Application Analysis of Nursing Students'Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching effects.With the help of IBM SPSS Modeler data mining software,this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine,and to explore the correlation between professional basic courses and professional core courses.Lastly,according to the detailed analysis of the mining results,valuable curriculum information will be found from the actual teaching data.

Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM

For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.

A Cross Entropy-Genetic Algorithm for m-Machines No-Wait Job-ShopScheduling Problem

No-wait job-shop scheduling (NWJSS) problem is one of the classical scheduling problems that exist on many kinds of industry with no-wait constraint, such as metal working, plastic, chemical, and food industries. Several methods have been proposed to solve this problem, both exact (i.e. integer programming) and metaheuristic methods. Cross entropy (CE), as a new metaheuristic, can be an alternative method to solve NWJSS problem. This method has been used in combinatorial optimization, as well as multi-external optimization and rare-event simulation. On these problems, CE implementation results an optimal value with less computational time in average. However, using original CE to solve large scale NWJSS requires high computational time. Considering this shortcoming, this paper proposed a hybrid of cross entropy with genetic algorithm (GA), called CEGA, on m-machines NWJSS. The results are compared with other metaheuritics: Genetic Algorithm-Simulated Annealing (GASA) and hybrid tabu search. The results showed that CEGA providing better or at least equal makespans in comparison with the other two methods.

Optimal online algorithms for scheduling on two identical machines under a grade of service

This work is aimed at investigating the online scheduling problem on two parallel and identical machines with a new feature that service requests from various customers are entitled to many different grade of service (GoS) levels, so each job and machine are labelled with the GoS levels, and each job can be processed by a particular machine only when its GoS level is no less than that of the machine. The goal is to minimize the makespan. For non-preemptive version, we propose an optimal online al-gorithm with competitive ratio 5/3. For preemptive version, we propose an optimal online algorithm with competitive ratio 3/2.

Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm

Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.

Multi-object optimization design for differential and grading toothed roll crusher using a genetic algorithm

Our differential and grading toothed roll crusher blends the advantages of a toothed roll crusher and a jaw crusher and possesses characteristics of great crushing,high breaking efficiency,multi-sieving and has,for the moment,made up for the short- comings of the toothed roll crusher.The moving jaw of the crusher is a crank-rocker mechanism.For optimizing the dynamic per- formance and improving the cracking capability of the crusher,a mathematical model was established to optimize the transmission angleγand to minimize the travel characteristic value m of the moving jaw.Genetic algorithm is used to optimize the crusher crank-rocker mechanism for multi-object design and an optimum result is obtained.According to the implementation,it is shown that the performance of the crusher and the cracking capability of the moving jaw have been improved.

Application of Grade Algorithm Based Approach along with PV Analysis for Enhancement of Power System Performance

This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.

YOLO-Banana:An Effective Grading Method for Banana Appearance Quality

The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.

基于ISSA-HKLSSVM的浮选精矿品位预测方法

针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.

基于改进CART算法的退役动力电池等级筛选方法

针对退役动力电池存在一致性差、等级筛选效率低的难题,提出了一种基于改进CART算法的退役动力电池等级筛选方法。首先,分析了传统CART算法的基本原理,为克服算法计算量大的缺陷,将Fayyad边界点判定定理与CART算法相结合,通过选取属性最优阈值点来减少计算量,提高分类效率;其次,基于代价复杂度后剪枝算法,采用交叉验证法对算法进行进一步优化;最后,将改进CART算法用于退役动力电池筛选分类,实验结果表明改进CART算法在保持较高准确率的情况下,可以有效提高退役动力电池的等级筛选效率。

果蔬采后分级和预冷车辆协同调度模型与算法

新兴的移动式分级、预冷技术应用于果蔬田间采后处理,有助于降低采后损耗,也催生了采后“最先一公里”冷链物流环节协同运作优化问题。以采后分级、预冷环节为例,综合考虑果蔬最佳预冷时间、先分级后预冷的服务顺序等特有协同情景,构建了移动式分级预冷资源协同调度优化模型。与现有模型不同,本研究考虑延迟预冷对果蔬新鲜度的特殊影响,设计了延迟预冷成本函数,在保障产品质量的同时最小化服务运作成本。设计混合遗传算法对模型进行求解,该算法融合了遗传算法与邻域搜索算法,增强混合算法的局部和全局搜索能力。其中,结合问题的双需求特点及关键协同约束,设计了基于双序列的解的表达方式、基于最佳插入策略的交叉算子以及基于三阶段邻域搜索的变异操作,以提高算法的收敛速度与求解质量。通过与标准遗传算法和变邻域搜索算法对比,验证了本文算法在求解大规模算例时可以更快收敛到更高质量的解。基于陕西省洛川县水蜜桃产业的分级预冷数据证明了模型的合理性。本研究有助于把协同运作优化思想引入果蔬采后“最先一公里”冷链物流环节,为降低我国果蔬采后损耗提供创新性解决思路。

基于GAS算法的卵砾石粒径自动识别应用研究

粒径和级配是表征床面组成的重要指标,基于GAS粒径自动识别技术可自动识别粗粒床面粒径并生成级配曲线,能够大幅提高现场采样和分析的效率。为了验证GAS的分割效果,采用GAS提供的默认参数进行分割,同时应用ImageJ软件手动分割进行验证。结果表明:GAS级配曲线的相对误差为5.7%,相关系数为0.992。另外,采用单参数和多参数敏感性分析法来标准化参数调整方案,gre、can1和can2对GAS提取的级配曲线和特征粒径有显著影响,其中gre起主导作用,而can1和can2控制着砾石边界的检测完整性。

基于STM32的牡蛎重量分级机控制系统设计

为提高牡蛎自动化加工水平,基于STM32单片机设计牡蛎重量分级控制系统。该控制系统由数据采集模块、分级控制模块、驱动模块和人机交互模块组成,以牡蛎重量为分级标准,称重托盘重量和位置信息为主要控制条件,采用遗传算法优化神经网络(GA-BP)动态称重模型和动态追踪定位方式,准确获得牡蛎重量和称重托盘位置,使用触摸屏进行人机交互,可实现称重分级一体化。试验结果表明,该控制系统的称重误差最大为0.8 g,在最大运行速度下,分级准确率达到95%,较好地实现牡蛎快速称重分级。

基于AS-OCT图像的核性白内障多级排序分类算法研究

目的探讨基于眼前节光学相干断层扫描(AS-OCT)图像的核性白内障智能辅助分级算法对白内障分级的诊断价值。方法采用诊断试验研究方法,收集2020年11月至2021年9月间同济大学附属第十人民医院电子病例系统中核性白内障患者939例1608眼的AS-OCT图像资料,所有资料均符合临床阅片清晰度要求。其中男398例664眼,女541例944眼,年龄18~94岁,平均年龄(65.7±18.6)岁。由3名经验丰富的临床医生基于晶状体混浊分类系统(LOCSⅢ分级系统),对所收集的AS-OCT图像进行1~6级人工标注。构建一种基于多级排序的全局-局部白内障分级算法,该算法包含5个基本的二元分类全局-局部网络(GL-Net),每个GL-Net聚合白内障核区域、原始图像等多尺度信息进行核性白内障分级。基于消融实验和模型对比试验,采用准确率、精确率、灵敏度、F1指标及Kappa系数对模型性能进行评价,且所有结果均通过五折交叉验证。结果模型在核性白内障数据集上的准确率、精确率、灵敏度、F1、Kappa分别为87.81%、88.88%、88.33%、88.51%、85.22%。消融实验结果表明,ResNet18结合局部特征和全局特征进行多级排序分类,模型在准确率、精确率、灵敏度、F1、Kappa指标上均有提升。与ResNet34、VGG16、Ranking-CNN、MRF-Net模型比较,本研究模型各性能指标均有提升。结论基于深度学习的AS-OCT核性白内障图像多级排序分类算法对白内障分级具有较高的准确度,有望辅助提高眼科医生对核性白内障的诊断精度以及效率。

基于机器视觉的棉花颜色检测方法

针对国内目前通过图像处理测量棉花颜色等级方法较少的现状,设计了一种基于机器视觉的棉花颜色检测方案。为提高棉花样本的拍摄质量及高效性,使用Halcon软件连接CMOS工业相机进行实时采集。首先对采集的棉花样本图进行预处理,通过阈值分割算法将棉花样本图转化为二值图像,且使用高斯滤波去噪声从而去除棉花中的杂质信息,并对预处理后的图像进行区域划分。然后通过RGB值转换为CIE XYZ颜色空间值,得到各子区域棉花颜色参数值,并引入K均值算法聚类各子区域颜色值以确定棉花最终颜色参数值,从而确定棉花颜色等级。最后通过实验验证及数据分析,将本文检测方法与MCG-1棉花检测仪器检测结果进行对比,结果表明2种方法检测结果一致;并通过在不同时间下持续对棉花样本进行数据检测,验证了本文方法的稳定性和精确性。本文检测方法可行且检测成本较低,可代替昂贵的仪器检测方法供企业使用。

基于拖拉机动态参数的路面识别及计算研究

提出了一种基于拖拉机悬架动态参数的路面高程计算方法和路面等级识别算法。由于车轮与路面之间存在直接关系,可根据拖拉机悬架非簧载质量加速度逆向计算路面高程,并建立路面高程逆推导模型。利用BP神经网络算法进行路面等级识别,选取与路面等级之间存在直接关系的非簧载质量加速度、簧载质量加速度和车速3个参数为路面等级识别的依据,即BP神经网络的输入层节点。建立了拖拉机二自由度四分之一模型,并在MatLab/Simulink平台中搭建仿真模型,得到了在各类型路面为输入时的路面高程计算和路面等级识别情况。仿真结果表明:由逆推导模型计算得到的路面高程能很好地反映原始路面高程,且路面等级识别具有较高的计算精度。

基于DBO-SVM的学生成绩三分类预测模型

为提高学生学习效率和教师教学管理水平,提出基于DBO-SVM的学生成绩三分类预测模型.首先根据皮尔逊相关系数筛选特征属性;然后针对数据不均衡问题,经对比实验选用ADASYN技术;之后由螳螂优化算法通过迭代寻优支持向量机的参数;最后由支持向量机建立学生成绩三分类预测模型.在学生成绩数据集上进行测试,实验结果表明:本文方法预测准确率比SSA-SVM提高了2.09%,达到了91.67%;F1分数的加权平均值为0.92,证明了模型的稳定性.

体质健康测试不合格大学生健康促进策略分析——以某高校2018~2022年学生体质健康测试数据为例

运用文献资料法、数理统计法、K-means算法对某高校2018~2022年学生体质健康测试总评等级不合格男女大学生成绩按各项目均值、身体形态、身体机能、身体素质三大模块和总分进行聚类分析,结果表明各模块中项目均值存在差异,身体形态与身体机能模块对总分的影响不大,而身体素质模块因涉及项目较多,利用“手肘法”确定的真实聚类数也各不相同,特征明显。针对数据分析结果,对学生体质健康测试总评等级不合格大学生提出强化健康教育与心理辅导、进行个性化评估与指导、实行针对性的健康体适能训练、进行数字化监督与反馈的健康促进策略,旨在分析此类学生的体质健康测试数据,反馈结果后进行运动干预,减少测试不及格率。

基于粒子群优化BP神经网络的汽车4S店客户流失预警

客户流失预警作为防止汽车4S店客户流失的重要手段,不仅为当代车企提供了有效的经济效益保证,也为车企对未来决策带来了新的研究依据。为建立汽车4S店客户流失预警分级标准,该文从客户基本信息、车龄、车辆销售价格、贷款金额、维修保养次数、维修保养时间等29个指标着手,基于粒子群优化BP神经网络算法,建立汽车4S店客户流失预警分级标准模型。该模型首先预测出客户流失概率,然后根据值为0-1之间的概率大小分为1-5共5个等级,其中1表流失可能性很小,5表示流失可能性很大。最终得到测试集客户流失预警从1到5等级的比例分别为71.39%、3.75%、3.50%、5.86%和15.50%。同时,通过训练集中有78.65%的客户未流失作为先验概率,判定预测概率小于等于先验概率为客户未流失,大于先验概率为客户流失,得到该模型总体的准确率为91.71%。

No-Wait Flowshops to Minimize Total Tardiness with Setup Times

The m-machine no-wait flowshop scheduling problem is addressed where setup times are treated as separate from processing times. The objective is to minimize total tardiness. Different dispatching rules have been investigated and three were found to be superior. Two heuristics, a simulated annealing (SA) and a genetic algorithm (GA), have been proposed by using the best performing dispatching rule as the initial solution for SA, and the three superior dispatching rules as part of the initial population for GA. Moreover, improved versions of SA and GA are proposed using an insertion algorithm. Extensive computational experiments reveal that the improved versions of SA and GA perform about 95% better than SA and GA. The improved version of GA outperforms the improved version of SA by about 3.5%.