Machine Learning for Hybrid Line Stability Ranking Index in Polynomial Load Modeling under Contingency Conditions

In the conventional technique,in the evaluation of the severity index,clustering and loading suffer from more iteration leading to more com-putational delay.Hence this research article identifies,a novel progression for fast predicting the severity of the line and clustering by incorporating machine learning aspects.The polynomial load modelling or ZIP(constant impedances(Z),Constant Current(I)and Constant active power(P))is developed in the IEEE-14 and Indian 118 bus systems considered for analysis of power system security.The process of finding the severity of the line using a Hybrid Line Stability Ranking Index(HLSRI)is used for assisting the concepts of machine learning with J48 algorithm,infers the superior affected lines by adopting the IEEE standards in concern to be compensated in maintaining the power system stability.The simulation is performed in the WEKA environment and deals with the supervisor learning in order based on severity to ensure the safety of power system.The Unified Power Flow Controller(UPFC),facts devices for the purpose of compensating the losses by maintaining the voltage characteristics.The finite element analysis findings are compared with the existing procedures and numerical equations for authentications.

Flexible Scanning Method by Integrating Laser Line Sensors with Articulated Arm Coordinate Measuring Machines

Measuring and reconstructing the shape of workpieces have been considered as a fundamental step in both reverse engineering and product quality control.Owing to increasing structural complexity of recent products,measurements from multiple directions are typically required in current scanning techniques.Specifically,the plane structured light can be applied to measure one area of a part at a time,with an additional algorithm required to merge the collected data of each area.Alternatively,the line structured light sensor integrated on CNC machines or CMMs could also realize multi-view measurement.However,the system needs to be repeatedly calibrated at each new direction.This paper presents a flexible scanning method by integrating laser line sensors with articulated arm coordinate measuring machines(AACMM).Since the output of the laser line sensor is 2D raw data in the laser plane,our system model introduces an explicit transformation from the 2D sensor coordinate frame to the 3D base coordinate frame of the AACMM(i.e.,the translation and rotation the of the 2D sensor coordinate in the sixth coordinate system of AACMM).To solve the model,the“conjugate pairs”are proposed and identified by measuring a fixed point(e.g.,a sphere center).Moreover,a search algorithm is adopted to find the optimal solution,which noticeably boosts the model accuracy.The experimental results show that the error of the system is about 0.2 mm,which is caused by the error of the AACMM,the sensor error and the calibration error.By measuring a complicated part,the proposed system is proved to be flexible and facilitate,with the ability to measure a part expediently from any necessary direction.Furthermore,the proposed calibration method can also be used for robot hand-eye relationship calibration.

On-line Estimation of Biomass in Fermentation Process Using Support Vector Machine

Biomass is a key factor in fermentation process, directly influencing the performance of the fermenta- tion system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine (SVM) for an on-line biomass estimation was ana- lyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.

On-line Chatter Detection Using an Improved Support Vector Machine

On-line chatter detection can avoid unstable cutting through monitoring the machining process.In order to identify chatter in a timely manner,an improved Support Vector Machine(SVM)is developed in this paper,based on extracted features.In the SVM model,the penalty factor(e)and the core parameter(g)have important influence on the classification,more than from Kernel Functions(KFs).Hence,first the classification results are conducted using different KFs.Then two methods are presented for exploring the best parameters.The chatter identification results show that the Genetic Algorithm(GA)approach is more suitable for deciding the parameters than the Grid Explore(GE)approach.

On-line Condition Monitoring and Diagnostic Network System for Rotating Machine Set

Several industrial computers and a server are combined to set up the on-line monitoring and diagnostic system of turbo-generator sets. The main function of the system is to monitor machine sets' running condition. Through analyzing running data, technicians can detect whether there exist faults and where they occur. To share and transmit the dynamic information of the turbo-generator sets, a distributed network system is introduced. NetWare network operating system is used in the LAN (Local Area Network) system. The LAN is extended to realize the sharing of data and remote transmission of information. Furthermore, functions of monitoring and diagnostic clients are listed.

A Transmission Line Fault Classification Approach by Support Vector Machines

This paper presents an approach for shunt faults detection and classification in transmission line using Support Vector Machine （SVM）. The paper compares between using three line post-fault current samples for one-half cycle and one-fourth cycle from the inception of the fault as inputs for SVM. Two SVMs are used, first SVMabc is used for faulty phase detection and second SVMg is used for ground detection. SVMs with polynomial kernel with different degrees are used to obtain the best classification score. The classification test results show that the proposed method is accurate and reliable.

Machining Line Planner输出STEP-NC数控程序的研究

介绍了STEP-NC的概念、数据模型及其结构特点,然后通过对比MLP(Machining Line Planner)和STEP-NC数控程序对特征和操作的不同定义方法,分析了在MLP中特征及加工工艺与STEP-NC的对应关系,探讨了在MLP中实现输出STEP-NC格式的零件加工程序的方法。

Method for the navigation line recognition of the ridge without crops via machine vision

Some agriculture machinery like the transplanter,needs to operate by following the crop-free ridges.In order to improve working efficiency and quality,some autonomous navigation systems were developed and applied to ridge-following machinery.At present,agricultural navigation systems are mainly the satellite navigation system and the machine vision system.The satellite navigation system is difficult to apply to the machinery that needs to work by following the ridge because it cannot distinguish the shape of the navigated ridge and guide the machinery working along the ridge.In this study,697 cloudy ridge images and 235 sunny ridge images were taken in the field,and these images were used as the dataset.Moreover,a machine vision navigation method based on the color of ridges was proposed.Firstly,the regions of interest(ROI)in the ridge image were extracted according to the reaction time and the forward speed of the machine.Then,a gray reconstruction method was used to enlarge the color difference between the ridge and the furrow.The optimal threshold for the gray image segmenting was calculated real-timely by using the threshold segmentation method.Then,based on the contour detection method,the ridge contour which was not surrounded by holes was extracted.Finally,the approximate quadrilateral method was proposed to recognize the ridge center line as the navigation line.The method proposed in this study was verified by four types of ridges with different colors and textures.The experimental results showed that the recognition success rates of the light ridge,the dark ridge,the film-covered ridge,and the sunny ridge were 100%,97.5%,100%,and 98.7%,respectively.The recognition success rate of the proposed method was at least 8%higher than that of the existing ridge-furrow recognition methods.The results indicate that this method can effectively realize navigation line recognition.This method can provide technical support for the autonomous navigation of agricultural machinery,such as transplanters,seeders,etc.,operating on the ridge without crops.

Optimization of Line Configuration and Balancing for Flexible Machining Lines

Line configuration and balancing is to select the type of line and allot a given set of operations as well as machines to a sequence of workstations to realize high-efficiency production. Most of the current researches for machining line configuration and balancing problems are related to dedicated transfer lines with dedicated machine workstations. With growing trends towards great product variety and fluctuations in market demand, dedicated transfer lines are being replaced with flexible machining line composed of identical CNC machines. This paper deals with the line configuration and balancing problem for flexible machining lines. The objective is to assign operations to workstations and find the sequence of execution, specify the number of machines in each workstation while minimizing the line cycle time and total number of machines. This problem is subject to precedence, clustering, accessibility and capacity constraints among the features, operations, setups and workstations. The mathematical model and heuristic algorithm based on feature group strategy and polychromatic sets theory are presented to find an optimal solution. The feature group strategy and polychromatic sets theory are used to establish constraint model. A heuristic operations sequencing and assignment algorithm is given. An industrial case study is carried out, and multiple optimal solutions in different line configurations are obtained. The case studying results show that the solutions with shorter cycle time and higher line balancing rate demonstrate the feasibility and effectiveness of the proposed algorithm. This research proposes a heuristic line configuration and balancing algorithm based on feature group strategy and polychromatic sets theory which is able to provide better solutions while achieving an improvement in computing time.

Description of a Propulsion Unit Used in Guiding a Walking Machine by Recognizing a Three-point Bordered Path

A reconfigurable propulsion unit based on the Peaucellier-Lipkin mechanism has the ability to describe exact straight or curved paths depending on the selected ratio between the lengths of two of its links. The Peaucellier-Lipkin mechanism with one degree of freedom is transformed into a more sophisticated parallel kinematic chain by including four more degrees of freedom. The resulting propulsion unit is able to adapt its kinematic structure and reach instant centers of rotation, in accordance with the presence of three points that border a geometric path. A laser sensor mounted on the body of the machine detects each point. Once the machine has detected the exact location of the border of the road, it walks along a curve parallel to that border. Although the proposed research describes only one propulsion unit or leg, the methodology can be applied to all the legs of the walking machine. The novel 5-DOF leg is able to reach different centers of rotation, providing either the concave or convex arcs that satisfy the basic principle of displacement of walking machines.

A Study on Comprehensive Modeling for Errors in on-line Measuring System of Machining Center

Based on the theory of multi-body system (MBS), bine’s and huston’s methods are applied to an on-line measuring system of machining center in this paper. Through the study on modeling technique, the comprehensive model for errors calculation in an on-line measuring System of machining center have been built for the first time. Using this model, the errors can be compensated by soft.ware and the measuring accuracy can be enhanced without any more inveSt. This model can be used in all kinds of machining center.

Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .

The Analysis of the Combined Territorial Machining and Contour Line Machining Based on the CAXA Manufacturing Engineer 2004

This paper introduces the rough machining of territorial machining and contour line machining based on the CAXA manufacturing engineer 2004, and gives an analysis of the combined machining of territorial machining and contour line machining. Finally, it proves the superiority of the combined machining by the example of high speed machining of an aluminium alloy lamina part.

Application Research of Gravel and Machine-Made Sand along the KKH-2 Project in Pakistan on Asphalt Pavement

According to the characteristics of stone along the KKH-2 project in Pakistan, the applicability of gravel and machine-made sand for road engineering was studied. Through investigation, the types of stone along the project were relatively simple, and the stone materials used for road construction were mainly limestone, sandstone and pebbles, and the reserves?were?abundant. The experiment research and analyses comparisons of the parameters and road performance characteristics of natural gravel materials were carried out, and the design parameters and road performance indicators of natural grit in the current code were supplemented and adjusted to make it more suitable for Pakistan to use natural gravel materials for road construction. Thesis combines the project,?proposing that mechanism sand and natural sand mixed concrete?is?not inferior?tonatural sand mixed concrete in terms of technical performance, and the overall cost is lower than that of natural sand mixed concrete. The research results are of great significance for saving engineering construction costs, ensuring road performance and prolonging service life.

The “Ocean Stabilization Machine” May Represent a Primary Factor Underlying the Effect of “Global Warming on Climate Change”

Contemporary references to global warming pertain to the dramatic increase in monthly global land surface temperature (GLST) anomalies since 1976. In this paper, we argue that recent global warming is primarily a result of natural causes;we have established three steps that support this viewpoint. The first is to identify periodic functions that perfectly match all of the monthly anomaly data for GLST;the second is to identify monthly sea surface temperature (SST) anomalies that are located within different ocean basin domains and highly correlated with the monthly GLST anomalies;and the third is to determine whether the dramatically increasing (or dramatically decreasing) K-line diagram signals that coincide with GLST anomalies occurred in El Ni&#241o years (or La Ni&#241a years). We have identified 15,295 periodic functions that perfectly fit the monthly GLST anomalies from 1880 to 2013 and show that the monthly SST anomalies in six domains in different oceans are highly correlated with the monthly GLST anomalies. In addition, most of the annual dramatically increasing GLST anomalies occur in El Ni&#241o years;and most of the annual dramatically decreasing GLST anomalies occur in La Ni&#241a years. These findings indicate that the “ocean stabilization machine” might represent a primary factor underlying the effect of “global warming on climate change”.

结合自适应阈值与动态ROI的地标线检测方法

基于机器视觉的地标线检测一直以来都是车间自主巡检机器人研究的重点和难点问题之一.针对检测过程中易受车间地面积水、光照不均以及油渍粉尘污染等环境因素的影响从而导致误检率、漏检率较高,难以满足工程实际需求的问题,本文提出了一种基于自适应阈值分割与动态感兴趣区域划分的车间地标线检测方法(AT-DROI),该方法以分块图像为基础,首先按照特定特征对子块进行搜索,并剔除其中的离群噪声点,然后将环境因子与采样点相结合计算出特征颜色的双门限阈值,最后利用粗细粒度掩膜抑制特征图像中的干扰信息,有效实现了室内复杂环境下对地标线的高精度检测.通过在自采集的实际车间场景视频序列上进行大量对比实验,实验结果表明,本文方法在自采集的多组实际车间场景视频序列上测试的平均准确率、假阳性率和假阴性率分别达到了95.15%、3.73%和4.85%,平均每秒检测帧数为26fps,能够满足实际生产中对地标线检测的准确性和实时性要求.

基于BA-MKELM的微电网故障识别与定位

提出一种基于贝叶斯算法优化多核极限学习机的微电网故障识别和定位方法。针对极限学习机输入参数和隐含层节点数随机选取导致回归能力不足的问题,引入核函数,将多项式与高斯径向基核函数加权组合构成多核极限学习机建立故障识别与定位模型,并采用贝叶斯算法对多核极限学习机相关参数进行优化,进一步提高模型的逼近能力。为了验证所提模型的故障识别与定位性能,选用极限学习机和多核极限学习机分别建立故障诊断模型进行比较分析。实验结果表明,所提方法能够高性能地识别和定位微电网中任何类型的故障,识别和定位精度更高。

基于结构光的飞机蒙皮对缝自主测量跟踪技术

针对飞机蒙皮对缝间隙和阶差的自动化测量问题,以线结构光视觉检测技术为基础,研究了蒙皮对缝自主测量跟踪方法。将传统的基于图像形态学的处理方法和光流法结合,实现了对蒙皮对缝特征的实时跟踪,从而确定间隙和阶差的测量位置。通过实测数据对手眼标定参数进行修正,提高了对缝跟踪的精度。结果表明,当跟踪速度为7 mm/s时,对缝跟踪的位置精度大于0.500 mm,对缝跟踪的姿态精度大于0.5°,跟踪系统具有较好的准确性和稳定性,能够满足飞机蒙皮对缝跟踪的要求。

基于集成树和MoE的馈线统计线损率双层估计模型

统计线损率是衡量电力系统经济运行的重要指标。然而,用户用电数据采集异常、数据传输中断等因素会导致统计线损率异常或缺失,这严重阻碍了智能配电网的线损精益化管理与经济高效运行。针对馈线统计线损率合理值的估计问题,该文提出了一种基于集成树和混合专家系统(MoE)的馈线统计线损率双层估计模型。首先,使用最大信息系数以更有效地分析统计线损率与其相关特征间的非线性关系,并采用鲁棒性强的K-Medoids聚类算法对馈线进行精细划分;然后,使用Stacking集成学习框架,基于基估计和元估计双层模型对馈线统计线损率进行两阶段估计,选用决策树和各类集成树模型作为基估计模型对统计线损率进行初步估计,将各基估计模型输出结果输入元估计模型MoE中进行最终估计,使用方均根误差(RMSE)和平均绝对误差(MAE)来衡量模型所估计统计线损率的合理性;最后,通过算例分析表明,与其他模型相比,该文所提馈线统计线损率双层估计模型具有更低的RMSE和MAE,对馈线统计线损率的估计效果更好。

大尺寸工件螺孔的机器视觉测量方法研究

基于3C产品外壳工件螺纹圆孔加工时通常采用角点定位的特点,利用线阵相机采集子图像的连续性,提出基于直线特征和圆特征定位的螺纹圆孔位置尺寸测量方法。基于拟合法改进的Hough直线定位方法和基于乘同余法改进Hough变换圆形检测方法,实现3C外壳工件图像边缘定位及螺纹圆孔的圆心坐标提取及直径尺寸测量;基于图像的连续性,采用子图像逐一检测,相邻子图像起始像素累加的方式,实现大尺寸图像螺纹圆孔尺寸全局检测。上述方法最终实现大尺寸3C外壳工件螺纹圆孔的位置尺寸测量精度为±0.2220mm和孔径大小测量±0.0904mm,单工件检测耗时为33.737s。