Use of Discrete Wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool

This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired.A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform(DWT)technique.The decision tree technique is used to select significant features out of all extracted wavelet features.C-support vector classification(C-SVC)andν-support vector classification(ν-SVC)models with different kernel functions of support vector machine(SVM)are used to study and classify the tool condition based on selected features.From the results obtained,C-SVC is the best model thanν-SVC and it can be able to give 94.5%classification accuracy for face milling of special steel alloy 42CrMo4.

Synchrophasing Vibration Control of Machines Supported by Discrete Isolators

This paper describes an analytical investigation into synchrophasing,a vibration control strategy on a machinery installation in which two rotational machines are attached to a beam-like raft by discrete resilient isolators.Forces and moments introduced by sources are considered,which effectively represent a practical engineering system.Adjusting the relative phase angle between the machines has been theoretically demonstrated to greatly reduce the cost function,which is defined as the sum of velocity squares of attaching points on the raft at each frequency of interest.The effect of the position of the machine is also investigated.Results show that altering the position of the secondary source may cause a slight change to the mode shape of the composite system and therefore change the optimum phase between the two machines.Although the analysis is based on a one-dimensional Euler– Bernoulli beam and each machine is considered as a rigid-body,a key principle can be derived from the results.However,the factors that can influence the synchrophasing control performance would become coupled and highly complicated.This condition has to be considered in practice.

Diagnosis of long QT syndrome via support vector machines classification

Congenital Long QT Syndrome (LQTS) is a genetic disease and associated with significant arrhythmias and sudden cardiac death. We introduce a noninva-sive procedure in which Discrete Wavelet Trans-form (DWT) is used to extract features from elec-trocardiogram (ECG) time-series data first, then the extracted features data is classified as either abnormal or unaffected using Support Vector Machines (SVM). A total of 26 genetically identified patients with LQTS and 19 healthy controls were studied. Due to the limited number of samples, model selection was done by training 44 instances and testing it on remaining one in each run. The proposed method shows reasonably high average accuracy in LQTS diagnosis when combined with best parameter selection process in the classifica-tion stage. An accuracy of 80%is achieved when Sigmoid kernel is used in v-SVM with parameters v = 0.58 and r = 0.5. The corresponding SVM model showed a classification rate of 21/26 for LQTS pa-tients and 15/19 for controls. Since the diagnosis of LQTS can be challenging, the proposed method is promising and can be a potential tool in the correct diagnosis. The method may be improved further if larger data sets can be obtained and used.

Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine

Support vector machine (SVM) has become an increasingly popular tool for machine learning tasks involving classification. In this paper, we present a simple and effective method of detect and classify hard exudates. Automatic detection of hard exudates from retinal images is worth-studying problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Discrete Cosine Transform (DCT) analysis and SVM makes use of color information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 1200 retinal images with variable color, brightness, and quality. Results of the proposed system can achieve a diagnostic accuracy with 97.0% sensitivity and 98.7% specificity for the identification of images containing any evidence of retinopathy.

韭菜培土机设计与试验

根据农艺需求,韭菜进入动态生长期后要进行2~3次培土工作,且随着韭菜假茎不断长高,培土高度也不断增加。为此,针对国内外现有中耕培土机培土易损伤韭菜茎叶、扶苗困难、不适合韭菜多次培土的问题,设计了一种韭菜培土机,经过对扶苗装置、培土刀的理论研究,确定了工作参数,并采用离散元法模拟了韭菜培土过程,分析了培土装置作业参数对土壤颗粒各轴向运动速度和垄上颗粒数量的影响。通过数据处理软件对田间试验结果进行分析,得到了最优组合参数,即刀具转速为370r/min、内挡土板展开角为93°、外挡土板展开角为68°、培土宽度为365mm、培土厚度为94mm、韭菜真叶土壤覆盖量为72g。

基于L_(2)灵敏度最小化的数字滤波器状态空间实现稀疏化方法

为解决传统数字滤波器在有限精度实现时因有限字长(Finite Word Length,FWL)效应导致滤波器性能下降的问题,提出一种L_(2)灵敏度最小化的数字滤波器状态空间实现稀疏化方法.推导前向差分算子数字滤波器结构传输函数及其等效状态空间实现,根据可控及可观格莱姆矩阵得到基于相似变换矩阵的L_(2)灵敏度表达式,并进行稀疏化校准,将L_(2)灵敏度最小化问题转换为凸函数求最值问题,求导得到L_(2)灵敏度最小化表达式,代回即得前向差分算子数字滤波器的稀疏化状态空间实现.仿真结果表明,所提方法设计的数字滤波器具有更好的抗FWL效应.

天基信息支援体系离散事件仿真与效能评估

针对天基信息支援体系效能评估中存在的准确性与实时性无法兼顾的问题,以体系仿真为基础,提出一种基于离散事件仿真的效能评估模型构建方法。首先,基于仿真工具建立离散事件仿真模型,对体系的结构与运作流程进行模拟,并基于仿真模型构建评估指标体系,生成效能评估数据样本。之后,利用深度学习对复杂映射的强大表现能力基于深度置信网络构建效能评估代理模型,结合无监督预训练和有监督调优实现网络的训练和参数优化。最后,对仿真生成的测试集样本进行评估模型验证实验。结果表明,所提方法在计算快速的同时对原评估模型有很高的还原度。

基于三维小波变换的高光谱图像分类算法

针对如何充分利用空间特征来达到较高的高光谱图像分类精度的问题,提出了一种基于三维离散小波变换(3D-DWT)与随机补丁网络(RPNet)结合的高光谱图像的地物属性分类算法。在分类过程中,综合3D-DWT提取的特征和RPNet深度学习框架提取的特征,利用支持向量机(SVM)对特征向量进行分类。所提出的方法在Indian Pines和University of Pavia两个数据集上进行测试,结果表明该方法比现有方法有显著的分类性能的提高。

Mechanism of cross-level settlements and void accumulation of wide and conventional sleepers in railway ballast

The cross-level and twist irregularities are the most dangerous irregularity types that could cause wheel unloading with the risk of derailments and additional maintenance expenses.However,the mechanism of the irregularities initiation and development is unclear.The motivation of the present study was the previous experimental studies on the application of wide sleepers in the ballasted track.The long-term track geometry measurements with wide sleepers show an enormous reduction of the vertical longitudinal irregularities compared to the conventional track.However,wide sleepers had higher twist and cross-section level irregularities.The present paper aims to explain the phenomenon by discrete element method(DEM)modeling the development process of sleeper inhomogeneous support at cross-level depending on the sleeper form.The DEM simulations show that the maximal settlement intensity is up to 3.5 times lower for a wide sleeper in comparison with the conventional one.Nevertheless,the cross-level differential settlements are almost the same for both sleepers.The particle loading distribution after all loading cycles is concentrated on the smaller area,up to the half sleeper length,with fully unloaded zones under sleeper ends.Ballast flow limitation under the central part of the sleeper could improve the resilience of wide sleepers to the development of cross-level irregularities.The mechanism of initiation of the cross-level irregularity is proposed,which assumes the loss of sleeper support under sleeper ends.The further growth of inhomogeneous settlements along the sleeper is assumed as a result of the interaction of two processes:ballast flow due to dynamic impact during void closing and on the other side high pressure due to the concentration of the pressure under the middle part of the sleeper.The DEM simulation results support the assumption of the mechanism and agree with the experimental studies.

Wavelet Multiview-Based Hybrid Deep Learning Model for Forecasting El Niño-Southern Oscillation Cycles

The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields.

岩化法防渗支护体系稳定性离散元模拟研究

为验证岩化法防渗支护体系的稳定性,采用PFC软件基于非连续介质理论的颗粒离散元方法,对无支护强透水性地层、仅钢管模袋混凝土桩支护的强透水性地层、岩化法防渗支护体系支护的强透水性地层三种不同的工况,在基坑开挖过程进行稳定性数值模拟,从颗粒位移、接触力链以及位移矢量三个方面进行了对比分析,判断岩化法防渗支护体系提升了强透水性地层的防渗性能,同时提升了稳定性。

基于离散小波变换的意识障碍患者意识水平评估

意识障碍(DOC)包括不同反应水平的神经状态,如无反应觉醒综合症(又称植物人状态,UWS/VS)和最小意识状态(MCS)。准确的对意识障碍患者进行意识水平评估,有助于医生为意识障碍患者提供合适的康复治疗方案,以最大程度恢复或改善患者的意识水平。脑电图能够实时记录反映意识障碍患者意识水平的生理电活动,是实现意识水平评估的重要工具。基于此,该文利用离散小波变换对意识障碍患者的静息态脑电图数据进行处理,以获取包含不同脑电频带信息的多尺度信号。然后,从这些多尺度信号中提取了不同生理脑电图频带的关键特征——功率、谱熵和相干度。最后,采用融合特征结合支持向量机(SVM)分类器实现意识障碍患者的意识水平评估。实验结果显示,该方法获得了91.37%的分类准确率。与已有的意识水平评估方法相比较,该方法在意识障碍患者分类识别方面表现出更高的准确率。

基于CNM-BA的投标异常行为的离散化信息识别技术

电力供应商围标串标主要依靠人工经验识别,准确率低、识别时间长和识别误差大。结合围标、串标中的离散化行为特征,设计基于社团检测-蝙蝠算法(CNM-BA)识别围标串标行为。融合改进蝙蝠算法和关联规则算法提取围标串标行为特征,离散化获取交易轨迹和行为特点,创新性地离散化改进蝙蝠算法;利用支持向量机算法对交易轨迹和特点信息进行识别分类,识别围标串标异常行为。实验结果表明,该方法的识别准确率高于88%,识别时间小于10 s,降低了识别误差,可以准确识别围标串标行为。

A Discrete Event Simulation Support System in C^(++)

This paper describes a discrete simulation support system that forms major parts of most simulation program. The support system contains three main features which differ from the most of other simulation support systems. It follows a strict three phase structure; supports visual interactive simulation. The principles of designing and implementing of the system are explained module by module.

浅埋隧道围岩稳定性与支护效果分析

本文以泉州某隧道浅埋段作为研究对象,运用离散元模拟软件,研究了地表倾角为30°、45°、60°、90°四种工况下,围岩变形、地表沉降、围岩应力、塑性区体积的变化规律;基于上述研究,分析了锚喷支护与注浆加固对围岩变形和力学特性的影响。研究表明:在无支护条件下土质浅埋隧道段围岩无自稳能力,地表边坡容易发生大面积下沉甚至滑坡;随着地表倾角增大塑性区体积、地表下沉量、地表下沉范围增大。锚喷支护可以显著降低地表沉降,缩小地表沉降影响范围,大幅度抑制围岩变形,降低塑性区体积,但是并未彻底改变地表和隧道围岩失稳的风险。注浆加固形成的加固圈提高了围岩力学参数,围岩自稳能力得到提升,锚喷支护可为长期围岩蠕变提供更大安全空间。

基于ARENA的场站飞行保障过程建模与仿真

运用离散事件仿真(DES)方法建立了航空兵场站飞行保障过程的数学模型,包括单个保障类型Ack模型、和整个飞行保障过程的层次结构模型PRi,以及飞行保障过程仿真策略模型PS。并在所建的数学模型的框架下利用离散事件仿真软件ARENA对航空兵场站网络化飞行保障过程进行了仿真。仿真结果表明:网络化条件下的主动保障方式的保障时间可以比传统的被动保障方式的保障时间缩短4.3分钟,提高保障效率10.5%。

装甲装备维修保障资源优化仿真研究

针对装甲装备维修保障资源配置数量优化测算问题,分析了装甲装备维修保障过程,基于离散系统建模理论,构建了装甲装备维修保障过程概念模型和数学模型;应用Anylogic仿真软件,建立了基于网络离散事件的装甲装备维修保障过程仿真模型,经过仿真运算,得到装甲装备维修保障任务满足度、资源利用率、平均在修时间等参数指标,进行了基于指标权重的维修保障资源评价,得到在不同条件下维修保障资源配置数的最优值,为装备维修保障资源优化研究提供一种新途径。

基于支持向量机的人脸分类

提出了一种基于支持向量机的人脸分类方法,首先对人脸图像作二维离散余弦变换,取离散余弦变换系数作为特征,然后用支持向量机进行分类。用Essex人脸图像数据库进行性别分类,取得了很好的分类效果。

粗糙集中连续属性离散化的一种新方法

提出一种新的间接离散化方法——超曲面法。首先给出了超曲面的定义 ,证明了超曲面为一个高阶的多项式 ,且给出了该多项式的总项数 ;同时证明了超曲面数与最大决策规则数之间的关系。然后提出了应用支持向量机来求取最优超曲面的方法。最后以空军低消耗器材的储存策略为例 ,说明了超曲面的求解过程。实例仿真结果表明 ,用支持向量机来求解超曲面 ,不仅方法简单 ,而且较容易寻得最优解。结果还表明 ,文中提出的超曲面间接离散方法能较好地区分决策表中的决策类别 ,从而获得更为简捷的决策规则。

三峡库区软硬互层近水平地层高切坡崩塌研究

由泥、砂岩互层或厚层砂岩夹泥岩等形成的类似"夹心饼干"的软硬互层近水平层状结构边坡,是三峡库区分布较为广泛的一种结构类型边坡。由于泥、砂岩风化速度的不同而产生的崩塌灾害现象是三峡库区最为常见的一种地质灾害,首先,从地形地貌、地层岩性、结构面组合、降雨作用和风化作用等方面对软硬互层高切坡的崩塌形成原因进行研究,软硬岩的差异性风化而造成的软岩空腔和硬岩中的结构面组合是造成崩塌的主要原因;其次,基于野外调查的地质现象分析,得到了软硬互层高切坡崩塌主要有倾倒、滑移、塑流拉裂、悬臂拉裂、错断等破坏机制,并概化出了各类破坏示意图;通过离散元数值模拟,对泥砂岩软硬互层高切坡崩塌的形成破坏过程进行了再现和分析,其破坏过程为泥岩剥落-岩腔-砂岩裂隙张开-危岩体弯曲-倾倒崩塌-堆积坡脚;最后,在三峡库区高切坡各类破坏机制的基础上,提出了相应的处治对策。研究成果对三峡库区高切坡地质灾害的认识和防治有一定的意义。