的预测对保障采气管线流动安全和指导地面采气管线布线具有重要价值。为此,基于扩展双流体分相模型和PR气体物性方程,采用最小压力梯度法结合均匀设计构建了临界携液流速样本数据集,同时采用灰色关联计算了样本标签权重,并将其引入支持向量回归算法,构建了临界携液流速的回归预测模型,随后利用网格寻优算法,通过采用K-CV(交叉验证)对模型中超参数进行了优化设计,最后采用延安气田现场运行数据进行了模型准确性验证。研究结果表明:管径大小对临界携液流速影响最大,其次为上坡倾角、含水率和运行压力,其他因素的影响差距相对较小,网格分段寻优对比遗传、粒子群算法在模型超参数优化上具有较强稳定性。该预测模型对延安气田内部采气管线的临界携液流速预测具有较强准确性,可对其积液预测和防治提供理论支撑。

Predicting configuration performance of modular product family using principal component analysis and support vector machine

A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.

ACM/ICPC竞赛中动态数组vector容器的教学探讨

动态数组vector容器在日常程序设计课程中没有涉及,而在ACM/ICPC竞赛中却经常被用到,因此有必要予以介绍。文章从创建一个vector对象,在vector对象中插入元素,在vector对象中删除元素,修改vector对象中元素的值,vector容器的常用成员函数和算法等方面详细地介绍了vector容器的使用。学生通过学习,能对vector容器有一个全面的认识,为其参加ACM/ICPC竞赛提供帮助。