Remote Steering of Self‐Propelling Microcircuits by Modulated Electric Field

The principles and design of “active” self‐propelling particles that can convert energy, move directionally on their own, and perform a certain function is an emerging multidisciplinary research field, with high potential for future technologies. A simple and effective technique is presented for on‐demand steering of self‐propelling microdiodes that move electroosmotically on water surface, while supplied with energy by an external alternating (AC) field. It is demonstrated how one can control remotely the direction of diode locomotion by electronically modifying the applied AC signal. The swimming diodes change their direction of motion when a wave asymmetry (equivalent to a DC offset) is introduced into the signal. The data analysis shows that the ability to control and reverse the direction of motion is a result of the electrostatic torque between the asymmetrically polarized diodes and the ionic charges redistributed in the vessel. This novel principle of electrical signal‐coded steering of active functional devices, such as diodes and microcircuits, can find applications in motile sensors, MEMs, and microrobotics.     

交流伺服系统在玻璃裂片机中的应用

随着运动控制技术的飞速发展，交流伺服系统应用的日益广泛。介绍了液晶生产中的关键设备玻璃裂片机，并详细分析松下MINAS A系列交流伺服系统在玻璃裂片机中的应用。     

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.     

新型交流脉冲MIG焊接法的研究

采用自行研制的具有方框形外特性的交流脉冲MIG焊接电源 ,又设计了双凹形焊接电流波形 ,即过零前后的电流波形均为脉冲大电流 ,有利于交流脉冲MIG焊接电弧的连续稳定。按照这个新型交流脉冲MIG焊接方法 ,不必加任何稳弧措施 ,可实现电弧的稳定燃烧 ,解决了电弧磁偏吹的问题 ,具有较好的焊接工艺性能 ,用于压力容器的全位置焊接和铝焊接均有很好的焊缝成型     

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.