基于kmeans-SVM的二叉树粗分类方法

针对大型数据库中进行匹配识别时存在识别速度慢、时间长、影响实时应用效果的问题,提出了一种树形层次结构的粗分类方法。通过k-means得到两类粗分类的样本,用这两类粗分类数据训练SVM分类器,找到分类超平面,再不断调整分类超平面,最后构建二叉树型结构达到粗分类的目的。三个方法相结合很好地缩小目标的搜索范围,提高了识别时候的效率。     

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.     

Survey on Passivity Based Control of Induction Machine

Induction machines (IM) constitute a theoretically interesting and practically important class of nonlinear systems. They are frequently used as wind generators for their power/cost ratio. They are described by a fifth‐order nonlinear differential equation with two inputs and only three state variables available for measurement. The control task is further complicated by the fact that IM are subject to unknown (load) disturbances and the parameters can be of great uncertainty. One is then faced with the challenging problem of controlling a highly nonlinear system, with unknown time‐varying parameters, where the regulated output, besides being unmeasurable, is perturbed by an unknown additive signal. Passivity‐based control (PBC) is a well‐established structure‐preserving design methodology which has shown to be very powerful to design robust controllers for physical systems described by Euler‐Lagrange equations of motion. PBCs provide a natural procedure to "shape" the potential energy yielding controllers with a clear physical interpretation in terms of interconnection of the system with its environment and are robust vis á vis to unmodeled dissipative effects. One recent approach of PBC is the Interconnection and Damping Assignment Passivity‐Based Control (IDA‐PBC) which is a very useful technique to control nonlinear systems assigning a desired (Port‐Controlled Hamiltonian) structure to the closed‐loop. The aim of this paper is to give a survey on different PBC of IM. The originality of this work is that the author proves that the well known field oriented control of IM is a particular case of the IDA‐PBC with disturbance.     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

基于振动实测的非均质地层钻头失效分析与对策

四川盆地作为国内页岩气开发的重点区域,通过近几年的不断攻关和实践,机械钻速得到了提高,但由于受地层复杂、可钻性差、非均质性强等地质因素的影响,导致井下钻柱系统不良振动剧烈,容易出现钻头损坏严重、钻速较低等问题,严重影响了钻井时效。为了解决上述难题,以该盆地涪陵工区上二叠统龙潭组—中二叠统茅口组为例,采用井下振动高频测量工具的实测手段,测量了钻头—钻柱系统的动态振动加速度参数,结合地层的岩性和矿物组分分析,研究钻头失效原因与对策,并开展了现场试验。研究结果表明:①在非均质地层中钻进的钻头—钻柱系统产生了大于40 m/s~2的高幅值瞬时冲击振动,高幅值的瞬时冲击是导致钻头先期失效的主要原因;②提出了抑制高幅值的瞬时冲击振动采用"减振+增压"工具组合和避免井下工具共振的钻井参数;③采用钻井新参数的试验井比邻井的高幅值瞬时振动降低了17%,单只钻头进尺增加24%,钻头工作环境得到了较大的改善,钻头使用数量减少。结论认为,该研究成果能够有效地改善钻头—钻柱系统的振动状态,有利于达成延长钻头使用寿命的目标。     

低觉醒脑电识别与唤醒的可穿戴系统研究

为智能化地识别警戒作业人员出现的低觉醒、注意力下降的生理状态，本文介绍了一种基于FPGA和脑电信号处理的低觉醒状态检测与唤醒系统，系统通过传感器从大脑头皮采集脑电信号，转换为数字信号，经傅里叶变换获取了脑电信号的θ相对能量、α相对能量、重心频率、谱熵等4个特征量，由4个特征量表征低觉醒状态并运用支持向量机对低警戒状态进行识别，当识别出低觉醒状态时采用声音报警模块发出声音，唤醒警戒作业人员。设计系统能够较好地识别出低觉醒状态，识别率达90.8%，可为提高警戒作业工作绩效提供一种可穿戴的智能装备。     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

基于Kӧtter方程的盾构掘进面上被切削土体受力研究

为了计算不同土体性质与不同掘进参数下盾构刮刀与土体之间的作用力以及由此带来的切削扭矩,引入了修正后的Kӧtter方程作为补充方程,求解出开挖面上土体在被动切削至破坏时滑动面上的应力分布,建立了被切削土体受力模型,求解出刮刀刃面与土体之间的正压力p、摩擦力Q与侧向土体间抗剪阻力U,进而推导出刀盘切削扭矩T的计算公式。分析得到特定刀盘上刮刀的p,Q,U值随土体c,φ值增大而增大,通过适当提高刮刀刃面粗糙度来增大摩擦可使土体更易被切至破坏;p,Q,U值随刀盘转速ω增大而减小,p,Q随支护荷载p线性增大且p对于被切削土体受力的影响在砂性土中较大,最终给出特定刀盘掘进至部分种类土体中的切削扭矩值作为工程参考。     

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。