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| 1 | Progress in the bionic study on anti-adhesion and resistance reduction of terrain machines显示文摘The theoretical studies of bionics of machinery have great scientific significance, and the development of bionic machines has large practical values in the field of engineering and technology. Through the rigorous selection process of evolution, the survived living organisms have successfully developed outstanding abilities to adapt to their surroundings and to reproduce their offspring. In this review,we interpreted the fundamental principles of anti-adhesion and anti-resistance of soil animals by reviewing the current status in this research field and summarizing the work of the research group at Jilin University of China in the past decades. The principles and technologies used in morphology bionics,electric-osmosis bionics,flexibility bionics,configuration bionics and coupling bionics were examined.Finally,the applications of the engineering bionics and their extensive prospects were introduced. | REN LuQuanKey Laboratory for Terrain-Machine Bionics Engineering of Ministry of Education, School for Biology and Agricultural Engineering, Jilin University, Changchun 130025, China | 2009 | Science China(Technological Sciences)2009，52，2： | 64 |
| 2 | Application of several optimization techniques for estimating TBM advance rate in granitic rocks显示文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior. | Danial Jahed Armaghani Mohammadreza Koopialipoor Aminaton Marto Saffet Yagiz | 2019 | Journal of Rock Mechanics and Geotechnical Engineering2019，11，4： | 15 |
| 3 | A reliability assessment method based on support vector machines for CNC equipment显示文摘With the applications of high technology,a catastrophic failure of CNC equipment rarely occurs at normal operation conditions.So it is difficult for traditional reliability assessment methods based on time-to-failure distributions to deduce the reliability level.This paper presents a novel reliability assessment methodology to estimate the reliability level of equipment with machining performance degradation data when only a few samples are available.The least squares support vector machines(LS-SVM) are introduced to analyze the performance degradation process on the equipment.A two-stage parameter optimization and searching method is proposed to improve the LS-SVM regression performance and a reliability assessment model based on the LS-SVM is built.A machining performance degradation experiment has been carried out on an OTM650 machine tool to validate the effectiveness of the proposed reliability assessment methodology. | WU Jun DENG Chao SHAO XinYu XIE S Q | 2009 | Science China(Technological Sciences)2009，52，7： | 14 |
| 4 | Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines显示文摘Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed. | Nassim Laouti Sami Othman Mazen Alamir Nida Sheibat-Othman | 2014 | International Journal of Automation and computing2014，11，3： | 11 |
| 5 | 面向对象的航空高光谱图像混合分类方法显示文摘传统的高光谱分类通常仅考虑单一像元的光谱或纹理特征,分类后容易出现地物破碎的现象。鉴于此,本文提出了一种面向对象的混合分类方法,将面向对象的分割结果与传统的像元级分类结果进行有机融合,充分利用对象的光谱特征和空间结构特征。在此基础上,引入了2种具体的混合分类方法,即多尺度分割的SVM分类和多波段分水岭分割的SVM分类。前者将地物光谱的可变性进行弱化处理,转化为多尺度均质对象单元进行分类;后者融入了地物的空间信息和形态学特征,对分割得到的同质区域进行分类。将这2种分类方法应用于航空高光谱数据,实验结果表明:面向对象的混合分类方法的总体精度分别为92.63%和96.13%,与传统的像元级分类法相比,分别提高了10.14%和13.64%,有效地解决了分类后地物的破碎现象。 | 李雪轲 王晋年 张立福 杨杭 刘凯 | 2014 | 地球信息科学学报2014，16，6： | 10 |
| 6 | Kinetic Characteristics Analysis of Aircraft During Heavy Cargo Airdrop显示文摘Airdrop is the most important approach for crisis transaction and unexpected events, it is necessary to investigate the flight characteristics of transport aircraft during the dropping process. This paper mainly focuses on the stability, controllability and model simplification of large aircraft with heavy cargo airdrop. In this process, the primary elements which have impact on force and moment are studied theoretically, the role of cargo mass, moving parameters and other factors on dynamical characteristics have been assessed by simulation and analysis. And then the aircraft model simplification is completed for control system designing in future.All the work above shows that the parameters of cargo moving play a dominant role in flight characteristics and the flight equations can be simplified to reduce the design complexity. | Jie Chen Cun-Bao Ma Dong Song | 2014 | International Journal of Automation and computing2014，11，3： | 3 |
| 7 | Fast Training of Support Vector Machines Using Error-Center-Based Optimization显示文摘This paper presents a new algorithm for Support Vector Machine (SVM) training, which trains a machine based on the cluster centers of errors caused by the current machine. Experiments with various training sets show that the computation time of this new algorithm scales almost linear with training set size and thus may be applied to much larger training sets, in comparison to standard quadratic programming (QP) techniques. | L. Meng, Q. H. Wu Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool, L69 3GJ, UK | 2005 | International Journal of Automation and computing2005，2，1： | 3 |
| 8 | Quest towards “factoring larger integers with commercial D-Wave quantum annealing machines”显示文摘Integer factorization (IFP), also called prime factorization, is an important problem in number theory, cryptography, and quantum computation. Factoring large integers to attack the RSA cryptosystem is intractable for powerful supercomputers, let alone classical computers. In 1994, Shor [1]presented an algorithm that potentially enabled a quantum computer to find prime factors in polynomial time. | XinMei Wang | 2019 | Science China(Physics,Mechanics & Astronomy)2019，62，6： | 2 |
| 9 | A novel algorithm for satellite data transmission显示文摘For remote sensing satellite data transmission,a novel algorithm is proposed in this paper.It integrates different type feature descriptors into multistage recognizers.In the first level,the dynamic clustering algorithm is used.In the second level,the improved support vector machines algorithm demonstrates its validity.In the third level,the shape matrices similarity comparison algorithm shows its excellent performance.The single child recognizers are connected in series,but they are independent of each other.Objects which are not recognized correctly by the lower level recognizers are then put into the higher level recognizers.Experimental results show that the multistage recognition algorithm improves the accuracy greatly with higher level feature descriptors and higher level recognizers.The algorithm may offer a new methodology for high speed satellite data transmission. | ZHANG ShouJuan ZHOU Quan | 2009 | Science China(Technological Sciences)2009，52，5： | 2 |
| 10 | 基于最小二乘支持向量机的电力系统混沌振荡控制显示文摘电力系统在周期性负荷扰动的作用下会发生混沌振荡,甚至由此而失去稳定.为抑制这种情况下的混沌振荡对电力系统的影响,利用支持向量机良好的非线性函数逼近和泛化能力,提出了最小二乘支持向量机(LS-SVM)的电力系统混沌振荡控制方法.运用最小二乘支持向量机对电力系统的动力学特性进行学习,得到训练好的电力系统LS-SVM模型,进而实现对电力系统混沌振荡的控制.该方法不需要被控混沌系统的解析模型,数值仿真结果表明该方法的可行性. | 谭文 李志攀 张敏 | 2010 | 湖南科技大学学报(自然科学版)2010，25，3： | 2 |
| 11 | 基于遗传算法和最小二乘支持向量机的织物剪切性能预测显示文摘提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力.
Abstract:
A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method. | 卢桂馥 王勇 窦易文 Gui-fu Yi-wen | 2009 | 计量学报2009，，6： | 2 |
| 12 | 一种基于加权支持向量机的垃圾邮件过滤方法显示文摘基于内容的垃圾邮件过滤本质上是文本分类问题,支持向量机分类器非常适合于垃圾邮件过滤这一二分类问题,但标准的支持向量机是基于分类精度进行优化的,对两类邮件的重要性未以区别,造成了邮件分类时虽然整体精度较高,但对正常邮件的误判率也较高.据此笔者提出了一种基于加权支持向量机的垃圾邮件过滤算法,通过增加两类邮件的类别权重及反映每封邮件重要性的权重,对支持向量机分类器进行训练,在保证分类精度的同时,尽可能地降低对正常邮件的误判率.实验表明该算法取得了很好的过滤效果. | 陈孝礼 刘培玉 张立伟 | 2009 | 山东师范大学学报(自然科学版)2009，24，4： | 1 |
| 13 | NEW INSIGHTS INTO PRINCIPLES FOR DESIGNING ARCHITECTURE OF ENVIRONMENT SYSTEM FOR TOOL INTEGRATION ─IN THE CASE OF PRODUCTION MACHINES DESIGN显示文摘NEWINSIGHTSINTOPRINCIPLESFORDESIGNINGARCHITECTUREOFENVIRONMENTSYSTEMFORTOOLINTEGRATION─INTHECASEOFPRODUCTIONMACHINESDESIGNZha... | Zhang Dan Zhang Wenjun | 1996 | Computer Aided Drafting,Design and Manufacturing1996，6，1： | 1 |
| 14 | An SVM-Based Prediction Method for Solving SAT Problems显示文摘We show how Support vector machines(SVM) can be applied to the Satisfiability(SAT) problem and how their prediction results can be naturally applied to both incomplete and complete SAT solvers. SVM is used for the classification of the variables in the SAT problem and the classification results are the assignment of the variables. And we also present empirical results of applying SVM to instances of the SAT problem from the Center for Discrete Mathematics and Theoretical Computer Science(DIMACS) archive and compare them against the results of other incomplete and complete algorithms for the SAT problem. | HUANG Shaobin LI Ya LI Yanmei | 2019 | Chinese Journal of Electronics2019，28，2： | 1 |
| 15 | Assessment of Noise Exposure of Sawmill Workers in Southwest,Nigeria显示文摘Economic wood processing employs the use of industrial machines for cutting,shaping,milling,and sawing timber,thereby leading to the generation of high levels of noise.Published data from empirical studies have categorized noise as an environmental hazard of global significance.Furthermore,noise exposure limits for different industries and all the industrial machines available has not been formally established as it presently exists in developed nations around the world.Therefore,this study assessed the daily exposure of sawmills workers to noise in Southwestern Nigeria.Reconnaissance surveys were first carried out in Osun,Oyo,Ondo,Ekiti,Lagos,and Ogun States to select sawmills that were fully operational and fit for the study.Two fully functional sawmills in two cities of each State were eventually selected for data collection,making a total of 24 sawmills,while the Circular Machines(CM),Planer Machines(PM),and Band-saw Machines(BM)were the machines in each sawmill considered.Two machines each of CM,PM,and BM were considered in each sawmill,making a total of forty-eight(48)machines each of CM,PM,and BM.Sound data were collected between 7 am and 7 pm each day for six days(between Monday and Saturday)using Extech 407732 sound level meter and all stabilized measurements were taken three times at different intervals.The data collected were in three different periods:Machine No-work Period(NPm),Machine Idle Period(IPm),and Machine Working Period(WPm).A two–way Analysis of Variance(ANOVA)was carried out at P<0.05 to determine whether there is a significant difference in the sound level average before and after the break,for both the idle and working periods of the three machines considered.This was also done to determine whether there is a signifi-cant difference between the sound level average of the results collected during idle and working periods of the three machines.Noise Pollution Levels(Lnp)ranged from 83.20 dB(PM)to 107.65(BM)and 93.42(CM and PM)–116.00(BM)respectively,while IPm also gave the least noise pollution level of 95.79 dB and WPm gave the highest level of 102.88 dB.The results revealed that all the machines’Lnp values in the working period are more than the 90 dB acceptable limit the recommendation value of 90 dB while 89.6%of CMs,75%of PMs,and 89.6%of BM had their Lnp above 90 dB in the idle period respectively.The minimum and the maximum noise dose levels for IPm,WPm and overall are 0.09(BM)and 2.37(CM),0.50(CM),and 4.77(PM)and 0.69(BM)and 6.64(PM)respectively.The study found out that the fundamental contributing factors to the high noise levels in sawmills are poor machine maintenance,use of old and obsolete machines,poor housekeeping strategy,limited space,workers’negligence,lack of PPE,and lack of occupational safety training.The study recommends that proper workplace practices such as use of personal protective equipment,new and modern machines,training,and occupational safety programmes be implemented in the considered sawmills. | Abiola O.Ajayeoba Adewoye A.Olanipekun Wasiu A.Raheem Oluwaseun O.Ojo Ayowumi R.Soji-Adekunle | 2021 | Sound & Vibration2021，55，1： | 1 |
| 16 | 基于鲁棒LS-SVM的控制图模式识别显示文摘提出一种基于鲁棒最小二乘支持向量机(LS-SVM)的控制图模式识别方法,并研究其应用于过程质量诊断的可行性、有效性.理论研究和仿真试验结果表明,该方法对于标准的6种控制图模式都具有很高的模式识别率,训练模式识别器所需样本少,且训练结果泛化能力强,计算方法简单迅速.
Abstract:
A technique based on the robust least squares support vector machines(LS-SVM) used for control charts pattern recognition is proposed, the applied feasibility and validity of this technique in process quality diagnosis is also investigated. Theoretical research and experimental results show that this approach performs well upon the six typical control charts pattern recognition with high recognition accuracy, simple computation and fast training process, and the preeminent generalization ability on the condition of small sample size. | 程志强 马义中 Zhi-qiang Yi-zhong | 2009 | 计量学报2009，，6： | 1 |
| 17 | Behavior and functional modeling methods of doubly salient electromagnetic generators for aircraft electrical power system applications显示文摘The Doubly Salient Electromagnetic Generator(DSEG) is a promising candidate in aircraft generator application due to the simplicity, robustness and reliability. However, the field windings and the armature windings are strongly coupled, which makes the inductance characteristics non-linear and too complex to model. The complex model with low precision also leads to difficulties in modeling and analysis of the entire aircraft Electrical Power System(EPS). A behavior level modeling method based on modified inductance Support Vector Machine(SVM) is proposed. The Finite Element Analysis(FEA) inductance data are modified based on the experiment results to improve the precision. A functional level modeling method based on input–output characteristics SVM is also proposed. The two modeling methods are applied to a 9 kW DSEG prototype. The steady state and transient process precision of the proposed methods are proved by comparing with the experiment results. Meanwhile, the modeling time consumption, the application time consumption and the calculation resource demand are compared. The DSEG behavior and functional modeling methods provide precious results with high efficiency, which accelerates theoretical analysis and expands the application foreground of the DSEG in the aircraft EPS. | Yanwu XU Zhuoran ZHANG Li YU Yuke SHI | 2019 | Chinese Journal of Aeronautics2019，32，2： | 1 |
| 18 | Comparison Of Different DC Motor Modeling Techniques显示文摘A DC motor is the most widely used actuator in the industry,especially for robotic applications such as position control of robot manipulators.When motor is used in high performance close loop motion control systems,an accurate model of motor is required for control system design[1,2].Mathematical description of DC motor is divided into two subsystems:Electrical and mechanical subsystems.Electrical subsystem of a DC motor is simple.It consist of a resistor,an inductor and a back-EMF source.Nowadays,digital RLC meters can be found in nearly all labs.This paper study the reliability of model obtained by using a digital RLC meter.Results shows that although RLC meters can measure impedances easily and quickly,obtained values are not precious because RLC meter’s output current is in the range of mA while motor works with several Ampers. | Farzin Asadi Kei Eguchi | 2018 | Journal of Electronic Research and Application2018，2，2： | 0 |
| 19 | A method to perform prognostics in electro-hydraulic machines: the case of an independent metering controlled hydraulic crane显示文摘Remaining useful life (RUL) estimation is a topic that has gained more and more attention in the field of fluid power systems, thanks to the tremendous potentials shown for improving safety and reduced production losses. However, many challenges related to RUL estimation are not solved yet, mainly connected to the definition of the health status of each component. A prognostic method based on a data-driven methodology for hydraulic systems is here proposed to estimate the percentage of life already spent by the monitored components. The potentials of the methodology are shown considering the case of a truck-mounted hydraulic crane, for which a simulation model was available. | Yuri Ghini Andrea Vacca | 2018 | International Journal of Hydromechatronics2018，1，2： | 0 |
| 20 | Brief introduction to Chinese electrical machine industry显示文摘The electrical machine manufacturing inChina began early in the tenth of twentiethcentury,but the electrical machine industry hasbeen developed quickly ever since the establish-ment of the new China.The output of electricalmachines in 1949 was only around 70 MW,while the output in 1987 was 40.000 MW,inwhich AC electrical motors amounted to 37,000MW.The varieties of products also developedquickly.From the manufacturing aspect,it maybe classified into five categories,i.e.large elec-trical machines,medium electrical machines,small electrical machines,fractional-horsepowermotors and micro motors for control use.Nowthey are stated separately as follows. | Qin He Shanghai Electrical Apparatus Research Institute | 1990 | Electricity1990，，2： | 0 |
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