Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

泌阳凹陷复杂断块群油气分布控制因素

泌阳凹陷北部斜坡复杂断块群油田以断鼻、断块及断层+岩性油气藏为主,垂向含油层系多,平面上叠合连片,油气主要富集在鼻状构造主体部位的断鼻断块内。通过对研究区油气控制因素的分析,指出继承性的鼻状构造控制了油气聚集方向和富集程度,油气藏含油范围和油气藏高度则主要受断层封闭性和封闭程度的控制,油气分布规律受伸展构造所伴生的各种断块群构造型式的控制。     

A Study on Turbo—rotor Multi—fault Diagnosis Based on a Neural Network

The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine.As it has many excellent qualities,the neural network might be used to solve the problems of multi-fault diagnasis of a turbo-rotor system.First,the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing.Then.the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propaga-tion)neural network.According to the BP neural network problems,the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated.The results indicated that the parallel Bp neural network method could solve the turbo-rotor multi-fault diagnosis problems.     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

Breakdown characteristics of oil and paper insulation for a very fast transient voltage

This paper describes the dielectric breakdown characteristics of oil and oil‐impregnated paper for very fast transient (VFT) voltages. Blumlein circuits generate VFT voltages of 60 and 300 ns in a pulse width that simulates disconnecting switching surges in gas‐insulated switch gears. We measured the breakdown voltages of needle‐to‐plane, plane‐to‐plane oil gaps and several pieces of paper between plane electrodes for VFT and lightning impulse voltages. The measured data were formulated in V‐t characteristics and Weibull probability distributions. The inclination n of V‐t characteristics of insulating paper is 150, which is less than n = 13.7 of the plane‐to‐plane oil gap in the VFT time range. The shape parameters of Weibull distribution obtained in this study show that the scattering of breakdown voltages of paper is much less than that of oil. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 141(4): 16–24, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10043     

千米桥潜山构造特征及其在油气成藏中的作用

千米桥潜山是黄骅坳陷最典型的新生古储型油气藏，恢复潜山古构造是认识其油气藏特征的重点和难点之一，运用地质力学方法研究潜山内幕构造之后认为，该潜山的形成基础是古生界印支期宽缓复背斜，在燕山期挤压构造作用下形成由逆冲叠瓦扇和反冲断层构成的三角带内幕构造，抬升受侵蚀形成潜山主体，喜马拉雅的两次古旋拉张使潜山掀斜定型。晚第三纪以来发生整体热沉降，潜山两侧凹陷沙三段生成油气向潜山侧向运移成藏。     

Operation of superconducting fault current limiter using vacuum interrupter driven by electromagnetic repulsion force for commutating switch

Using conventional high‐temperature superconducting wire, a model superconducting fault current limiter (SFCL) is made and tested. Solenoid coil using Bi2223 silver sheath wire is so made that inductance is as small as possible and a vacuum interrupter is connected in series to it. A conventional reactor coil is connected in parallel. When the fault current flows in this equipment, superconducting wire is quenched and current is transferred into the parallel coil because of voltage drop of superconducting wire. This large current in parallel coil actuates magnetic repulsion mechanism of vacuum interrupter. Due to opening of vacuum interrupter, the current in superconducting wire is broken. By using this equipment, current flow time in superconducting wire can be easily minimized. On the other hand, the fault current is also easily limited by large reactance of parallel coil. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 164(1): 52–61, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20315     

A novel distributed approach to robust fault detection and identification

In this paper, we propose a novel distributed robust fault detection and identification (RFDI) scheme for a class of nonlinear systems. Firstly, a detection and identification estimator—robust fault tracking approximator (RFTA) is designed for online health monitoring. A novel feature of the RFTA is that it can simultaneously detect and accurately identify the shape and magnitude of the fault and disturbance. Moreover, it takes less online training time compared with the traditional neural network based fault diagnosis schemes. For some distributed systems, a network of distributed estimators is constructed where the RFTA is embedded into each estimator. Then we use consensus filter to filter the outputs of each estimator. One of the most important merits of the consensus filter is that its outputs can dramatically improve the accuracy of fault detection and identification. Next, the stability of the distributed RFDI scheme is rigorously investigated. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

基于聚类算法实现信号盲分类

本文解决了信号处理、工业控制等领域存在的非平稳信号盲分类问题。在聚类中广泛应用的K-Means算法及其它基于中心的聚类算法有两个共同的缺陷-需要预先确定类数目且随机初始化中心引起性能不稳定。本文提出的算法较好地解决了这两个问题，提高了算法稳定性，实现了非平稳信号盲分类。提取非平稳信号的小波系数作为聚类的样本空间，分析聚类结果的统计偏差以估计类的数目，采用调和均值准则进行分类。最后给出的仿真结果表明本文提出的方法较传统的K-Means算法明显降低分类错误率。