Theory of the reverse branch of the static characteristic of a germanium diode in the prebreakdown region

1967 ◽  
Vol 10 (9) ◽  
pp. 71-75
Author(s):  
I. M. Senderikhin
Keyword(s):  
Static Characteristic ◽  
Germanium Diode ◽  
Reverse Branch

scholarly journals Double Closed-Loop Compound Control Strategy for Magnetic Liquid Double Suspension Bearing

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1195
Author(s):  
Jianhua Zhao ◽  
Yongqiang Wang ◽  
Xuchao Ma ◽  
Sheng Li ◽  
Dianrong Gao ◽  
...  
Keyword(s):  
Control Strategy ◽  
Electromagnetic Force ◽  
Closed Loop ◽  
Static Characteristic ◽  
Magnetic Liquid ◽  
Initial Current ◽  
Electromagnetic System ◽  
Initial Flow ◽  
Hydrostatic Force ◽  
Position Feedback

As a new type of suspension bearing, the magnetic liquid double suspension bearing (MLDSB) is mainly supported by electromagnetic suspension and supplemented by hydrostatic support. At present, the MLDSB adopts the regulation strategy of “electromagnetic-position feedback closed-loop, hydrostatic constant-flow supply” (referred to as CFC mode). In the equilibrium position, the external load is carried by the electromagnetic system, and the hydrostatic system produces no supporting force. Thus, the carrying capacity and supporting stiffness of the MLDSB can be reduced. To solve this problem, the double closed-loop control strategy of “electromagnetic system-force feedback inner loop and hydrostatic-position feedback outer loop” (referred to as DCL mode) was proposed to improve the bearing performance and operation stability of the MLDSB. First, the mathematical models of CFC mode and DCL mode of the single DOF supporting system were established. Second, the real-time variation laws of rotor displacement, flow/hydrostatic force, and regulating current/electromagnetic force in the two control modes were plotted, compared, and analyzed. Finally, the influence law of initial current, flow, and controller parameters on the dynamic and static characteristic index were analyzed in detail. The results show that compared with that in CFC mode, the displacement in DCL mode is smaller, and the adjustment time is shorter. The hydrostatic force is equal to the electromagnetic force in DCL mode when the rotor returns to the balance position. Moreover, the system in DCL mode has better robustness, and the initial flow has a more obvious influence on the dynamic and static characteristic indexes. This study provides a theoretical basis for stable suspension control and the safe and reliable operation of the MLDSB.

The external demagnetizing factor and the static characteristic loop

2012 ◽  
Vol 407 (13) ◽  
pp. 2434-2437 ◽  
Author(s):  
J. Takács ◽  
Gy. Kovács ◽  
L.K. Varga
Keyword(s):  
Static Characteristic ◽  
Demagnetizing Factor

An encapsulated lithium drifted germanium diode for gamma ray spectrometry

1966 ◽  
Vol 45 (2) ◽  
pp. 322-324 ◽  
Author(s):  
P.E. Gibbons ◽  
J.H. Howes ◽  
S. Pyrah
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Spectrometry ◽  
Germanium Diode

A New Stress Sensor Based on Amorphous Alloy for Dynamic Weigh Rail

2011 ◽  
Vol 204-210 ◽  
pp. 608-611
Author(s):  
Yan Ping Shi
Keyword(s):  
Amorphous Alloy ◽  
Testing Machine ◽  
Material Testing ◽  
Static Characteristic ◽  
Stress Sensor ◽  
Actual Experiment ◽  
High Measurement ◽  
Material Testing Machine ◽  
Output Equation

In order to solve some problem of stress sensor in dynamic weigh rail, a new kind of inductive stress sensor based on magnetostriction effect of amorphous alloy was developed. First, the principle of sensor, and inferred its output equation were discussed. Second, the static characteristic experiment and the actual experiment on the material testing machine and the mine haulage rail were separately made. The results of the test have showed that this stress sensor has some characteristics as high measurement sensitiveness and accuracy. Also being simple, convenient and solid to install and use, so it is feasible to apply the sensor for the dynamic weigh rail.

Denoising of river surface photogrammetric DEMs using deep learning

2021 ◽  
Author(s):  
Radosław Szostak ◽  
Przemysław Wachniew ◽  
Mirosław Zimnoch ◽  
Paweł Ćwiąkała ◽  
Edyta Puniach ◽  
...  
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Water Level ◽  
Water Surface ◽  
Research University ◽  
Static Characteristic ◽  
Water Levels ◽  
Training Dataset ◽  
Observation Data ◽  
Characteristic Points

<p>Unmanned Aerial Vehicles (UAVs) can be an excellent tool for environmental measurements due to their ability to reach inaccessible places and fast data acquisition over large areas. In particular drones may have a potential application in hydrology, as they can be used to create photogrammetric digital elevation models (DEM) of the terrain allowing to obtain high resolution spatial distribution of water level in the river to be fed into hydrological models. Nevertheless, photogrammetric algorithms generate distortions on the DEM at the water bodies. This is due to light penetration below the water surface and the lack of static characteristic points on water surface that can be distinguished by the photogrammetric algorithm. The correction of these disturbances could be achieved by applying deep learning methods. For this purpose, it is necessary to build a training dataset containing DEMs before and after water surfaces denoising. A method has been developed to prepare such a dataset. It is divided into several stages. In the first step a photogrammetric surveys and geodetic water level measurements are performed. The second one includes generation of DEMs and orthomosaics using photogrammetric software. Finally in the last one the interpolation of the measured water levels is done to obtain a plane of the water surface and apply it to the DEMs to correct the distortion. The resulting dataset was used to train deep learning model based on convolutional neural networks. The proposed method has been validated on observation data representing part of Kocinka river catchment located in the central Poland.</p><p>This research has been partly supported by the Ministry of Science and Higher Education Project “Initiative for Excellence – Research University” and Ministry of Science and Higher Education subsidy, project no. 16.16.220.842-B02 / 16.16.150.545.</p>

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