Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

数据驱动的机床等待过程节能方法研究

为了降低机床等待过程中的能耗，提出了一种实时数据驱动的机床等待时间预测与节能控制方法。首先，建立了射频识别驱动的生产进度评估方法，并以生产进度数据作为输入，构建了基于堆栈降噪自编码的机床等待时间预测模型；其次，依据预测的机床等待时间，提出了机床状态切换方法，以降低机床能耗；最后，通过一个电梯零部件制造车间的案例分析，表明该方法的预测误差仅为4.1%，同时将机床等待过程能耗降低了57%，实现了制造车间的节能减排。     

浅谈城市废水水质检测方法与应用

国家与地区的经济发展水平一定离不开工业的发展,我国作为工业大国,工业发展对于国家整体经济有着不可取代的作用。然而随着工业的发展,全球性的环境污染和生态破坏越来越严重。特别是我国经济正处于快速发展阶段,城市化进程不断推进,城市污染情况日益严重,城市污水治理工作逐渐被大众所关注,逐渐开始进入公众可能给事业。我国二十世纪七十年代开始逐渐开始现代城市污水的治理工作。因此,对城市污水的水质监测工作成为了极为重要的一个环节。从城市污水检测方式这一角度进行研究,分析当前城市污水水质检验方法提升实际效果的应用模式,希望能够更好的处理我国城市日益严峻废水处理问题。     

A Polyallylamine Anchored Amine‐Rich Laser‐Ablated Graphene Platform for Facile and Highly Selective Electrochemical IgG Biomarker Detection

Current immunosensors have an insufficient number of binding sites for the recognition of biomolecules, which leads to false positive or negative results. In this research, a facile, cost‐effective, disposable, and highly selective electrochemical immunosensing platform is developed based on cationic polyelectrolyte polyallylamine (PAAMI) anchored laser‐ablated graphene (LAG). Here, for the first time, PAAMI is introduced to stabilize LAG flakes, while retaining the intrinsic thermal and electronic properties of the substrate by noncovalent π–π interaction and electrostatic physical absorption. The sensing platform offers a suitable number of anchoring sites for the immobilized antibodies by providing ? NH₂ functional groups. The proper grafting of PAAMI is confirmed through X‐ray photoelectron spectroscopy and Raman spectroscopy. The immunosensing platform is applied to detect immunoglobulin (IgG) biomarkers as a proof of concept. Under optimized conditions, the sensing platform exhibits a linear range of 0.012–15 and 15–352 ng mL^?1 with a limit of detection of 6 pg mL^?1 for IgG detection with high selectivity. Based on the analysis, the developed immunosensing platform can be used for point‐of‐care detection of IgG in clinical diagnostic centers. Furthermore, the developed strategy is well suited for the detection of other cancer biomarkers after immobilizing the relevant antibodies.     

Static pentane extraction as a potential alternative to Soxhlet extraction for polypropylene olive oil overall migration limit measurements

Static pentane extraction of olive oil from polypropylene was evaluated as a safer, higher throughput alternative to pentane Soxhlet extraction for the recovery of olive oil from polypropylene for overall migration limit determinations. Static, ambient temperature, pentane extraction achieved quantitative recovery of olive oil from polypropylene plaques prepared by using the OM‐5 overall migration limit protocol. In addition to validation data for static extractions, a modified esterification procedure is presented as a means to accommodate the larger sample sets that are enabled with static extraction.     

回弹检测法检测混凝土强度相关问题的探讨

本文就当前社会人们对建筑安全水平和质量要求不断提高的背景下,对回弹检测法检测混凝土强度的相关问题进行探讨,并以此推动回弹法检测混凝土强度行业标准的规范化发展进程,希望能够给从事相关行业的工作者提供帮助。     

基于卷积神经网络的葡萄叶片氮含量识别方法

为实现对葡萄叶片氮素含量快速、便捷的识别，在卷积神经网络VGG-16网络结构基础上，将数据增广后的图像按不同梯度划分进行模型训练，通过十折交叉验证法探究最佳的训练集与验证集分配比例，并构建4个不同深度的网络模型进行训练对比，采用全局平均池化代替全连接层约简网络参数量。训练结果表明，氮含量梯度设为0.70%、0.35%和0.175%时，室内简单背景识别准确率分别为85.9%、76.2%和71.1%；晴天室外复杂背景下识别准确率分别为44.6%、35.0%和30.4%。研究结果表明利用VGG-16建立的网络学习模型对葡萄叶片氮含量识别提供了一种新的便捷方法，对农业信息化和智能化技术应用具有一定促进作用。     

Window Detection in Facades Using Heatmap Fusion

Journal of Computer Science and Technology - Window detection is a key component in many graphics and vision applications related to 3D city modeling and scene visualization. We present a novel...